Artificial Intelligence and Robotics in Cardiology and the Cath Lab By Alfred Raso A thesis submitted to the School of Radiologic Sciences in collaboration with a research agenda team In partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN RADIOLOGIC SCIENCES (MSRS) WEBER STATE UNIVERSITY Ogden, Utah April 25, 2025 ii THE WEBER STATE UNIVERSITY GRADUATE SCHOOL SUPERVISORY COMMITTEE APPROVAL of a thesis submitted by Alfred Raso This thesis has been read by each member of the following supervisory committee and by majority vote found to be satisfactory. ______________________________ Dr. Tanya Nolan, EdD Chair, School of Radiologic Sciences ______________________________ Christopher Steelman, MS Director of MSRS Cardiac Specialist ______________________________ Dr. Laurie Coburn, EdD Director of MSRS RA ______________________________ Dr. Robert Walker, PhD Director of MSRS iii THE WEBER STATE UNIVERSITY GRADUATE SCHOOL RESEARCH AGENDA STUDENT APPROVAL of a thesis submitted by Alfred Raso This thesis has been read by each member of the student research agenda committee and by majority vote found to be satisfactory. Date April 25, 2025 ______________________ ____________________________________ Alfred Raso iv Abstract The integration of artificial intelligence (AI) and robotics into interventional cardiology, specifically within cardiac catheterization laboratory (cath lab), has the potential to revolutionize patient care through enhanced precision, improved diagnostic accuracy and optimized workflow efficiency. This systematic review examines the current landscape of AI and robotics in cardiology, with a specific focus on the applications in the cath lab. It helps to address the benefits, challenges to implementation and the evolving responsibilities of allied health professionals. Key areas of focus include robotic assisted percutaneous interventions (R-PCI), AI driven imaging tools and training frameworks to support the adoption of these technologies. The review synthesizes findings from selected studies that met inclusion criteria for reference to AI imaging, robotic intervention systems and educational training programs. Results show that AI algorithms enhance diagnostic accuracy, while robotics systems improve procedural precision and reduce radiation exposure to operators. Considering the identified gaps and rapidly evolving technology, the review emphasizes the need for structured educational initiatives. Suggestions have been made to enhance continued education opportunities and create formal academic courses tailored to allied health professionals. In response the author proposes the development of a dedicated academic course “AI and Robotics in the Cath Lab” a to prepare allied health professionals for the future of interventional cardiology. Continued research, collaboration and educational innovation will be essential to ensure equitable and effective integration of AI and robotics into cath lab practice on a global scale. v Table of Contents Chapter 1: Introduction ....................................................................................................... 1 Background ................................................................................................................... 2 Statement of the Problem .............................................................................................. 4 Purpose of the Study ..................................................................................................... 5 Research Questions ....................................................................................................... 5 Nature of the Study ....................................................................................................... 5 Significance of the Study .............................................................................................. 6 Definition of Key Terms ............................................................................................... 6 Summary ....................................................................................................................... 7 Chapter 2: Literature Review .............................................................................................. 8 Introduction ................................................................................................................... 8 Artificial Intelligence and Deep Learning in Cardiology ............................................. 8 Robotic-Assisted Cardiac Interventions ....................................................................... 9 AI and Robotics in Surgery: What Cardiology can Learn .......................................... 11 Imaging Innovations and AI Integration ..................................................................... 12 Training and Simulation for Robotic-Assisted Procedures ........................................ 13 Telerobotic Interventions and the Future of Remote Cardiac Procedures .................. 13 Challenges and Future Directions ............................................................................... 14 Physician Perceptions of AI in Interventional Cardiology ......................................... 15 Documentation ............................................................................................................ 16 Summary ..................................................................................................................... 16 Chapter 3: Research Method............................................................................................. 19 Research Methods and Design(s)................................................................................ 20 Use of PRISMA for transparency, completeness and reproducibility ........................ 21 Systematic Review process and steps ......................................................................... 22 Justification for research design.................................................................................. 22 Materials/Instruments ................................................................................................. 23 Assumptions................................................................................................................ 23 Limitations .................................................................................................................. 24 Delimitations ............................................................................................................... 24 Ethical Assurances ...................................................................................................... 25 Summary ..................................................................................................................... 25 Chapter 4: Findings ........................................................................................................... 27 Results ......................................................................................................................... 27 Evaluation of Findings ................................................................................................ 28 Summary ..................................................................................................................... 29 Chapter 5: Implications, Recommendations, and Conclusions ........................................ 31 Implications................................................................................................................. 31 Recommendations ....................................................................................................... 32 vi Conclusions ................................................................................................................. 33 References ................................................................................................................... 35 Appendix A: College Course on integration of AI in cardiology and the Cath Lab ........ 37 1 Chapter 1: Introduction Throughout recent years, the incorporation of artificial intelligence (AI) and robotics into the cath lab has played a big role in the future of cardiovascular medicine. While AI and robotics have long existed in surgical and diagnostic applications, their integration into interventional cardiology is accelerating due to growing demands for minimally invasive, precise and safer procedures. AI and robotic technologies in cardiology can help to enhance precision, improve patient outcomes, increase access to the areas that need treatment, and to have a better overall workflow on procedures. Robotics are common in other parts of the medical world, such as surgery. Their application has been expanding throughout various realms in cardiology and cath labs over the last few decades. Robotic systems, initially popularized in cardiac surgery in the 1990s, have evolved significantly by now offering substantial benefits in percutaneous coronary intervention (PCI).1 Robotic- assisted PCI improves procedural control and operator safety while potentially enhancing patient outcomes. Artificial intelligence, particularly deep learning (DL), has made big advancements in cardiology in recent years. DL is a subset of machine learning (ML) and these use deep artificial neural networks (DNNs) with multi layered neurons in order to process complex datasets. According to Ohashi et al., AI has been helpful in order to automate the interpretation of medical images with things such as coronary computed tomography (CTA) and echocardiograms. With the addition of AI algorithms support of interpreting coronary imaging in the cath lab will be able to further help with diagnosing diseases such as coronary artery disease. There is potential for these types of algorithms to enable image based coronary interventions in the near future according to Beyar et al.1,5 Despite the benefits of these emerging technologies, challenges remain in achieving their widespread adoption. One of these main challenges is high implementation costs, which would include the initial purchase of the equipment, maintenance, and required training for staff. These challenges present significant hurdles for many healthcare institutions trying to begin 2 implementing the exciting but expensive new technology. Additionally, integrating AI and robotic systems into existing workflows can require substantial adjustments, which can lead to resistance among healthcare teams. These challenges highlight the need for further innovation and policy development to make these technologies more accessible for widespread use. My research emphasizes the roles of AI and robotics in improving both accessibility and safety for patients and staff within cath labs and cardiology departments globally. AI has the capability to optimize procedural planning, predict complications, and recommend approaches for individual patients. At the same time, robotic systems enable precise interventions while reducing radiation exposure for operators, which is important to consider for long-term safety. These advancements underline how technology can not only enhance clinical outcomes but also create safer working environments for healthcare professionals. This thesis aims to explore and compare the current state of artificial intelligence and robotics in cardiology. Main focuses will include AI’s safety, reliability, and growing prevalence in cath labs. By examining recent advancements, an understanding is built of how these technologies are reshaping cardiovascular care, as well as discussing the challenges that must be addressed to maximize their potential. While the future of AI and robotics in cardiology is promising, overcoming barriers such as cost, training, and integration is essential for realizing their full benefits. Background In response to the growing demand in cardiology for minimally invasive procedures, the integration of artificial intelligence (AI) and robotics in cardiology and cath labs has significantly advanced in cardiovascular medicine. Specific examples of AI include deep learning (DL), which has revolutionized diagnostics, decision support, and imaging. DL accomplishes this by using deep neural networks to process complex datasets, this helps in the interpretation of coronary CT angiography (CTA) and echocardiograms. The goal of this procedure is to enhance diagnostic accuracy which also speeds up clinical decision-making according to Ohashi et al5. 3 Robotic systems, specifically with the CorPath GRX, have contributed in developing percutaneous coronary interventions (R-PCI) by increasing procedural precision, reducing operator fatigue, and lowering radiation exposure for healthcare providers.4 These technological advancements build upon foundational work established by robotic assisted cardiac surgeries utilizing systems such as the da Vinci platform, which paves the way for contemporary R-PCI technologies. The CorPath 200 and its successor, the CorpAth GRX, enabled remote-controlled, joystick-guided manipulation of guidewires and devices, minimizing radiation risks and operator fatigue.22 AI has also optimized imaging technologies, such as C-arm Cone-Beam CT (CBCT), which provide real-time, high-resolution 3D imaging during interventions according to Yang F et al.12 These innovations enhance procedural accuracy and reduce positioning errors, benefiting each patient and healthcare team. With simulation-based training programs, incorporating AIdriven algorithms enable healthcare providers to practice robotic-assisted procedures in realistic and controlled environments. By having a controlled environment and more practice, it helps to ensure a smoother transition to advanced technologies during the actual procedures. Newly innovative AI based platforms like the TAVIPILOT AI drive AiiR have been able to integrate a multimodal medical image analysis, digital twin technology and autonomous robotic control helping procedure precision in various heart valve positioning. This will be helpful because around 1.7 million people just across the EU and United States are in need of TAVI procedures, while around 300,000 TAVI procedures are done annually. 15 Despite their potential and proven benefits, the widespread adoption of AI and robotics still faces significant challenges. These challenges range from high costs of equipment, maintenance, and training pose barriers.2 The hardest part of AI and robotics in the cath lab is getting the equipment into the lab initially. The initial cost of the equipment on top of the training required to operate the robots can cost the labs a significant amount of money.10 4 Whenever new equipment is introduced into medical procedures there are substantial adjustments that need to be mastered and incorporated into the lab and their workflows.9 Working through these adjustments can delay patient care, as well as delay operations for the medical team. As technology evolves policies change constantly and continuing education is essential. Also, stakeholder collaborations are needed in order to address challenges and hopefully expand access to more rural areas. AI and robotics are reshaping cardiology by improving precision, safety, and efficiency in cardiovascular interventions. While challenges remain, these technologies are created for the purpose of being efficient and accessible, creating better outcomes for patients and medical staff. While AI in the cath lab continued to grow, the need for continued research continues to grow as well along with training to maximize their impact on global cardiovascular health. Statement of the Problem As artificial intelligence (AI) and robotics rapidly reshapes the landscape of cardiovascular care, it is imperative that cardiac catheterization curriculums are able to evolve accordingly. With the incorporation of a dedicated course on AI and Robotics in cardiology this will help to ensure enhancing patient care with increased procedural precision, safety, and workflow efficiency. Although there are proven benefits with the incorporation of this technology, significant barriers have affected their widespread adoption. Not only does the incorporation of AI and robotics have high initial costs, but they also require extensive training. With the high cost and extensive training that can create some resistance to change among healthcare professionals and limitations for cath labs in order to implement the new technology. Additionally, the role of cath lab technologists in this role of AI and robotics in cardiology and cath labs seems to be very underexplored. This has left a gap in understanding how best to integrate these technologies within current workflows, specifically for cath lab technologists, in order to optimize their potential in this constantly changing setting. This thesis aims to explore these challenges by examining the current state of AI and robotics in cardiology by identifying the barriers to their 5 adoption. Thus, by ensuring they are equipped with the skills to thrive and support advancements in interventional cardiology. Purpose of the Study My study is both a descriptive and exploratory research design that focuses on the current state of AI and robotics in both cardiology, specifically within the cath lab. Throughout the essay I have included applications, benefits, and challenges regarding the use and integration of new equipment entering cardiology and the cath lab. I discuss important barriers, such as the cost and resistance of implementing this technology, as well as showing the evolving role of cath lab technologists in these new adapting settings. Research Questions Q1. How do robotic-assisted percutaneous coronary interventions (R-PCI) act in comparison to manual percutaneous interventions procedures in terms of precision, safety, and outcomes? Q2. What systematic barriers hinder R-PCI/ AI adoption in cath labs and how can tailored training for technologists address these gaps Q3. How might AI and robotics reshape the role of cath lab technologists in procedural workflows Nature of the Study My study is both a descriptive and exploratory research design that focuses on the current state of AI and robotics in both cardiology, specifically within the cath lab. Throughout the essay I have included applications, benefits, and challenges regarding the use and integration of new equipment entering cardiology and the cath lab. I discuss important barriers, such as the cost and resistance of implementing this technology, as well as showing the evolving role of cath lab technologists in these new adapting settings. 6 Significance of the Study My research highlights the potential of AI and robotics in cardiology, while also identifying challenges that arise in the adoption and accessibility of this equipment. By addressing these challenges, my research will contribute to the understanding of the great equipment and how it will affect the healthcare staff, policy makers and technology developers. One of the main focuses will be on the role of the cath lab techs and other allied healthcare staff for the risk of being underprepared for what is to come with the emerging technology. The cath lab team will play a significant role on how the setup of the equipment works, as well as mastering the new technology with constant trailing to help patients have the best and safest outcomes. With the adoption of AI and robotics, which are still growing and changing constantly, existing research focuses strongly on clinicians’ data for the development of this equipment. This can present a challenge where this type of equipment among cath labs is still currently underexplored. This thesis seeks to help in filling in that information gap and to emphasize the critical involvement that healthcare workers and cath lab techs will have in this technological evolution. Definition of Key Terms AI: Artificial Intelligence (AI) in Cardiology R- PCI: This term refers to Robotic Percutaneous Coronary intervention M-PCI: This term refers to Manual Percutaneous Coronary intervention (DL) and machine learning (ML) used in diagnostics, decision-making, and procedural planning. Deep Learning (DL): This is a subset of AI that utilizes neural networks to help in processing complex datasets and is specifically used in coronary CT angiography and echocardiograms. Robotics in Cardiology: Some of the various robotics found, included systems like the CorPath GRX, which help in enabling precision in percutaneous coronary interventions (PCI) and reducing operator fatigue and radiation exposure. 7 Minimally Invasive Procedures: These are examples of techniques which require small incisions. C-arm Cone-Beam CT (CBCT): This is a type of AI-driven imaging technology which helps to see real-time, high-resolution 3D imaging.. Barriers to Adoption: This references the challenges for implementing into the cath lab and cardiology such as high cost, resistance to change, training demands, and workflow adjustments. Cardiovascular Health Equity: This addresses accessibility to AI and robotics globally. Summary This is a reflection that explores the transformative potential of artificial intelligence and robotics in cardiology, specifically there is a focus of how this new technology will integrate into cath labs and for allied healthcare staff. A descriptive and exploratory research method was used in order to address three key research questions: How do robotic-assisted percutaneous coronary interventions (R-PCI) act in comparison to manual percutaneous interventions procedures in terms of precision, safety, and outcomes? What systematic barriers hinder RPCI/ AI adoption in cath labs and how can tailored training for technologists address these gaps? How might AI and robotics reshape the role of cath lab technologists in procedural workflows? The study examines the benefits and challenges of what implementing AI and robotics has done so far. For example, it has improved procedural precision, patient outcomes and workflow efficiency while also bringing up the high costs and training demands along with this new technology. There is a particular emphasis on overlooked contributions that will need to occur for allied health professionals and exploring how to expand accessibility to new and emerging technology. This research fills a critical up and coming knowledge gap for actionable insights for healthcare professionals and policymakers to create more and better access to minimally invasive procedures for all patients for better outcomes. 8 Chapter 2: Literature Review Introduction In recent years, the incorporation of artificial intelligence (AI) and robotics into the cardiac catheterization (cath) lab has significantly influenced the future of cardiovascular medicine. While AI and Robotics are not new to the field of cardiology but as the demand for minimally invasive procedures grows, there has been more of a push in cardiology for advancing technologies. AI and robotic technologies in cardiology can have the potential to enhance procedural precision, improve patient outcomes, increase access to underserved areas and to streamline workflow within cath labs. Although robotics are well established in other medical specialties, such as surgery, its application within cardiology and cath labs has expanded steadily over the past few decades. My research has helped reveal an emphasis on how AI can both improve accessibility and safety for both patients and staff within cath labs and cardiology departments worldwide. While these advancements have brought benefits, challenges remain in the widespread adoption of them. This literature review explores and compares the current states of artificial intelligence and robotics in cardiology with a focus on safety, reliability and increasing integration in cath lab environments. Artificial Intelligence and Deep Learning in Cardiology Artificial intelligence, particularly deep learning (DL), has made significant advancements in cardiology over the last few years. Some main areas of advancement are found in diagnostics, decision support, and image analysis. DL is a subset of machine learning (ML) that utilizes deep artificial neural networks (DNNs) with multiple layers of neurons to process complex datasets. A major area of exploration is the use of AI to automate the interpretation of medical images, including coronary computed tomography angiography (CTA) and echocardiograms.5 Advancement in cardiology imaging can assist in faster clinical decision- 9 making, helping solve patient issues in a faster manner. Beyar et al.1 also goes into depth about how AI algorithms and computational based interpretation of coronary imaging can also be helpful in the cath lab. Particularly when trained on large datasets, these forms of AI can accurately diagnose conditions such as coronary artery disease (CAD) and possibly in the future image based coronary interventions. Robotic-Assisted Cardiac Interventions Robotic systems are also transforming cardiovascular procedures. The largest comparative study to date regarding R-PCI by Patel et al. included 996 patients undergoing PCI, with 310 receiving Robotic PCI and 686 receiving manual PCI at the Apex Heart Institute in India.19One of the most recent developments of robotics in the cath lab is robotic-assisted percutaneous coronary intervention (R-PCI). In the article by Kotaro, et al.,4 The CorPath GRX system is described as a piece of equipment that has gained popularity for its ability to successfully perform more complex coronary procedures with precision and reduced radiation exposure, as opposed to traditional manual PCI (M-PCI). Several studies have compared the clinical outcomes of R-PCI with M-PCI, emphasizing that the clinical success rates for both methods are close. A key to R-PCI also mentioned by Kotaro et al. and A. Khokhar et al. is how robotic systems can significantly reduce radiation exposure to operators. 4,10 By finding a way to reduce frequent exposure to ionizing radiation, high energy radiation was a key concern and with the introduction of these robotics it can now be a solution. Research by Liu, Z.-Y., & Zhai, G.-Y. have reported that R-PCI has a safety and technical success rate near 90%, further strengthening switching from M-PCI to R-PCI. Its credibility has been regulatory approvals, including the FDA-approved CorPath200 and CorPath GRX in the US and the R-One system in Europe.914 A newer innovation in telerobotic technology includes R-PCI tele-stenting and telerobotic coronary angiography, which enables remote treatment of complex coronary lesions remotely across seas.20 For example, in China, there are 10 newer developed systems called the ET Cath 200 and ALLIVAS.14 Although these systems have been a great advancement, they will still require long-term studies before R-PCI can become a more standard coronary intervention. A big benefit of being able to do these procedures remotely is that it improves access to interventional care in rural areas without the need for local specialists. However, just as with any other newer equipment introduced to the medical field, challenges with these newer systems remain. From procedural delays, catheter compatibility issues with heavily calcified lesions, and to ensure smooth transitions between R-PCI and M-PCI in unstable patients are just a few examples of the challenges that will be worked on for these systems in the next few years. The reduction in radiation exposure also plays an important role for long-term health outcomes as repeated exposure to X-rays can lead to increased risks of health issues, such as cancer. Robotic systems, such as the CorPath GRX and R-One, allow the operator to remain outside the radiation field, minimizing their risk while still given the tools to conduct a successful procedure. This not only increases safety for the medical team but minimizes safety risks of the patient during a procedure.4,13 Additionally, robotic-assisted procedures can also help reduce operator fatigue, especially in complex cases that run for long periods of time.13 While robotic systems in the cath lab are continuing to prove they are effective, there are still significant barriers when it comes to getting the equipment into the procedure rooms. The cost of robotic systems remains a major hurdle with high initial purchase prices, maintenance costs, and the need for specialized training. This was mentioned in the review by Koulaouzidis et al.2 “to buy and set up surgical robots, robotic procedures are more expensive than traditional surgeries. It is unclear if the cost of these systems will go up or down in the future. Some people think that the price will decrease as technology advances and as more people get experience using robotic systems. Others think that the cost of these systems will go up as technology advances and more sophisticated software is introduced.” Although the future of these costs remains 11 unclear for now, there is hope that robotic procedures will become more affordable and accessible in the future. AI and Robotics in Surgery: What Cardiology can Learn AI and robotics have a long-term presence in surgery, offering valuable takeaways for cardiology applications. A pioneer in advanced robotic surgeries is the da Vinci Surgical System which has been adopted for minimally invasive procedures anywhere from general surgery to urology. The 1990s marked a period of tremendous advancement in surgical robotics. In 1997, Dr. Friedrich Mohr and his team at the Leipzig Heart Center in Germany performed the first robotic-assisted heart surgery using a prototype called the Aesop 3000. This system allowed for enhanced precision in thoracoscopic procedures and opened the door for more complex cardiac surgeries. In 1998, Dr. Randall Wolf and his team at the Ohio State University Medical Center successfully completed the first robotic-assisted coronary artery bypass grafting (CABG). Just one year later, in 1999, Dr. Randas Batista performed the first robotic mitral valve repair using the da Vinci system. These early milestones culminated in FDA approval of the da Vinci system in 2000 for general laparoscopic surgery, followed by approval for cardiac surgery in 2001 which marked the beginning of its widespread clinical use.21 The biggest difference between incorporating systems like the da Vinci versus R-PCI is the need for human input. In PCI the operator has complete control over the robotic system as opposed to robotic assisted surgery where the robots can perform complex tasks with minimal human interruption. AI powered surgical systems can also track the precise locations of surgical instruments to ensure accurate and safe resections of tumors or placements of implants.17 The advancements found in surgery can offer potential applications in cardiology such as using robotic systems to assist in procedures that by offering help with more precision with stent placement or valve repair. The ability of AI to recognize patterns can be helpful to reduce human 12 error after long cases to improve patient outcomes. The ability to minimize human errors could improve patient outcomes in complex cardiac interventions. Imaging Innovations and AI Integration In addition to robotic-assisted interventions, AI is also playing a big role with advanced imaging technologies by helping to improve procedural efficiency and patient outcomes. An example of this technology is mentioned in the article by Yang F. et al.12, regarding the C-arm Cone-Beam CT (CBCT). A CBCT is a type of technology that provides real-time, high-resolution 3D imaging during interventional procedures. These types of AI-driven systems can automatically not only help detect, but correct positioning errors as well. With the assistance of these machines, precision for these procedures is a lot more achievable. Another major way AI can play a crucial role in cath labs is by helping reduce radiation exposure during imaging-based interventions. By benefiting from machine learning algorithms, imaging systems can optimize Xray exposure levels and minimize unnecessary radiation. This can help improve radiation safety on the patients and the healthcare provider’s end. Additionally, the AI-driven AiiR platform can integrate multi-modal medical image analysis, digital twin technology and autonomous robotic control. All of these will help in playing a big role in procedural precision with use of AI and robotics. AI-trained algorithms use a variety of patient datasets to help predict complications and make personal recommendations based on the specific procedure being performed. The TAVIPILOT will be the first application that will help with precise aortic valve positioning. This will increase support for higher accuracy in cardiovascular interventions, specifically towards navigating potential complications and having robots contribute to understanding of the complex personalized anatomy of patients.15 The ability of AI to assist in real time decision making and robot assisted procedures will help lead to more patient specific interventions, which will not only contribute in improving the safety and clinical outcomes of the patience, but help reduce fatigue for the providers. 13 Training and Simulation for Robotic-Assisted Procedures In an article by A. Khokhar et al.9, there was an emphasis on the need to execute these new technologies. As new robotic systems continue to enter the cath lab, training becomes a necessity to ensure that these systems are used correctly. There are now simulators that can replicate the cath lab environment which allows staff to practice robotic-assisted procedures without live patients. These simulators have been helpful to incorporate AI-driven algorithms and make realistic simulations of coronary interventions. By being able to practice complex cases in repetition before attempting the actual procedure, it gives the providers time to work on and master the skills they need in a more safe, low risk and controlled environment. In many cath labs, members of the healthcare team are usually involved in managing equipment and assisting with procedures, making it extremely important that they feel confident and equipped with the work that they are doing. Simulation-based training programs can help all team members gain familiarity with robotic systems and develop the skills necessary to support robotic-assisted interventions. With the providers being more comfortable with multi-joint collaborative control and remote operation algorithms, the further refine R-PCI’s capabilities are possible for remote surgical procedures.14 By improving the training of the entire team, the transition from manual to robotic-assisted PCI becomes smoother, making the complex procedures safer for everyone involved. Telerobotic Interventions and the Future of Remote Cardiac Procedures To assist with the need of expanding accessible care to underserved populations, recent advancements in telerobotic technology are pushing boundaries of what possible for global interventional cardiology. A preclinical study by Madder er al. 20 explored how transatlantic telerobotic coronary angiography (ICA) uses the Electromagnetic Navigation System to enable remote navigation of a magnetic guidewire and diagnostic catheter using a bedside magnetic field generator, robotic drive, and controller. In the study, the model and robot were located in Zürich, 14 Switzerland, while the operating physician was either present onsite or controlling the system remotely from Grand Rapids, Michigan, USA. Across 260 consecutive attempts, including 220 transatlantic procedures, the study demonstrated 100% technical success for remote operators, with engagement times comparable to onsite robotic use (26.7s vs. 33.2s, P = .003).21 Although manual procedures were still faster, this study confirms the technical feasibility of crosscontinental ICA and opens the door for future global cardiac care delivery models, particularly in underserved or geographically isolated areas. This is another exciting technological advancement which reinforces for allied health professionals, the urgency of preparing technologists for a future that may include assisting or managing remote robotic systems. Challenges and Future Directions Just like any other type of technology, AI and robotics comes with its own set of challenges. Despite the significant benefits that AI and robotics offer in interventional cardiology, there are several challenges presented when aiming for widespread adoption. Najafi et al.10, mentioned how one of challenges was the high cost of robotic systems and AI-integrated imaging technologies. Although robotic systems are projected to become more cost-effective over time, the initial investment remains a significant challenge for many healthcare institutions. Additionally, integrating robotic systems with existing clinical workflows can be quite complicated, requiring many changes to standard operating procedures and retraining staff. This could possibly make cath lab staff more resistant to AI incorporation in the cath lab along with it being difficult for management to implement. Therefore, as AI and robotic technologies rapidly reshape the landscape of cardiovascular care, it’s imperative that cardiac catheterization curricula are able to evolve accordingly. Incorporating a dedicated course on Artificial Intelligence and Robotics in Cardiology can help to ensure that future cath lab professionals are not only aware of these transformative tools but prepared to engage with them critically and effectively in clinical practice. 15 This course could equip graduate level technologists and nurses with the foundational knowledge to understand AI algorithms, the practical skill to interpret AI- augmented images and procedural data and the ethical insight to evaluate these technologies roles in decision-making. As interventional cardiology moves towards greater automation, remote capabilities and data driven precision, students must be prepared to contribute meaningfully to these innovations in clinical cases at the bedside as well as in the design of future workflows. Integrating this content into the cath lab curriculum empowers learners to transition from passive users of technology to informed collaborators in the implementation, promoting safety, evidence based, and patient centered cardiovascular care in this technology driven healthcare environment. Physician Perceptions of AI in Interventional Cardiology As AI is becoming increasingly integrated into interventional cardiology, physician perspectives play a crucial role in its adoption. A recent global survey of 521 interventional cardiologists ranging in age from 36 to 45 with 7.5% being women.16 Most of physicians surveyed were practicing in the US 51% and European Union 15.6%. The study had found that while 63.7% of respondents were optimistic about AI in IC, 73.5% believed that physicians currently lack sufficient knowledge to use AI effectively. Most respondents (46.1%) agreed that specialized training is necessary, and only 22.1% reported that they are currently using AI in their clinical practice. Although, they expect or want to have AI implemented, given that 60.6% anticipated implementation within the next five years. Despite the enthusiasm behind AI being implemented there is concern for blind reliance on AI. However, throughout the survey most participants noted how AI should be used but should be a tool to confirm as opposed to a primary decision for clinical decision making. Therefore, from this information strategies to help prepare the interventional cardiologist workforce for safe and effective AI integration remain uncertain except that AI is on the trajectory to continue to be implemented throughout the next couple of 16 years. As mentioned throughout this research, further investigation is going to be required in order to have conclusive evidence of the adaptation of AI being implemented. 16,18 Documentation For this literature review, I carried out an in-depth search across multiple academic databases and search engines to gather a thorough and diverse selection of sources for my topic. These sources provided me with research regarding Artificial Intelligence (AI) and the development of robotics in the cath lab. I used this approach to ensure I didn’t overlook any critical studies, peer reviewed articles, and to provide a well-rounded view of the topic. I primarily used Google Scholar, along with various references cited in Euro Intervention, Frontiers in Cardiovascular Medicine, and Wiley Online Library. Specific search criteria I used was “AI and Robotics”, “Interventional Cardiology and AI”, “Cath Lab and AI/ Robotics”, “Cath Lab Technologists and Artificial Intelligence” and “Robotics and Artificial Intelligence Costs”. It was particularly helpful using references cited in other articles to expand the search criteria to include references and have access to various professional healthcare workers that work internationally in cath labs, along with other professionals in different sections of cardiology. Summary Artificial intelligence (AI) and robotics have grown increasingly important in cardiology and in cath labs as the demand for minimally invasive procedures grows. Ohashi et al.4, pointed out that deep learning has made a difference in diagnostics, decision support and imaging. Another advancement has been by algorithms analyzing medical imaging like in coronary CT angiography, enhancing diagnostic accuracy, and enabling faster clinical decision making according to another article by Beyar et al.1 Robotic assisted procedures have been involved in the cath lab with robotic percutaneous coronary interventions (R-PCI). These varying PCI procedures have helped in precision, reducing operator fatigue and lowering radiation exposure for healthcare operators. Others systems such as the CorPath GRX, a piece of equipment 17 introduced by Kotaro, et al.4, is a system that has allowed operators to be able to perform more complex interventions to enhance safety and stability. Furthermore, artificial intelligence has optimized imaging technologies. An example of this is the Cone Beam CT (CBCT) in real-time high-resolution 3D imaging. While also helping with radiation exposure, this type of machine has helped with reducing positioning errors to have easier patient movement for patients and staff.12 Training programs have also been able to be introduced for simulation and 3D modeling which is essential for incorporating in the cath lab and various realms of cardiology.9 Training is an important aspect of these rapidly expanding advancements by having the ability to best use the technology with different staff for more access to patients. While I was unable to find specifics on how cath lab techs and other staff will be able to use machine learning, I still strongly believe it could be a great skill for the upcoming future of cardiology. That is the importance of having training to best use the state-of-the-art equipment to have best patient outcomes. These are essential to master robotic systems and help make the integration into workflows. Despite all these advancements in technology and innovations, challenges remain. According to Koulaouzidis et al. and Najafi et al., 2,10 advanced technology has come at a high cost and brings up being able to integrate these systems to be used in the labs. Artificial intelligence and robotics have demonstrated significant potential to enhance precision, safety and efficiency in coronary interventions. These technologies are still evolving very rapidly and can help in reshaping various realms of cardiology and the cath lab. With the rapid advancements, the question that can be asked is if there is any kind of implemented policy that could happen to help with the cost of bringing these emerging technologies to improve access to more patients. By increasing access and implementing these innovations more broadly, more patients will have access to improved outcomes and can help make more precise and less invasive procedures. Conducting this research it has been clear that there is not a lot of research that has included the role of what cath lab techs will have in the emerging realm of artificial intelligence. I 18 think some of the most important roles that cath lab techs can have can be being aware of the emerging technology and artificial intelligence that will be introduced to cardiology and the cath lab in the coming years. Cath lab technologists can play a vital role in radiation safety for themselves and throughout the cath lab environment. Additionally, their involvement in training programs focused on the set up and operation of emerging technologies as well as adopting the skillset to learn machine learning. By learning these additional skills cath lab techs can be set up for success in the changing realm of cardiology and the cath lab. Overall, the integration of AI and robotic technologies can enhance procedural precision, improve patient care, and expand necessary treatments to best streamline workflow processes. 19 Chapter 3: Research Method For this thesis a systematic literature review was used to investigate the integration of artificial intelligence and robotics into cardiology and catheterization labs. The systematic approach was selected to help with a rigorous and replicable methodology for analyzing research, identifying trends, and synthesizing findings that were related to remarkable innovations in AI in cardiology. This method was chosen over other research methods like empirical clinical trials or qualitative case studies due to the ability to consolidate a broad range of existing peer reviewed articles. By doing so a comprehensive understanding of the current state of AI and robotic applications in the field was able to be done. With the evolving nature of AI in healthcare, being able to use evidence from multiple studies was helpful to ensure and to make generalizable conclusions. One of the main reasons for this design was the ability to systematically identify research gaps and future directions of AI and cardiology. There was analysis of past studies that highlighted areas that required further exploration like long term clinical outcomes, economic implications and the evolving role of cath lab tech and allied health professionals. By using the systematic approach, it was helpful to identify these gaps based on broad based evidence as opposed to observations. Additionally, this thesis aimed to assess the clinical impact that AI and robotic assisted procedures would have in interventional cardiology. After the evaluation of the existing evidence of various studies and articles insights were provided that could help in possible future technological advancements. Alternative research methods like randomized controlled trials or cohort studies were thought to be not suitable because of 20 the reliance on primary data collections. Similarly, qualitative methods were also not used because of limited access to diverse professionals across multiple institutions as well as wanting less subjectivity or generalizability. Research Methods and Design(s) For this thesis a systematic literature review was used to investigate the integration of artificial intelligence and robotics into cardiology and catheterization labs. The systematic approach was selected to help with a rigorous and replicable methodology for analyzing research, identifying trends, and synthesizing findings that were related to remarkable innovations in AI in cardiology. This method was chosen over other research methods like empirical clinical trials or qualitative case studies due to the ability to consolidate a broad range of existing peer reviewed articles. By doing so a comprehensive understanding of the current state of AI and robotic applications in the field was able to be done. With the evolving nature of AI in healthcare, being able to use evidence from multiple studies was helpful to ensure and to make generalizable conclusions. One of the main reasons for this design was the ability to systematically identify research gaps and future directions of AI and cardiology. There was analysis of past studies that highlighted areas that required further exploration like long term clinical outcomes, economic implications and the evolving role of cath lab tech and allied health professionals. By using the systematic approach, it was helpful to identify these gaps based on broad based evidence as opposed to observations. Additionally, this thesis aimed to assess the clinical impact that AI and robotic assisted procedures would have in interventional cardiology. After the evaluation of the 21 existing evidence of various studies and articles insights were provided that could help in possible future technological advancements. Alternative research methods like randomized controlled trials or cohort studies were thought to be not suitable because of the reliance on primary data collections. Similarly, qualitative methods were also not used because of limited access to diverse professionals across multiple institutions as well as wanting less subjectivity or generalizability. Use of PRISMA for transparency, completeness and reproducibility The PRISMA guidelines were utilized to help with transparency, completeness and reproducibility of this systematic review. By using these guidelines, it was helpful to have a structured framework to follow to have a good methodology and to help minimize bias in the study selection process. To help with transparency a PRISMA diagram was used to illustrate possible articles that had identification, screening, eligibility assessment and an inclusion and exclusion criteria. By using the diagram, it was helpful to help filter studies, so the review process was able to be systematic and reproducible. A comprehensive literature search was conducted with various databases like PubMed, Scopus, Google scholar and Wiley Online Library were used to search with. A predefined inclusion criteria for relevant study usage was used to find relevant and quality studies. With the use of multiple databases there was increased coverage and a reduced risk of missing other relevant studies. Also to help in minimizing bias of bias clear eligibility criteria and structured approach was used to find a diverse and various number of studies and articles. Critical appraisal of sources was used to help in evaluating the quality of studies to help ensure inclusion of high-quality evidence 22 Systematic Review process and steps - - 1) Formation of research questions guided by PICOS framework to define the scope of the revie 2)Database selection and search strategy development keywords used were “AI and Robotics”, “Interventional Cardiology and AI”, “Cath Lab and AI/ Robotics”, “Cath Lab Technologists and Artificial Intelligence” and “Robotics and Artificial Intelligence Costs”. 3) Study selection and screening duplicate removal with title/ abstract screening and full text assessment conducted with PRISMA guidelines 4) Data extraction and synthesis relevant information with study objectives, methodologies and results and conclusions systematically extracted and analyzed 5) Critical appraisal studies evaluated quality using various objectives and tools. 6) Synthesis and Reporting findings were categorized into key themes. Some of the themes were AI driven diagnostic tools, robotics assisted procedures and clinical Justification for research design I used the systematic research review because I thought it was the optimal choice for this thesis by aligning with the objective to synthesize existing research on AI and robotics in cardiology as opposed to generating new data. Along with the rapid evolution and these technologies in medicine and cardiology, the systematic review provided a broad and I thought also detailed criteria. A defined research question was able to be formulated which focused on roles, benefits and challenges in AI and robotics in interventional cardiology. I systemically searched using multiple databases including Google Scholar, Euro Intervention, Frontiers in Cardiovascular Medicine and Wiley Online Library. With these choices of databased I was able to have coverage of peer reviewed articles. By using the PRIMSA application there was an emphasis on the need of a clearly defined search strategy. I was able to apply inclusion and exclusion criteria with studies focusing on AI applications in diagnostics, decision making support, imaging, robotics assisted interventions and training for cath lab techs. For exclusion 23 criteria some studies liked data helpful for the study and some were published before 2019. For screening and selection of various studies and articles, I was able to document study selection processes. Some examples were identification by extracting articles based on keywords. I think by using these I was able to have a structured approach to minimize bias and was able to have the inclusion of high-quality studies. Data extraction and synthesis were conducted by extracting key information such as study design, population, AI robotic technology used, outcomes and limitations. I grouped studies into various categories that were most alike. For example, diagnostic AI, robotic assisted PCI, imaging innovations and training methods. By using this qualitative synthesis of information allowed me to make various comparisons across the studies to find emerging trends. By following this structured systematic approach, I was able to achieve the objectives to synthesize the existing knowledge and identified research gaps. Additionally, I was able to provide insights to inform future advancements in AI and robotics in interventional cardiology. By using PRISMA and the other tools I think that helped strengthen the thesis by having transparency, completeness and reproducibility. After using all these various tools I think that also helped with having relevant research to be applicable to various researchers in the field. Materials/Instruments Assumptions The research done for this thesis was under several key assumptions. For example, it was assumed that the data extracted from clinical studies and trials regarding robotic assisted PCI, is accurate, reliable and reflective of real outcomes. Additionally, it 24 was presumed that advancements in artificial intelligence and robotics within cardiology and catheterization laboratories will continue to progress consistently with the current trends. Another assumption is that healthcare professionals involved in these robotic assisted procedures were all upheld to standardized protocols and best practices. Without these key assumptions, the validity of the comparisons drawn between the traditional and AI- assisted interventions would not be valid. Limitations Several limitations existed within the framework of this research. One primary limitation is the availability of long-term data on robotic assisted PCI and AI integration in cath labs. That is because these technologies are still evolving constantly and have not been around for very long. Another limitation in the research is the potential for bias in some of the existing literature with AI and robotic technology. Also, this research may not account for all variables for procedural outcomes some of them being operator experience, patient comorbidities and differences in institutions with the adoption of these innovative technologies. Lastly, there may be financial constraints and other challenges that can be associated with implemented robotic systems in various healthcare settings which could be limiting for the generalized findings. Delimitations The focus of this thesis was on the integration of AI and robotics in cardiology and had an emphasis on robotic assisted PCI in catheterization laboratories. There are other applications of AI in cardiology such as diagnostics or predictive analytics which were beyond the scope of this research. The research also primarily excluded nonrobotics interventional cardiology advancements to help in remaining more targeted 25 towards technological advancements and developments. Also, the geography of the research primarily examines studies that were conducted in developed healthcare systems where robotics assisted PCI was able to be implemented. Furthermore, this research primarily relied upon peer reviewed literature and clinical trials published within the last decade to help with relevance and accuracy. Ethical Assurances Ethical considerations are some of the most important findings from this research, particularly with human clinical research. All referenced studies and trials were assumed to have been conducted following all ethical guidelines such as informed consent, patient confidentiality and IRB approvals. There were no new human or animal subjects involved in this study given that it was dependent on existing literature and research. Additionally, efforts were made to introduce the information and findings to ensure no biases within the reviewed studies needed to be acknowledged. Ethical concerns around AI decision making, patient safety and clinician oversight in robotic assisted procedures were also discussed. All of these were helpful to address the broader ethical implications that could be brought up with technological integration into healthcare. Summary This thesis used a systematic literature review to explore the integration of artificial intelligence (AI) and robotics in cardiology and catheterization laboratories. The reasoning for the systematic approach was selected to ensure a rigorous, transparent, and replicable method for analyzing peer-reviewed studies, identifying trends, and synthesizing findings. It offered a broader and more generalizable understanding than primary data collection methods such as clinical trials or qualitative case studies. The 26 PRISMA guidelines were used to enhance transparency and minimize bias in study selection, with inclusion and exclusion criteria applied across several databases including PubMed, Scopus, and Wiley Online Library. Studies were categorized into key themes such as AI-driven diagnostics, robotic-assisted PCI, imaging innovations, and training for allied health professionals. The systematic review design also allowed for the identification of research gaps, particularly in long-term clinical outcomes, cost-effectiveness, and the evolving role of cath lab technologists. The method enabled a qualitative synthesis of evidence to compare emerging trends and potential clinical impacts of AI and robotics in interventional cardiology. Limitations included the evolving nature of the technology, limited long-term data, and potential biases in existing studies. Assumptions were made about the accuracy of reported clinical outcomes and adherence to standardized protocols in robotic procedures. Ethical assurances were addressed by relying solely on previously published, ethically approved studies, and by acknowledging broader concerns related to AI decision-making and clinician oversight. The research was delimited to roboticassisted PCI in developed healthcare systems and excluded non-robotic advancements to maintain a focused scope. 27 Chapter 4: Findings Results The integration of artificial intelligence (AI) and robotics in interventional cardiology has made significant advancements over the last few years. The biggest advancements being in procedural precision, patient outcomes, and occupational safety. A specific example of advancements has been robotic-assisted percutaneous coronary intervention (R-PCI) which has shown key benefits over manual PCI (M-PCI). The reduction in longitudinal geographic miss (LGM), has been a factor that has influenced long-term clinical outcomes. The PRECISE trial found that R-PCI significantly decreased the incidence of LGM (12.2% vs. 43.1%, p < 0.0001) due to AI-assisted robotic systems contributing to the improvement of lesion length measurement accuracy in comparison to the traditional visual estimation.2 Clinical success rates for R-PCI are high, with the CORA-PCI study reported a 99.1% success rate across 157 lesions, 78.3% of which were complex lesions.1,2 Additionally, a study conducted at Bern University Hospital in Switzerland achieved a 100% clinical success rate, with 81% of the cases performed entirely robotically and 19% required partial manual conversion for assistance with poor guiding catheter, back-up or platform limitations.11 These studies were all performed through AI technology and have all consistently shown successful and safer results. Throughout this research it has been consistent in portraying that a main benefit that has drawn providers to incorporating AI in their procedures is the reduction in radiation exposure for operators. For example, a study from Kurume University Hospital reported that radiation exposure for physicians was nearly eliminated in R-PCI compared to the traditional M-PCI (0 µSv vs. 24.5 µSv, p < 0.0001).4 This study has similar results 28 to the PRECISE trial mentioned previously, where there was a reported 95.2% reduction in physician radiation exposure with R-PCI. The reduction in radiation exposure benefits not only the patient’s safety, but the long-term safety of the whole interventional team.2,4 Newly innovative AI based platforms like the TAVIPILOT AI drive AiiR have been able to integrate a multimodal medical image analysis, digital twin technology and autonomous robotic control helping procedure precision in various heart valve positioning. This will be helpful because around 1.7 million people just across the EU and United States need TAVI procedures, while around 300,000 TAVI procedures are done annually. AI has also played a big role in enhancing diagnostic capabilities in cardiology. A retrospective study using AI-derived Fractional Flow Reserve (FFR) values from 304 vessels across three Israeli medical centers demonstrated high sensitivity and specificity in the ability to detect significant to coronary lesions (FFR ≤ 0.80).3 Another AI-driven imaging technology called C-arm Cone-Beam CT (CBCT), has also improved procedural precision while minimizing unnecessary radiation exposure by incorporating AI technology.12 Evaluation of Findings These findings mentioned above have highlighted the potential of AI and robotics in interventional cardiology. With precision improvements associated with R-PCI and AI assisted diagnostics, to lower radiation exposure using R-PCI, all contribute to better patient prognosis through more efficient procedures. Although many studies have shown there are great advantages to this technology, challenges remain while this technology continues to develop. There have been studies that show R-PCI may result in longer procedural times, and that the R-PCI may not even be an option for complex cases, where manual intervention may be required. For 29 example, at Inselspital 19% of R-PCI procedures needed partial manual conversion.11 This makes the procedures more time consuming for the staff, leading to burnout. Financial constraints remain a significant barrier to the widespread adoption of AI and robotics in interventional cardiology. The high costs of robotic platforms, AI-driven imaging technologies, and specialized training programs must be carefully evaluated in terms of long-term cost-effectiveness and healthcare resource allocation.2,14 Summary In conclusion, AI and robotics are making a lot of advancements in interventional cardiology by improving procedural accuracy, patient safety, as well as occupational health for medical professionals. Throughout multiple studies it seems that R-PCI has demonstrated comparable or superior clinical success rates to M-PCI while reducing LGM and has minimized radiation exposure for physicians. With the help of AI-driven imaging and diagnostic tools are being enhanced to help the assessment of coronary lesions and guiding interventional decisions with increased precision. From this research it seems that these advancements can help the physician and interventional teams but not replace. Although these advancements have proven to have great benefits, existing challenges must be addressed for their continued success. Some examples of these challenges included procedural inefficiencies, the need for further validation of AI algorithms, and the high cost of implementation. To further aid in developing this research potential for future research to focus on could be long-term clinical outcomes, cost-benefit analyses, and strategies to integrate these technologies into mainstream cardiovascular care effectively. Overcoming these barriers will be critical to fully 30 realizing the potential of AI and robotics in improving patient outcomes and advancing interventional cardiology. 31 Chapter 5: Implications, Recommendations, and Conclusions Implications As artificial intelligence and robotics have been integrated into cardiology and cath labs there have been implications for clinical practice, healthcare operations, economics, education and global health equity. With the ability of the AI algorithms to demonstrate enhanced diagnostic accuracy and speed up of clinical decision-making processes with the automation of interpreting complex medical images like coronary computed tomography angiograph (CTA) and echocardiograms. The addition of CorPath GRX and R-One and TAVIPILOT AiiR have all shown improvements in procedural precision and reduction in fatigue of operators, along with improvements in radiation exposure. The benefits of this technology can lead to long-term safety in healthcare environments and specifically in cath labs. By implementing these technologies, they can help to streamline workflows, optimize procedural planning and enable stimulated based training programs that will be needed to prepare healthcare providers for use the advanced interventions to the best of their ability. Although the initial costs of the equipment are high and training may be expensive, the long-term benefits of these emerging technologies are reduced procedural errors, improved patient outcomes, and enhanced efficiency to possibly lead to lower healthcare costs. From the educational aspect of the evolving role of the cardiovascular technologist there is a need for targeted training programs to equip them along with other allied health professionals to operate the AI- driven systems to the best of their ability. These technologies have potential to help on a global scale in cardiovascular care by expanding access to underserved areas. Which has been successfully demonstrated transatlantic telerobotic coronary angiography 32 by Madder et al., offers a signal of future directions in cardiovascular care, where distance may no longer be a barrier to expertise.20 This has significant implications for global access to care and demands a parallel evolution in training models for allied health professionals, who may be called upon to support robotic systems operated by remote physicians. Investing in scalable, cross-platform training and international certification pathways will be essential to support this emerging technology. However, in order to better integrate technology, a collaborative effort throughout the world needs to occur among stakeholders and additional policies need to be implemented for the innovative AI and robotics to be better accessible on an even broader scale. Recommendations To address the challenges that come with implementing AI and robotics in cardiology and to help maximize their benefits, a few recommendations have emerged from this research. First, healthcare institutions could invest in stimulation-based training programs to address the gap in training preparedness and have dedicated course material on AI and robotics in cardiology. This course would equip allied health professionals with the foundational knowledge to understand AI algorithms, practical skills to interpret AI augmented imaging and procedural data and with the ethical insight to evaluate these technologies in decision making. By incorporating training before adopting this equipment into real life cases, the allied healthcare workers would have a smoother transition from passive users of technology to informed collaborators for patient centered cardiovascular care in the rapidly changing digital era. (see below for example of college course in appendix a) Second, collaboration between governments and healthcare organizations could help in the development of policies that assist with initial costs of 33 equipment and adoption, specifically in rural or underserved areas because most times that is where the adoption of new technologies is limited. Third, with the focus on workflow integration strategies within healthcare institutions by being able to integrate strategies to make minimal disruptions to already existing workflows. Fourth, by expanding research initiatives with continued research to refine AI algorithms and innovative robotic technologies. Some of these could be focusing on the improvement of safety, reliability and simplification to use. Research could explore the impact of the new technologies to improve clinical outcomes and workflow inefficiencies. Fifth could be global access to care by encouraging support of the implementation of AI and robotics into underserved areas. Conclusions In conclusion, to recognize the benefits of AI and robotics in cardiology globally its essential to address barriers that come with it. This thesis has explored how AI-driven algorithms optimize imaging technologies like coronary CTA. While robotic systems such as CorPath GRX, RoboCath and TAVI Pilot AiiR helping to improve procedural outcomes by reducing operator fatigue and radiation exposure. These upcoming technologies have proven their ability to enhance diagnostic accuracy, procedural precision, workflow efficiency, and safety for both patients and healthcare providers. Addressing these barriers include investing in education programs, ongoing research efforts, and phased integration strategies. By proactively finding solutions to these obstacles while also promoting innovation in cardiovascular technology development, these advancements reshape interventional cardiology and improve patient outcomes. The future of AI and robotics in cardiology is promising, but also requires collective efforts 34 from healthcare providers, researchers, and industry leaders to ensure this equipment can be accessible globally. 35 References 1. Beyar R, Davies JE, Christopher, Dudek D, Cummins P, Bruining N. Robotics, imaging, and artificial intelligence in the catheterisation laboratory. 2021;17(7):537-549. doi:https://doi.org/10.4244/eij-d-21-00145 2. Koulaouzidis G, Charisopoulou D, Bomba P, et al. Robotic-Assisted Solutions for Invasive Cardiology, Cardiac Surgery and Routine On-Ward Tasks: A Narrative Review. Journal of Cardiovascular Development and Disease. 2023;10(9):399. doi:https://doi.org/10.3390/jcdd10090399 3. Eyal Ben-Assa, Salman AA, Cafri C, et al. Performance of a novel artificial intelligence software developed to derive coronary fractional flow reserve values from diagnostic angiograms. Coronary Artery Disease. 2023;34(8):533-541. doi:https://doi.org/10.1097/mca.0000000000001305 4. Kotaro Kagiyama, Yoshiaki Mitsutake, Ueno T, et al. Successful introduction of roboticassisted percutaneous coronary intervention system into Japanese clinical practice: a firstyear survey at single center. Heart and Vessels. 2021;36(7):955-964. doi:https://doi.org/10.1007/s00380-02101782-6 5. Ohashi H, Frédéric Bouisset, Dimitri Buytaert, et al. Coronary CT Angiography in the Cath Lab: Leveraging Artificial Intelligence to Plan and Guide Percutaneous Coronary Intervention. Interventional Cardiology Review. 2023;18. doi:https://doi.org/10.15420/icr.2023.12 6. Blandino A, Bianchi F, Masi AS, et al. Outcomes of manual vctaersus remote magnetic navigation for catheter ablation of ventricular tachycardia: a systematic review and updated meta‐analysis. Pacing and Clinical Electrophysiology. 2021;44(6):1102-1114. doi:https://doi.org/10.1111/pace.14231 7. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial Intelligence in Cardiology. Journal of the American College of Cardiology. 2018;71(23):2668-2679. doi:https://doi.org/10.1016/j.jacc.2018.03.521 8. Interventional in. Robotics in Interventional Oncology: The Next Frontier in ImageGuided Interventions - Endovascular Today. Endovascular Today. Published 2023. Accessed November 11, 2024. https://evtoday.com/articles/2023-oct/robotics-ininterventional-oncology-the-nextfrontier-in-image-guidedinterventions?c4src=archive:feed 9. Khokhar AA, Marrone A, Konstantinos Bermpeis, et al. Latest Developments in Robotic Percutaneous Coronary Interventions. Radcliffe Cardiology. Published December 6, 2023. Accessed November 11, 2024. https://www.icrjournal.com/articles/latestdevelopments-roboticpercutaneous-coronary-interventions 10. Najafi G, Kreiser K, Mohamed, Hamady MS. Current State of Robotics in Interventional Radiology. Current State of Robotics in Interventional Radiology. 2023;46(5):549-561. doi:https://doi.org/10.1007/s00270-023-03421-1 36 11. Häner JD, Räber L, Moro C, Losdat S, Windecker S. Robotic-assisted percutaneous coronary intervention: experience in Switzerland. Frontiers in Cardiovascular Medicine. 2023;10:1294930. doi:https://doi.org/10.3389/fcvm.2023.1294930 12. Yang F, Planche B, Zheng M, Chen C, Chen T, Wu Z. Automating Catheterization Labs with Real-Time Perception. arXiv.org. Published 2024. Accessed December 6, 2024. 13. Leung, J., Xu, J., French, J. K., Hashim Kachwalla, Kaddapu, K., Badie, T., Mussap, C., Rajaratnam, R., Leung, D. Y., Lo, S. T., & Juergens, C. (2024). Rationale and design of the randomised, controlled Percutaneous coronary intervention using Assisted Robotic TechnologY (PARTY) trial. Open Heart, 11(2), e002950–e002950. https://doi.org/10.1136/openhrt-2024-002950 14. Liu, Z.-Y., & Zhai, G.-Y. (2024). Narrative review of latest research progress about robotic percutaneous coronary intervention. Journal of Geriatric Cardiology, 21(8), 816– 825. https://doi.org/10.26599/1671-5411.2024.08.004 15. Accueil - Caranx Medical. (2025, January 14). Caranx Medical. https://caranxmedical.com/ 16. Alzahrani M, Altowairqi A, Alzahrani M, et al. Interventional cardiologists’ perspectives and knowledge towards radiation safety: A cross-sectional study. J Invasive Cardiol. 2024;36(3):E226-E232. doi:10.25270/jic/24.00052 17. Radetzky, A., Stauder, R., Uder, M., & Bumm, K. (2021). Artificial intelligence in image-guided surgery: A review of the current state and future perspectives. European Journal of Radiology, 139, 109697. https://doi.org/10.1016/j.ejrad.2021.109697 18. Robocath. Future of robotics in the cath lab – Robocath presentation. Published May 2024. Accessed April 6, 2025. https://www.robocath.com/wpcontent/uploads/2024/05/Future-of-robotics-inthecathlab_Robocath_2024_VDEF_BD.pdf 19. Patel T, Shah S, Soni Y, et al. Comparison of Robotic Percutaneous Coronary Intervention With Traditional Percutaneous Coronary Intervention. 2020;13(5). doi:https://doi.org/10.1161/circinterventions.119.008888 20. Madder RD, VanOosterhout S, Parker JL, Candreva A. Transatlantic Telerobotic Coronary Angiography. JACC Advances. 2024;4(1):101456-101456. doi: https://doi.org/10.1016/j.jacadv.2024.101456 21. Gil RJ, Beyar R., Haude M. . A brief history and clinical use of robotic procedures in the cardiovascular system. Pol Heart J. 2025;83(3):1–7. Accessed April 17, 2025. https://journals.viamedica.pl/polish_heart_journal/article/view/105099/81780 37 Appendix A: College Course on integration of AI in cardiology and the Cath Lab Course Description/ Introduction This course explores the transformative impact of artificial intelligence (AI) and robotics in cardiology with a specific focus on cardiac catheterization laboratory (cath lab) technologies. Students will engage with current and emerging applications of AI, machine learning, and robotic systems in diagnostic imaging, interventional cardiology, workflow optimization, and patient outcomes. Through lectures, case studies, practical simulations, and analysis of landmark clinical trials, technologists will gain the theoretical foundation and applied skills necessary to integrate cutting-edge technologies into their practice. This course is designed for aspiring or practicing cath lab technologists, aiming to prepare them for the evolving landscape of interventional cardiology where technological proficiency is increasingly essential. Course Learning Objectives By the end of this course, students will be able to: 1. Explain the foundational concepts of AI and robotics as applied to cardiology and interventional procedures. 2. Evaluate the current applications of AI in diagnostic imaging, risk prediction, and procedural planning. 3. Identify the clinical benefits, limitations, and safety considerations of roboticassisted PCI and electrophysiology. 4. Analyze landmark studies and trials (e.g., PARTY trial, PRECISE, etc.) involving AI and robotic systems. 38 5. Interpret data generated by AI-assisted tools and integrate findings into clinical decision-making. 6. Apply knowledge of robotics to procedural support and understand workflow integration in the cath lab. 7. Demonstrate ethical reasoning in the use of patient data, machine learning algorithms, and automation in healthcare. 8. Anticipate future directions and innovations in cardiovascular technology and their implications for cath lab practice. Course Modules Module 1: Introduction to AI and Robotics in Healthcare • Definitions, history, and development of AI and robotics • Relevance in modern healthcare and cardiology Module 2: Fundamentals of AI and Machine Learning • Supervised vs. unsupervised learning • Neural networks and decision-making algorithms • Basic programming logic and algorithm transparency Module 3: Cardiovascular Imaging and AI • AI in echocardiography, CT angiography, and MRI • Image interpretation, segmentation, and reporting automation Module 4: AI in Clinical Decision Support Systems (CDSS) • Risk prediction models (e.g., CAD-RADS, SYNTAX scores) • Integration into EHRs and procedure planning 39 Module 5: Robotic Systems in the Cath Lab • History and evolution of robotic PCI systems (e.g., CorPath GRX) • Robotics in electrophysiology and structural heart procedures Module 6: Human-Machine Interaction in the Cath Lab • Workflow integration • Interface usability, safety protocols, and technologist roles Module 7: Clinical Applications and Case Studies • Analysis of real-world uses of AI and robotics in cath lab settings • Successes, failures, and lessons learned Module 8: Review of Key Trials and Evidence • PARTY trial, PRECISE-DAPT, and others • Evidence-based support for robotic and AI-assisted interventions Module 9: Radiation Safety and Robotics • Dose reduction through robotic PCI • Technologist exposure and long-term health implications Module 10: Ethical and Legal Considerations • Patient data privacy, bias in algorithms • Informed consent and liability in AI-assisted care Module 11: AI in Electrophysiology and Device Implantation • Arrhythmia mapping, AFib ablation planning • Predictive modeling for device therapy 40 Module 12: Simulation Training in Robotic and AI Systems • Hands-on virtual and augmented reality simulations • Developing muscle memory and visual interpretation skills Module 13: Future Trends in Cardiovascular Technology • Predictive analytics, digital twins, remote robotic interventions • The potential of AI in personalized cardiology Module 14: Preparing the Technologist Workforce • Skills development, certification pathways • Interdisciplinary collaboration and communication Module 15: Capstone Project and Final Assessment • Student-led presentations of a proposed AI/robotic integration in a cath lab setting