Cross-Sectional Survey on Artificial Intelligence Adoption in Cardiac Catheterization: Insights from Interventional Cardiologists and Allied Professionals By Nathaniel Faatoafe Bharat Alexander Nadir Khan A thesis submitted to the School of Radiologic Sciences in collaboration with a research agenda team In partial fulfillment of requirements for the degree of MASTER OF SCIENCE IN RADIOLOGICAL SCIENCES WEBER STATE UNIVERSITY Ogden, Utah December 13, 2024 THE WEBER STATE UNIVERSITY GRADUATE SCHOOL SUPERVISORY COMMITTEE APPROVAL of a thesis submitted by Nathaniel Faatoafe Bharat Alexander Nadir Khan 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 THE WEBER STATE UNIVERSITY GRADUATE SCHOOL RESEARCH AGENDA STUDENT APPROVAL of a thesis submitted by Nathaniel Faatoafe Bharat Alexander Nadir Khan This thesis has been read by each member of the student research agenda committee and by majority vote found to be satisfactory. Date December 13, 2024 ____________________ ___________________________ Nathaniel Faatoafe December 13, 2024 ____________________ ___________________________ Bharat Alexander December 13, 2024 ____________________ ______________________________ Nadir Khan Abstract Background With the advent of artificial intelligence (AI) in healthcare, its integration into cardiac care, particularly in catheterization labs, is of significant interest. This study aims to assess the perceptions and readiness of interventional cardiologists and allied health professionals toward adopting AI technologies in cardiac procedures. This study provides the attitudes, concerns, and barriers to adopting AI in catheterization by conducting a detailed assessment. The background establishes the context of rapid technological advancement and the necessity to evaluate its acceptance within specialized medical fields. Table of Contents Chapter 1: Introduction ..................................................................................................1 Background ........................................................................................................1 Statement of the Problem....................................................................................2 Significance of the Problem................................................................................3 Purpose of the Study ..........................................................................................3 Research Questions ............................................................................................6 Nature of the Study ............................................................................................7 Significance of the Study ...................................................................................7 Definition of Key Terms.....................................................................................7 Summary.............................................................................................................8 Chapter 2: Literature Review..........................................................................................9 Documentation ..................................................................................................10 Historical Development of Al in Cardiology.....................................................11 Applications of AI in Cath Lab..........................................................................14 Challenges in Interventional Cardiology Al......................................................17 AI Concerns in Cath Lab...................................................................................20 Summary............................................................................................................21 Chapter 3: Research Method..........................................................................................21 Research Methods and Design(s).......................................................................22 Population ..........................................................................................................22 Sample................................................................................................................22 Materials/Instruments ........................................................................................23 Operational Definition of Variables (Quantitative/Mixed Studies Only) .......23 Data Collection, Processing, and Analysis .....................................................23 Assumptions.....................................................................................................25 Limitations ......................................................................................................25 Delimitations ...................................................................................................26 Ethical Assurances ..........................................................................................26 Summary..........................................................................................................26 Chapter 4: Findings ....................................................................................................27 Results ............................................................................................................28 Evaluation of Findings.....................................................................................28 Summary..........................................................................................................31 Chapter 5: Implications, Recommendations, and Conclusions...................................32 Implications.....................................................................................................32 Recommendations............................................................................................32 Conclusions......................................................................................................34 References....................................................................................................................36 Appendices ......................................................................................................41 Appendix A: Survey Instrument......................................................................41 Appendix B: Recruitment Materials................................................................43 Appendix C: Data Collection Overview..........................................................45 Appendix D: Results Summary……………………………………………...46 Appendix E: IRB Approval and Ethical Compliance .....................................47 Appendix F: Tables and Graphs……………………………………………..48 Appendix G: Glossary of Terms......................................................................49 Appendix H: Sample Raw Data.......................................................................50 1 Chapter 1: Introduction Cardiac catheterization labs have evolved using artificial intelligence (AI) integration technology to treat heart disease.1 AI integration is transforming cardiac catheterization labs by enhancing operational efficiency, procedure accuracy, and patient outcomes. 1 Current technology, such as predictive analysis and robotic-assisted interventions, support making informed decisions in complex cardiac cath lab procedures. 1 A previous survey published in the Journal of Invasive Cardiology highlighted that 84.7% of interventional cardiologists were familiar with AI concepts. Yet, only 22.1% had implemented AI in their practice.2 This disparity between awareness and practical application is because of the need to explore the perceptions further the perceptions and challenges faced by Cath Lab staff. This study focuses on including allied health professionals whose roles are essential in the success of AI tools, providing a comprehensive perspective on adoption. Background The role of AI integration in healthcare has been rapidly evolving, offering new possibilities in diagnostics, assistive support in procedures, and patient care, especially in cardiac catheterization labs. AI is significantly valuable in treating coronary artery disease and structural heart disease.1,3 Especially in imaging technology (X-ray, fluoroscopy, CT-scan, etc.), AI now enables various imaging modalities such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT), which are powerful imaging guidance tools in coronary diagnostic and 2 3 intervention in the cath lab guide and support cath lab staff. In addition to predictive analysis and robotic-assisted intervention. AI-based studies have shown predictive analysis with IVUS and OCT images to predict and assess plaque characteristics and vulnerability and the likelihood of adverse cardiovascular events, leading to improve optimization of treatment susceptibility as a stent or medication therapy.1,3,22, 24 This integration allows Cath lab staff to make more informed decisions in real-time, anticipate procedure risks, and improve procedural accuracy, efficiency, and patient outcomes.3 Despite the many advancements, AI adoption in cardiac catheterization faces several hurdles, such as technical integration issues, requiring a high degree of standardization and integrating protocols, and concerns about data privacy and accuracy/reliability in high-risk cardiac procedures.1 Findings from the Journal of Invasive Cardiology revealed that 73.5% of interventional cardiologists believe there is insufficient knowledge and training to use AI effectively, while 46.1% express the need for role-specific educational programs.2 This highlights a crucial barrier to successful integration. Moreover, allied health professionals, who often manage AI tools during procedures, remain underrepresented in research on AI adoption. 2 Statement of the Problem While AI offers transformative potential in cardiac catheterization labs, limited research exists on how interventional cardiologists and allied health professionals perceive its integration. Data from the original survey revealed high awareness but low implementation with 84.7% interventional cardiologist understand AI concepts and only 22% have integrated AI into their 3 clinical practice. Barriers such as 46.1% of interventional cardiologists emphasized the need for targeted training, and 73.5% believe of physician sufficient AI knowledge for patient care, . 2 This study aims to bridge the gap by examining perceptions, barriers, and educational needs across multidisciplinary Cath Lab teams. Significance of the Problem This study contributes to the understanding of AI adoption in cardiac catheterization labs by integrating the perspectives of interventional cardiologists and allied health professionals. Insights from the original survey underscore the importance of addressing training gaps and fostering positive perceptions to ensure seamless integration of AI into clinical workflows. By identifying specific barriers and opportunities, this research aims to inform targeted strategies for optimizing AI implementation, ultimately enhancing patient outcomes and procedural efficiency. For example, it aims to guide the development of targeted training programs to improve AI literacy and confidence among cath lab staff. Inform policy decisions and workflow strategies to improve the application of AI tools. Purpose of the Study The purpose of this study is to examine the perceptions of interventional cardiologists and allied health professionals regarding AI integration in cardiac catheterization labs. It builds upon findings from the original survey to explore current attitudes, perceived 4 barriers, and strategies to improve AI adoption . Study Objectives Investigate Perception: Explore the attitudes of interventional cardiologists and allied health professionals towards AI integration, including perceived benefits and concerns. Identify Barriers and Enablers: Evaluate factors that influence AI adoption in a cardiac catheterization lab, such as technical challenges, data privacy concerns, work displacement, and procedure efficiency. Evaluate Impact on Clinical Practices: Analyze how AI integration affects procedural accuracy, efficiency, and patient outcomes for interventional cardiologists and allied healthcare professionals. Predict Future Adoption Trends: Examine how cath lab professionals envision the future role of AI in interventional cardiology and identify the educational needs to support this transition. Hypotheses (1) Interventional cardiologists and allied healthcare professionals with positive attitudes towards AI are more likely to be supported in the cardiac catheterization lab, leading to improved procedure outcomes. (2) Education and comprehensive AI training are critical factors in accepting and adopting AI use among cardiac cath lab staff. (3) Interventional cardiologists and allied health professionals who are more familiar with AI tools are likely to demonstrate increased confidence in incorporating AI into clinical workflows. 5 Expected Outcomes Studies expected to provide insights into specific perceptions and challenges faced by cath lab staff regarding AI integration. Findings will inform targeted strategies for promoting AI adoption, improving clinical outcomes, and optimizing procedure workflows in the cardiac catheterization labs. (1) The study will show critical training gaps in education in interventional cardiologists and emerging allied health professionals. (2) Correlation between awareness of AI tools and confidence in their use, encouraging the importance of training. (3) Barriers such as data privacy concerns and work integration suggest strategies to address these challenges. (4 ) The study will provide evidence-based recommendations for fostering collaboration and improving AI adoption across multidisciplinary cath lab teams. Methodology Overview This study will utilize a quantitative research design and a survey to gather data from interventional cardiologists and allied healthcare professionals in the catheterization labs. The survey, distributed via the Qualtrics platform, collects responses to explore the perception, attitudes and experiences regarding AI integration. The survey aims to identify critical factors influencing AI adoption, and assess the perceived impact on procedure accuracy and patient outcomes by analyzing the study. Understanding interventional cardiologists and allied healthcare professionals views on AI technology can inform strategies for practical use in cardiac catheterization labs. 6 Limitations and Delimitations Hospital indifferences worldwide in healthcare infrastructure and AI adoption levels across countries may impact the consistency of responses. Another being the fast pace of AI development in healthcare. Some studies may find this relevant as new technology emerges. Study employs a quantitative survey approach that focuses on capturing and measuring more data related to perception, attitude and barriers without qualitative insights that might reveal deeper content or individual purpose perspectives. Resource constraints limit access to the latest AI technology in cardiac catheterization labs. The study targets interventional cardiologists and allied healthcare professionals and cardiac catheterization labs, excluding general cardiologists, and other healthcare professionals. Research Questions 1.How do interventional cardiologists and allied health professionals understand the role of AI in improving procedural accuracy in cardiac catheterization labs? 2. What are the main barriers and facilitators perceived by interventional cardiologists and allied health professionals regarding the adoption of artificial intelligence in cardiac catheterization labs? 3. How do interventional cardiologists and allied health professionals perceive the impact of artificial intelligence on procedural efficiency and patient outcomes in cardiac catheterization labs? 4. What do interventional cardiologists and allied health care, professionals predict the future 7 will be with the role of AI in the cardiac catheterization laboratory? Nature of the Study This study employs a quantitative survey via Qualtrics platform. The survey gathers data from a diverse sample of interventional cardiologists and allied healthcare professionals in cardiac catheterization labs worldwide. The survey collects data to understand perceptions, barriers, and the impact of AI on clinical outcomes in cardiac catheterization labs. Significance of the Study This study's findings will contribute valuable insights into how interventional cardiologists and allied healthcare professionals perceive Al globally, offering guidance on how to better integrate Al in different healthcare settings. The research will help shape the future of Al adoption strategies in cardiac catheterization labs, informing both policy development and training programs. Key Terms Artificial Intelligence (Al): Machine-based systems that can perform tasks typically requiring human intelligence, such as learning and problem-solving. Cardiac Catheterization Lab (Cath Lab): A specialized hospital room where interventional cardiologists diagnose and treat heart conditions using minimally invasive procedures. Interventional Cardiologist: A cardiologist specializing in catheter-based treatment of heart diseases. 8 Qualtrics: A survey platform used to collect and analyze quantitative and qualitative data. Allied Health Professional: Healthcare professionals in supporting roles, such as nurses, technicians, and technologists. AI-assisted Imaging: The use of AI to enhance medical imaging techniques such as OCT (Optical Coherence Tomography) and IVUS (Intravascular Ultrasound). IRB This research involving human participants, specifically interventional cardiologists and allied health professionals, will adhere to ethical standards. Prior to conducting this study, we will seek approval from the Weber State Institutional Review Board (IRB). This process ensures that the research complies with ethical guidelines, protects the rights and well-being of participants, and maintains data confidentiality. In the event that this study qualifies for IRB exemption, due to its nature of involving minimal risk, this will be duly noted and documented as per IRB guidelines. Summary This study explores the perceptions of interventional cardiologists and allied healthcare professionals regarding AI integration and cardiac catheterization labs. It aims to identify critical factors influencing the acceptance and adoption of AI technology. By analyzing quantitative data on the attitudes, perceived benefits, and challenges related to AI, this research provides valuable insight into the readiness of interventional cardiologists and allied health professionals to integrate AI into the cardiac catheterization lab. The findings are expected to promote strategies for successfully implementing AI, fostering collaboration across multidisciplinary teams, and improving procedure outcomes in a cardiac 9 Catheterization Lab. This research seeks to bridge the understanding and support of interventional cardiologists and allied health professionals in effectively using AI technology in clinical work. Chapter 2: Literature Review Artificial Intelligence (AI) emerges as a groundbreaking force in rapidly evolving healthcare technology, transforming patient care and medical procedures, especially in interventional cardiology. Its integration within the catheterization labs enhances diagnostic precision, procedural efficiency, and patient outcomes. This chapter investigates the evolution, applications, challenges, and opportunities related to AI integration in cardiac catheterization labs. The review starts by defining AI and its importance to healthcare. It focuses on machine learning and predictive analytics in cardiology by tracking its origin and history to advance machine learning algorithms. Examining its diverse AI applications, from AI assist imaging technology to robotic-assisted interventions. The chapter on his score shows the potential of AI to revolutionize procedural, accuracy, and clinical workflows in addition to challenges such as data, security, algorithm bias, and knowledge gaps to provide a balanced perspective. It will conclude with an analysis of concerns raised by healthcare professionals regarding AI implementation and its implications for future practice. 10 Definition and Scope of AI in Healthcare AI is a technology that enables computers and machines to “stimulate human learning, problemsolving, decision making, creativity, and autonomy.”4 There are four types of artificial intelligence: reactive machines, limited memory, theory of mind, and self-awareness. Reactive Machines, respond to particular stimuli without saving past experiences, support real-time imaging in guiding catheters and used to identify blocks during procedure. Limited memory, capable of using past experience for decisions, can predict outcomes by analysing patient history and real time data. Theory of mind could enhance team workflow by understanding emotions and intentions, adapting to decision making patterns. Self-awareness is a long-term possibility in AI, it might autonomously control procedures to respond to certain patient variables 42. AI applications have been used in healthcare, manufacturing, security, and education. 5,6 AI is a nontechnical, misused term referring to machine learning; a subset of AI.7 Machine learning uses data and algorithms to mimic humans' learning, gradually improving accuracy and efficient work systems.8 Machine learning techniques have been developed to analyze high-throughput data to obtain valuable insights, categorize, predict, and make evidence-based decisions in novel ways, promoting the growth of novel applications. 9 This integration is one of many branches of AI in healthcare, which signifies a revolutionary change, particularly in specialized areas like cardiology. The progress of AI integration technologies in cardiac catheterization labs marks a significant improvement. Predictive analytics and automated imaging allow cath lab staff to diagnose and treat coronary artery and structural heart diseases more accurately and efficiently. The scope of AI healthcare extends beyond diagnostics, such as predictive risk modeling and interventional approaches. AI can cross this vast amount of data in real-time and support the decision-making 11 process for cardiology by integrating AI tools such as IVUS and OCT. Staff can make informed decisions, enhancing procedure accuracy and minimizing risk, preventing major adverse cardiac events and patient outcomes. The study aims to help understand how interventional cardiologists and allied healthcare professionals in a cardiac catheterization lab adopt and understand machine learning with it’s imaging applications. By exploring and defining the scope of AI in healthcare, this section provides a foundation for examining perceptions, barriers, and strategies related to AI integration in clinical workflows. Historical Development of Al in Cardiology ""Artificial intelligence" was proposed at a Dartmouth College conference in 1955. It did not enter the healthcare field until the early 1970’s when research produced MYCIN, an Al program that helped identify infection treatments. MYCIN used a type of Al called a rule-based system on predetermined rules to make decisions and solve problems, like a cookbook to a computer. 10 In the field of cardiology, rule-based approaches were a huge success and were used to interpret ECGs, choose the correct treatment, diagnose disease and even to assist physicians." 11 The use of artificial intelligence in electrocardiograms (ECGs) is a direct and efficient method to better assist with the entirety of some medical procedures. Artificial intelligence is set to use objective data to proceed with the output of information it will provide. This objective data comparison allowing for already processed readings can be crucial and time-saving in healthcare, especially cardiology, where seconds may mean everything. 12 AI technologies from the 1970’s laid the foundation and showed their potential in AI in healthcare. Rule-based systems rely on explicitly programming specific commands in a chronological or set manner. Rule-based systems are used today for other applications but have improved and evolved with more advanced AI and machine learning. Al is a collection of technology. The 1980’s-1900’s emerged from machine learning, a subset of Al focusing on algorithms that enable machines to improve other tasks through data. 12 Machine learning allows for a deeper understanding and the ability to expand on set rule-based systems. In healthcare, rule-based systems were first designed to diagnose disease, whereas machine learning can analyze and predict disease. Machine learning has been the foundational development of other AI technologies, especially in healthcare. Deep learning, a more complex form of machine learning involving neural networks, evolved to enhance other domains in AI technologies like Natural Processing(NPL), Predictive Analysis, and Computer Vision. 12 Al is the umbrella term for a wide range of flexible and adaptable technologies and methodologies. The 2000’s significant shift one of the many uses of Al is being able to process a large volume of data, which, in time, has found its application in cardiology for risk prediction, cardiovascular imaging, and electrophysiology. 13 Machine learning can predict the risk of adverse cardiac events following STEMI. Such as ML models as Naive Bayes, k-nearest neighbors (KNN), decision tree, and XBoost.14 13 In electrocardiography, machine learning models have illustrated better how to identify different ECG arrhythmias and ST segment abnormalities. Diagnosis of atrial fibrillation, myocardial infarction, and ventricular tachycardia can also be done through this technology. 15,16 Other applications are wearable devices such as Apple smart watches capable of ECG monitoring and detecting arrhythmias in the general population. 17 Al has been thriving in cardiovascular imaging. Deep learning can assist in analyzing echocardiograms, cardiac computed tomograms (CCT), and MRI. Countries such as India have a shortage of trained medical professionals. AI has become valuable in echocardiographic diagnostics. One Al technology is the automated Heart Model Al software (Philips, Andover, MA), which significantly reduces the time needed for image acquisition and analysis by 82% compared to manual 3D measurements using QLAB. 18 In the 2020s, recent advancements in interventional cardiology have become the leading edge of various technologies in intravascular imaging, hemodynamics, and robotics. 19 One study showed the potential of machine learning to assist decision-making in treating complex coronary disease between percutaneous coronary intervention(PCI) and coronary artery bypass grafting (CABG). As shown in the study, a machine learning study offers better prediction accuracy for five years, mortality, and treatment outcomes than the traditional methods. 20 Another domain is intravascular imaging. Machine learning algorithms automatically measure intravascular ultrasound images' lumen size and plaque burden. (IVUS) and Optical coherence tomography (OCT).21 AI technology is continuously changing and evolving in cardiac catheterization cath labs from rule-based systems to advanced predictive algorithms, and robotic-assisted interventions have set 14 a strong foundation in its application in cardiac catheterization labs. Despite the technological advancements, it is vital to consider the human element that applies these Al tools in cardiac catheterization labs understanding the concerns and challenges faced by interventional cardiologists and allied health professionals. Their perspectives are just as crucial in implementing and optimizing Al tools in the cardiac catheterization lab driving forward. Applications of AI in Cardiology AI in Structural Heart Disease Management Al-assisted Image Fusion and navigation, provides necessary navigation and guidance during structural heart disease. This can provide precise placement of the device without occluding the ostium, optimize catheter movements and accuracy in valve deployment. 22 TrueFusion system eases the structural procedures by adding anatomical or functional landmarks in the display which reduces the fluoroscopy time and radiation. By collaborating their default color volume doppler imaging; 3D TEE and the angiographic images, the soft-tissue and functions of the myocardium are easily identified. This comprehensive method provides various angles from TEE with minimal contrast, less fluoroscopy and accurate device placement. 23 Voice assistant in catheterization provides access to search data or any medical records and also to set voice PIN which eliminates the need for passwords. These technologies have potential to create an impact, enabling the operator to control the equipment in the cath lab. For instance, CardioCube (Amazon Alexa's) is an Al 15 24 software that connects the patient data to the physician. With this method it is possible to implement it in the cath lab to analyze all medical records and also to integrate the data. A study in a small group of patients has demonstrated that it is possible to collect risk factors and past medical history with a high accuracy level of 97.5%. 24 AI in Imaging and Diagnostics Al in intravascular imaging analysis, such as Optical coherence tomography(OCT) and intravascular ultrasound (IVUS). OCT with Al helps by automatically detecting high lipid plaque calcification with high-definition images of the coronary arteries for more precise and accurate stent placement and better outcomes 25 Enhanced Al- OCT systems like Abbot Ulteran 2.0 Software, studies have shown the importance of high-definition images in identifying suboptimal stenting improves patient safety and enhances PCI outcomes. Of 1,002 lesions assessed across 832 patients, successful OCT assessment occurred in 98.2% of cases. Among these lesions, 31% showed suboptimal stent implantation, often leading to major adverse cardiac events (MACE) in the patients.26 IVUS with Al with a personalized data driven approach. Companies like Boston Scientific Study shows that the use of IVUS with Al significantly reduces MACE and ST from Syntax II trials. An increase of 30.2% success rate demonstrated in higher CTO procedure cases. IVUS use in post stent implementation has proven to improve outcomes and patient safety. 27 The use of AI and IVUS may also allow for precise stent placement positioning, as AI will allow for an objective view opposed to a subject view from the attending interventional cardiologist to 16 understand the difference in calcium and the arterial wall. In the cath lab physical Al plays a crucial role in the intervention, where robotic and remote PCI are emerging nowadays. This technology provides better accuracy in the coronary techniques, during stenting and most importantly it reduces the radiation exposure to the cath lab personnels. Remote navigation systems enhance success rates and outcomes, as well as separate 1 mm steps of the wire or balloon catheter enabling precise movement, accurate lesion length measurement and stent deployment. Robotics and Remote Interventions One recent robotic technology, Carindus, Siemens Healthineers Company, developed the CorPath GRX system robotic-assisted control, enhanced by AI.28 PRECISION GRX study, a robotic PCI technology demonstrated a high clinical success. Of 980 patients enrolled across different US, Singapore, and Brazil centers, the study reached a clinical success rate of 97.8% in subjects and 98.1% in robotically treated lesions. Technology allows control of the guide catheter, protecting the operator from radiation exposure. It also showed 86.5% technical success without manually intervening. The robotic system reduces radiation exposure by 95% without the need for lead protection devices. This study changed procedure approaches in intervention cardiology using robotic-assisted PCI and improved patient safety and radiation exposure.29 This technology is also FDA-approved and adopted worldwide. In remote PCI, the set-up will be on the remote spot, where the patient will be in a lab. From the control station (where the console is present), the physician's mobile device is connected through Internet/5G, through the mobile device the procedure is performed with minimal delay in the 17 latency (121 ms).1 Despite the advancement mentioned above, these technologies will also face challenges that have to be addressed for their effective implementation in the Cath lab. Predictive Analytics for Decision-Making Machine learning algorithms based on AI predictive models support transforming decisionmaking to innovation cardiology. The algorithms analyze patient data, including history, imaging results, and laboratory values, to predict outcomes, perform risks assessments, and treatment plans. AI-integration in imaging guided tools (IVUS and OCT) and robotics analyze images of coronary plaque morphology in predicting MACE and better long term outcomes. Additionally, AI tools can help differentiate between PCI and coronary artery bypass grafting with complex multi-vessel support artery disease. The wrist stratification model identifies patients at high risk for adverse events, such as restenosis or stent thrombosis. Management can ensure timely intervention and better long-term outcomes by integrating data into devices, such as electronic health records and other models.30 Challenges and Considerations for AI Adoption in Cardiac Catheterization Labs Cybersecurity and Data Privacy Concerns. Wireless connectivity or cloud-based Al technologies will always face threats and experience cyber security risks. As all the data is transferred wirelessly or through the cloud, it becomes available to unauthorized access, cyberattacks or data breach can happen. In 2021, a huge data breach was reported which involved personal and protected data of more than 1,000,000 individuals. This breach is a hacking incident 18 where unauthorized access gained the healthcare networks and was hacked. A detailed survey 31 in 2022, the HIMSS healthcare service security survey in the health industry, covers workforce trends and standard security practices. The report suggested strengthening cyber security in healthcare. 40 Data Diversity and Bias. Absence of diversity in the data can lead to bias in the outcomes and low data makes low performance (GIGO). Due to a narrow or limited data, it is not possible for an Al software to conclude the decision. In the case of imaging, insufficiency in the wide range of data, the Al will struggle to analyze the characteristics of the lesion type. Therefore, it is crucial to consider adding a variety of data to enhance the performance and outcomes 22 The original survey revealed that 42% of participants identified data quality as a significant barrier to AI adoption, emphasizing the importance of diverse and high-quality inputs.2 Methodological and Algorithmic Bias. Methodological bias is a systematic error in the AI that could impact the results or findings in the cath lab. A complete examination of data is mandatory to ensure and enhance the performance of AI. 32 If a demographic data which is crucial for a research study in the cath lab is not effective, the outcomes would be biased or limited due to the improper collection of data. The original survey showed that 36% of respondents expressed concerns about algorithmic transparency and the potential for bias, further highlighting the need for rigorous algorithm validation. 2 Knowledge and Training Deficit. Another obstacle, aside from the technical aspects of optimizing and implementing Al choice in a cardiac cath lab, is the knowledge deficit and the 19 resistance to change to new technology and procedure approaches. Interventional cardiologists and allied health professionals have different attitudes and perspectives. Healthcare professionals have two different views on Al. There is a side that is optimistic and favors the new technology. "Automating elements of medical practice means clinicians will increasingly have more time to spend with the patient on those tasks where human-delivered care is key."33 The other side consists of caretakers who are threatened by the new technology. "Healthcare professionals may feel that their autonomy and authority is threatened if Al challenges their expertise." 34 The career outlook of an interventional cardiologist involves challenges, especially an emotional one, dealing with procedure complications and adverse outcomes, managing malpractice issues, preventing burnout, and balanced work life. 35 The original survey reported that 73.5% of interventional cardiologists identified a lack of training as a primary barrier, aligning with similar sentiments expressed by allied health professionals. This resistance to change underscores the need for structured training programs that address both technical and emotional concerns. Operational and Workflow Challenges. As AI becomes more collaborative in healthcare, a concern raised by the physicians about their adoption and integration with the Al. Such as robotic assistant(CorPath GRX) control systems enhanced by AI improve patient outcomes and radiation exposure. 28 Despite these benefits, only 21% of respondents in the original survey reported using robotic-assisted AI tools routinely, illustrating the gap between potential and practical adoption.2 20 AI Concerns in Cath Lab Transparency and Trust. Black-box refers to the lack of transparency in how the Al finds out the predictions and conclusions. For instance, an Al recommends a plan but failing to provide an explanation could create uncertainty and a lack of confidence among the cath lab professionals. Algorithm development concerns are an issue that may cause a drawback to the technology. In healthcare, this can be directly seen as a misdiagnosis due to biased ethnic information and that there is a learning of unimportant associations. 37, 38,39 The incorrect output of information may also present the attending physician with a biased answer as well, due to constant trust and reliable answers Al may have presented before. Accountability concerns may become prominent regarding who may be liable for the biased and misdiagnosis. The original survey revealed that 46% of respondents cited the lack of interpretability as a significant challenge. To build trust, future AI systems must prioritize explainability and provide clear rationales for their outputs Ethical and Professional Concerns. AI potential to replace certain clinical tasks raises ethical and professional concerns. For example, as studies comparing AI algorithms to human physicians in medical imaging exceed human performances leading fears of job displacement among healthcare providers.36 The original survey highlighted that 41% of respondents were concerned about job security due to AI adoption. Addressing these fears requires emphasizing AI as a collaborative tool that enhances, rather than replaces, clinical expertise. 21 Summary This chapter has provides a comprehensive overview of AI’s development applications and challenges in interventional cardiologists. From its origins in rule-based systems to advanced prediction models and robotic interventions, AI's role in procedure outcomes is evident. AIassisted imaging, predictive analytics, and remote-to-navigation systems are showing their most significant potential in procedural accuracy and efficiency in cardiac catheterization labs. However, healthcare professionals face considerable challenges in data privacy, algorithmic biases, and knowledge and training deficits. Interventional cardiologists and allied health professionals' perspective shows cautious optimism regarding AI’s potential need for education and training programs. Addressing these barriers will be crucial in successfully adapting AI's full potential. This chapter describes the foundation of understanding how insights can inform future strategies for the success of AI adoption, with the study’s goals of bridging gaps in knowledge and fostering multidisciplinary collaboration. Chapter 3: Research Method Purpose This chapter outlines the methodological framework used to investigate the perceptions and readiness of Cath Lab professionals toward AI adoption. By adapting the survey instrument from the Journal of Invasive Cardiology, this study expands the scope to include allied health professionals, providing a comprehensive understanding of AI integration challenges. 22 Research Design The study employed a cross-sectional survey design using the Qualtrics platform. The survey was adapted from the original study to include questions relevant to both interventional cardiologists and allied health professionals. This design allowed for a broad assessment of knowledge, perceptions, and barriers. This design enabled a comparative analysis of knowledge, perceptions, and barriers to AI adoption across professional roles. Population The population of this study was based around a group of people specializing in the cardiac catheterization lab. The amount of people selected for participation in the results of this study was of 232 people. The population selected is appropriate for this study due to being able to pinpoint the different mental perceptions in this field of interventional cardiology. Samples of the population was carefully selected thoroughly as samples were selected based on the most direct role participants had in the cardiac catheterization laboratory. Sample The sampling method done for this study was a quantitative cross sectional study. Representation was based on niche specific groups in healthcare, being personnel in the cardiac catheterization laboratory. Original data that was collected in the prior study was done on interventional cardiologists, this study differs in the regard as it attempts to understand the perceptions of allied healthcare professional as well. Participants were selected through random stratified sampling methods, as participant selection was based on networked group with the similarity of either 23 interest or engagement in the cardiac catheterization laboratory. Survey Instrument The survey included 18 questions across the following sections: 1. Demographics: Role, age, gender, and years of experience. 2. AI Awareness: Familiarity with AI concepts and self-reported expertise. 3. AI Usage: Current and anticipated use of AI tools, such as robotic assistants and imaging systems. 4. Barriers: Challenges related to training, workflow integration, and data security. 5. Educational Needs: Preferences for workshops, modules, and demonstrations. Data Collection Participants were recruited globally through professional networks, LinkedIn, and social media platforms such as Twitter, Facebook, Twitter, Instagram and specialized forums. The survey was available online for three weeks and collected anonymous responses to ensure confidentiality. 24 Data Analysis Responses were analyzed using descriptive and comparative statistics: 1. Descriptive Statistics: Summarized demographic data, AI awareness, and reported barriers. 2. Comparative Analysis: Assessed differences between interventional cardiologists and allied health professionals regarding their perceptions and adoption of AI. 3. Trend Analysis: Identified patterns in perceptions and adoption timelines. Ethical Considerations The study adhered to strict ethical guidelines: • Informed consent was obtained electronically. • Data was stored securely and used exclusively for research purposes. • The adaptation of the original survey was appropriately credited. Operational Definition of Variables ● Perception of AI: Questions focused on attitudes towards AI in healthcare, the perceived benefits, concerns, and challenges of adopting AI technologies. ● Experience with AI Tools: Questions on direct experience using AI in cardiac procedures, including the types of tools used, frequency, and effectiveness. 25 ● Barriers to AI Adoption: Measured by responses related to training, data privacy, and workflow disruption, job displacement, etc. Assumptions Our proposal assumes that a (a) healthcare professionals/interventional cardiologists in cardiac catheterization labs have a positive attitude towards AI are more likely to adopt its use, enhancing procedural accuracy and patient outcomes. The assumption that cardiologists and allied health professionals will view AI positively is because of a prior study showing supportive impressions among the interventionists. This evidence supports the anticipation of a similar attitude in the current context, mentioning their openness to innovative technologies in interventional practices. (b) Comprehensive education and heightened awareness of AI benefits are critical factors in facilitating its adoption in cardiac catheterization labs. (c) Artificial Intelligence Technology (AIT) can mitigate the knowledge deficit among cardiologists and allied health professionals, promoting a more informed and effective use of AI in cardiac cath lab. This assumption is supported by planning to conduct surveys to gather data from interventional cardiologists and allied health professionals, testing this hypothesis. Limitations Our study acknowledges limitations such as its primary focus on interventional cardiologists and allied health professionals in the United States, which may not fully represent global perspectives or the diversity within the healthcare sector. To mitigate these limitations, the study could potentially expand its demographic reach or include a diverse range of professionals in future research phases. Further limitations include reliability of survey answers, as a verification 26 method ensuring data results is not capable. Mitigation of this is done by presenting survey questionnaires to cardiac catheterization specific group niches. Delimitations Our research is deliberately narrowed to interventional cardiologists and allied health professionals working in cardiac catheterization labs, excluding general cardiologists and other healthcare workers not directly involved with AI tools in cardiac procedures. This focus is chosen to maintain relevance and specificity in findings related to AI integration in cardiac catheterization labs. Ethical Assurances Ethical principles were upheld throughout the study: ● Informed Consent: Participants were provided with detailed information about the study’s purpose, voluntary participation, and anonymity before completing the survey. ● Confidentiality: Data was securely stored and accessible only to the research team. ● Adaptation Acknowledgment: The original survey framework from the Journal of Invasive Cardiology was adapted with appropriate credit, ensuring academic integrity. Summary Our proposal intends to explore AI integration in cardiac catheterization labs, focusing on the perceptions and experiences of interventional cardiologists and allied health professionals. It 27 aims to fill a knowledge gap regarding AI's role in cardiology, anticipating that this research will guide future integration strategies and enhance patient care outcomes. The study’s main principle being of real time current data, with deep analysis of both quantitative and qualitative. Chapter 4: Findings Purpose This chapter presents the findings of the study, focusing on the perceptions and readiness of interventional cardiologists and allied health professionals regarding the integration of artificial intelligence (AI) in cardiac catheterization labs. The results are analyzed within the framework of the adapted survey from the Journal of Invasive Cardiology to compare and expand upon prior findings. Participant Demographics A total of 232 respondents participated in the survey: • Roles: Cardiovascular technologists (34.74%), nurses (36.15%), radiologic technologists (19.08%), and interventional cardiologists (7.98%). • Age: The majority (64.96%) were between 25 and 44 years old. • Gender: Female participants constituted 60.75%, while male participants accounted for 39.25%. • Geographic Distribution: Most respondents (74.16%) were from the United States, with smaller representation from India (8.13%) and other regions (15.71%). This diverse sample provides a comprehensive view of perceptions across multidisciplinary teams in cardiac catheterization labs. 28 Alignment with the Adapted Survey Framework This study adapted the survey, “Interventional Cardiologists’ Perspectives and Knowledge Towards Artificial Intelligence,” to include allied health professionals. The expansion provided broader insights into AI integration challenges and opportunities across all Cath Lab roles. Findings from this study align with the original survey: • AI Awareness: Our Survey: 95% of participants reported being familiar with AI, a higher awareness rate compared to the original study (84.7%). • Optimism: Respondents expressed cautious optimism, with a mean score of 63.02/100 for AI’s potential to improve procedural accuracy and outcomes. • Barriers to AI Adoption: (1) Training requirements were identified as a significant barrier by 73.68% of respondents in your survey, closely matching the original survey (73.5%). (2) A lack of knowledge was reported as another key obstacle by 26.32% of participants Findings by Research Questions 1. Research Question 1: How do Cath Lab professionals perceive AI’s role in improving procedural accuracy Perceived Benefits ● AI tools such as image analysis (e.g., HeartFlow, Ultreon OCT) and ECG analysis were rated highly for their potential to improve procedural accuracy, ● Our Survey: 77.27% identified image analysis tools as high potential and 82.35% emphasized the importance of ECG analysis. 29 Routine Usage ● Despite their potential, these tools are not routinely used, only 28.76% routinely use image analysis tools, indicating a gap between perceived benefits and practical application. 2. Research Question 2: Barriers to AI Adoption In the Cath Lab? Training as a Barrier • Training emerged as the most significant barrier, with 73.68% of respondents agreeing that comprehensive training is necessary for successful AI adoption. • Many respondents expressed concerns about the lack of tailored educational resources, with 34.27% favoring AI demonstrations as the preferred training format. • Concerns about job displacement were relatively low, with 43.26% of respondents not worried and 22.34% believing AI could increase demand for Cath Lab professionals. Concerns about AI integration tools in the existing. Cath Lab was also noted among allied health professionals. 3. Research Question 3: How does AI impact procedural efficiency and patient outcomes? • Most respondents (65.96%) believed AI would increase efficiency, while only 9.33% anticipated a decrease. • However, the practical adoption of advanced tools like robotic assistants remains low, with 89.66% of respondents reporting they do not use them routinely. • This disconnect between perceived benefits and real-world application underscores the need for targeted implementation strategies. 30 4. Research Question 4: Predictions for AI’s Future Role • When asked about the timeline for AI adoption, 53.33% of respondents predicted integration within five years, while 19.33% anticipated it within ten years. • Preferred educational formats included AI demonstrations (45.16%) and workshops (36.77%), reflecting a demand for hands-on, practical training resources. Additional Insights 1. Educational Needs • Respondents emphasized the importance of practical and hands-on training, with 36.77% preferring workshops and 45.16% favoring demonstrations by AI representatives. 2. Perception of AI Tools • Image and ECG analysis tools remained the highest-rated for their potential to improve clinical outcomes, with 85.43% and 88.12%, respectively, recognizing their significant benefits. • In contrast, tools like robotic assistants and virtual/augmented reality were rated lower in immediate potential, with 41.58% and 59.26% of respondents rating them highly, respectively. • These findings indicate that while traditional AI applications are widely accepted, more advanced tools still face skepticism, primarily due to limited hands-on exposure and a lack of comprehensive understanding among Cath Lab professionals. 31 Summary This study confirms the findings of the original survey while expanding its scope to include allied health professionals. Key findings reveal: • High AI Awareness: The majority of respondents demonstrated familiarity with AI but lacked advanced expertise. • Cautious Optimism: Professionals expressed optimism about AI’s potential while highlighting barriers to adoption. • Significant Barriers: Training gaps and workflow integration challenges remain critical obstacles to AI implementation • Underutilized Tools: Despite recognizing the benefits of AI tools, their routine use is limited 32 Chapter 5: Implications, Recommendations, and Conclusions Implications Broadening the Scope of Research: The adaptation of the survey to include allied health professionals revealed that barriers such as training needs and knowledge gaps are not limited to interventional cardiologists but are shared across all roles in the cath lab. Consistency with Previous Studies: The alignment of findings with the original survey reinforces the validity of the results, suggesting that the challenges and opportunities identified are reflective of broader trends in interventional cardiology. Practical Adoption Challenges: While optimism about AI’s potential is high, the limited routine use of tools like robotic assistants highlights the need for practical, user-friendly solutions and better integration into existing workflows. Education and Training as Cornerstones: The overwhelming emphasis on training needs underscores the importance of developing structured educational programs tailored to cath lab professionals. Recommendations 1. Training and Education. Develop comprehensive AI training programs tailored to the roles of interventional cardiologists and allied health professionals. Hands-on workshops and AI 33 demonstrations to build practical skills and confidence in using AI tools. Modules to address the unique needs of allied health professionals, such as cardiovascular technologists and radiologic technologists. 2. Integration Strategies: Conduct pilot programs for AI implementation to assess workflow integration and identify areas for improvement before full-scale adoption. Establish interdisciplinary teams to oversee AI integration, ensuring that both technical and clinical perspectives are considered. Develop standardized protocols for AI tool usage to reduce variability and improve efficiency. 3. Research and Development: Encourage collaborative research between AI developers and healthcare institutions to create tools that address the specific needs of Cath Labs. Conduct longitudinal studies to evaluate the impact of AI on patient outcomes, procedural accuracy, and workforce dynamics over time. Explore the feasibility of real-time feedback mechanisms in AI tools to enhance user trust and understanding. 4. Policy and Regulation: • Advocate for institutional policies that ensure data security and minimize risks associated with cybersecurity breaches. • Develop certification programs for AI tools to ensure quality and reliability in clinical applications. • Promote continuing education credits tied to AI training to incentivize participation among healthcare professionals. 34 Conclusions This study expands upon the original survey by including allied health professionals, offering a comprehensive and multidisciplinary perspective on the adoption of AI in cardiac catheterization labs. Key findings emphasize the universal importance of education and training, the need to address practical barriers, and the value of interdisciplinary collaboration in overcoming AI adoption challenges. High AI awareness but low expertise is evident and the underutilization of AI tools in the cath lab remains due to logistical and educational challenges. 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Accessed December 4, 2024. https://www.ibm.com/think/topics/artificial-intelligence-types 41 Appendices Appendix A: Survey Instrument Survey Title: Cross-Sectional Survey on Artificial Intelligence Adoption in Cardiac Catheterization: Insights from Cath Lab Staff Survey Introduction: This survey was designed to assess the perceptions, awareness, and educational needs of cath lab professionals regarding AI. It was distributed globally to cardiologists, nurses, radiologic technologists, and cardiovascular technologists. Survey Consent Statement: Participants were informed of the following: • The study was approved by Weber State University’s IRB. • Participation was voluntary and anonymous. • Data was securely stored and used exclusively for research purposes. Survey Structure: 1. Demographics: • Role, age group, gender, years of experience, geographic location. 2. AI Awareness and Familiarity: • Levels of familiarity with AI concepts. • Perceived proficiency with AI tools. 42 3. AI Usage: • Current and anticipated use of AI tools (e.g., image analysis, robotic assistants). 4. Barriers and Challenges: • Training needs, data privacy concerns, and workflow integration. 5. Educational Needs: • Preferred formats for AI training (e.g., workshops, clinical guidelines). 6. Perceptions and Optimism: • Perceived impact of AI on efficiency, procedural accuracy, and patient outcomes. Key Survey Questions: 1. What is your role in the Cath Lab? 2. On a scale of 1-5, how familiar are you with AI technologies? 3. What do you perceive as the main barriers to AI adoption? 4. What educational resources would help you incorporate AI into your practice? 43 Appendix B: Recruitment Materials Email Invitation Template: Subject: Help Shape the Future of AI in Cardiology Dear [Recipient], We invite you to participate in our global survey on AI in cardiac catheterization labs. Your input will help shape strategies for integrating AI into interventional cardiology. Key Details: • Time: ~10 minutes • Anonymity guaranteed • Approved by Weber State University’s IRB [Survey Link] https://weber.co1.qualtrics.com/jfe/form/SV_3gQPjQfsK1l5BGe?Q_CHL=social&Q_SocialSour ce=facebook Thank you for contributing to this significant research. Sincerely, [Research Team Contact Information] Recruitment Channels: 44 • Professional networks (LinkedIn, Twitter, Facebook, and Instagram). • Cath lab-specific forums. • Word-of-mouth via participating institutions. 45 Appendix C: Data Collection Overview Participant Demographics: • Total Participants: 232 Roles• Cardiovascular technologists: 34.74% • Nurses: 36.15% • Radiologic technologists: 19.08% • Interventional cardiologists: 7.98% Geographic Distribution• United States: 74.16% • India: 8.13% • Other regions: 15.71% Survey Timeline: • Launch Date: November 1, 2024 • Close Date: November 28, 2024 46 Appendix D: Results Summary AI Awareness: • 95% of respondents were familiar with AI, with 26% confident in their ability to explain AI concepts. Barriers Identified: 1. Training requirements: 73.68% of respondents. 2. Concerns about data privacy: 28%. 3. Lack of workflow integration: 34%. Perceptions of AI Impact: • 65.96% believed AI would increase their efficiency. • 53.33% anticipated widespread adoption within five years. Preferred Educational Resources: • AI demonstrations: 45.16% • Workshops and courses: 36.77% 47 Appendix E: IRB Approval and Ethical Compliance IRB Approval Statement: Approval was obtained from Weber State University’s IRB, ensuring adherence to ethical research standards. Participants provided informed consent electronically. Confidentiality Measures: • All responses were anonymized. • Data was stored in password-protected electronic formats. 48 Appendix F: Tables and Graphs Table 1: Role Distribution of Participants Role Percentage Cardiovascular Technologist 34.74% Nurse 36.15% Radiologic Technologist 19.08% Interventional Cardiologist 7.98% Graph Example: Optimism About AI Adoption Bar graph showing optimism levels across roles, showing higher optimism among technologists and nurses. 49 Appendix G: Glossary of Terms ● Artificial Intelligence (AI): Machine-based systems performing tasks requiring human intelligence. ● Cath Lab: Cardiac Catheterization Laboratory for minimally invasive procedures. ● IVUS (Intravascular Ultrasound): An imaging technology used in cardiac procedures. ● Interventional Cardiologist: A cardiologist specializing in catheter-based treatment of heart diseases. ● Qualtrics: A survey platform used to collect and analyze quantitative and qualitative data. ● Allied Health Professional: Healthcare professionals in supporting roles, such as nurses, technicians, and technologists. ● AI-assisted Imaging: The use of AI to enhance medical imaging techniques such as OCT (Optical Coherence Tomography) and IVUS (Intravascular Ultrasound). 50 Appendix H: Sample Raw Data Key Insights from Survey Analysis: 1. Demographics: • Roles: Nurses (36.15%), Cardiovascular Technologists (34.74%), Radiologic Technologists (18.31%), Interventional Cardiologists (7.98%). • Region: United States dominated participation (74.16%), followed by India (8.13%) and other regions (15.71%). • Age Group: Most participants were aged 25–44 (64.96%). 2. AI Awareness: • 95% of respondents were familiar with AI, but only 3.86% considered themselves experts. • 26.09% felt confident explaining AI concepts, while 53.62% had a general understanding. 3. AI Usage and Potential: • Image Analysis Tools: 22.99% used routinely; 45.10% reported occasional use. • Optimism: Average optimism score for AI adoption: 60.29/100. • Anticipated Timeline for Adoption: 51 • 51.06% anticipated adoption within five years. • 10.64% already had AI implemented and used in clinical practice. 4. Educational Needs: • Preferred resources: AI demonstrations (45.16%), workshops (36.77%), clinical guidelines (10.32%). 5. Barriers to AI Adoption: • Training requirements: 73.68%. • Lack of workflow integration: 34%. • Concerns about data privacy: 28%.