AI in Cardiac Imaging: A UK-Based Perspective on Addressing the Ethical, Social, and Political Challenges

Trustworthy and ethical AI-enabled cardiovascular care: a rapid review

BACKGROUND

Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist among the public and medical community. Given the rapid and transformative recent growth of AI in cardiovascular care, to inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients' and healthcare providers' perspectives when using AI in cardiovascular care.

METHODS

In this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI-based medical devices (interventions of interest) in the context of cardiovascular care from patients', caregivers', or healthcare providers' perspectives. The search was completed on May 24, 2022 and was not limited by date or study design.

RESULTS

After reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues (n = 59, 40.7%); risk of healthcare inequity or disparity (n = 36, 24.8%); risk of patient harm (n = 24, 16.6%); accountability and responsibility concerns (n = 19, 13.1%); problematic informed consent and potential loss of patient autonomy (n = 17, 11.7%); and issues related to data ownership (n = 11, 7.6%). Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients' interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights.

CONCLUSION

This review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients' and healthcare providers' perspectives. Successful integration of AI into cardiovascular care necessitates implementation of mitigation strategies. These strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.

Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.

Evolutionary trends and innovations in cardiovascular intervention

Cardiovascular diseases remain a global health challenge, prompting continuous innovation in medical technology, particularly in Cardiovascular MedTech. This article provides a comprehensive exploration of the transformative landscape of Cardiovascular MedTech in the 21st century, focusing on interventions. The escalating prevalence of cardiovascular diseases and the demand for personalized care drive the evolving landscape, with technologies like wearables and AI reshaping patient-centric healthcare. Wearable devices offer real-time monitoring, enhancing procedural precision and patient outcomes. AI facilitates risk assessment and personalized treatment strategies, revolutionizing intervention precision. Minimally invasive procedures, aided by robotics and novel materials, minimize patient impact and improve outcomes. 3D printing enables patient-specific implants, while regenerative medicine promises cardiac regeneration. Augmented reality headsets empower surgeons during procedures, enhancing precision and awareness. Novel materials and radiation reduction techniques further optimize interventions, prioritizing patient safety. Data security measures ensure patient privacy in the era of connected healthcare. Modern technologies enhance traditional surgeries, refining outcomes. The integration of these innovations promises to shape a healthier future for cardiovascular procedures, emphasizing collaboration and research to maximize their transformative potential.

The Intersection of ChatGPT, Clinical Medicine, and Medical Education

As we progress deeper into the digital age, the robust development and application of advanced artificial intelligence (AI) technology, specifically generative language models like ChatGPT (OpenAI), have potential implications in all sectors including medicine. This viewpoint article aims to present the authors' perspective on the integration of AI models such as ChatGPT in clinical medicine and medical education. The unprecedented capacity of ChatGPT to generate human-like responses, refined through Reinforcement Learning with Human Feedback, could significantly reshape the pedagogical methodologies within medical education. Through a comprehensive review and the authors' personal experiences, this viewpoint article elucidates the pros, cons, and ethical considerations of using ChatGPT within clinical medicine and notably, its implications for medical education. This exploration is crucial in a transformative era where AI could potentially augment human capability in the process of knowledge creation and dissemination, potentially revolutionizing medical education and clinical practice. The importance of maintaining academic integrity and professional standards is highlighted. The relevance of establishing clear guidelines for the responsible and ethical use of AI technologies in clinical medicine and medical education is also emphasized.

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients' lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.

Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging

Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.

Six Drivers to Face the XXI Century Challenges and Build the New Healthcare System: "La Salute in Movimento" Manifesto

The aging of the population, the burden of chronic diseases, possible new pandemics are among the challenges for healthcare in the XXI century. To face them, technological innovations and the national recovery and resilience plan within the European Union can represent opportunities to implement changes and renovate the current healthcare system in Italy, in an effort to guarantee equal access to health services. Considering such scenario, a panel of Italian experts gathered in a multidisciplinary Think Tank to discuss possible design of concepts at the basis of a new healthcare system. These ideas were summarized in a manifesto with six drivers for change: vision, governance, competence, intelligence, humanity and relationship. Each driver was linked to an action to actively move toward a new healthcare system based on trust between science, citizens and institutions.

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

What Makes Artificial Intelligence Exceptional in Health Technology Assessment?

The application of artificial intelligence (AI) may revolutionize the healthcare system, leading to enhance efficiency by automatizing routine tasks and decreasing health-related costs, broadening access to healthcare delivery, targeting more precisely patient needs, and assisting clinicians in their decision-making. For these benefits to materialize, governments and health authorities must regulate AI, and conduct appropriate health technology assessment (HTA). Many authors have highlighted that AI health technologies (AIHT) challenge traditional evaluation and regulatory processes. To inform and support HTA organizations and regulators in adapting their processes to AIHTs, we conducted a systematic review of the literature on the challenges posed by AIHTs in HTA and health regulation. Our research question was: What makes artificial intelligence exceptional in HTA? The current body of literature appears to portray AIHTs as being exceptional to HTA. This exceptionalism is expressed along 5 dimensions: 1) AIHT's distinctive features; 2) their systemic impacts on health care and the health sector; 3) the increased expectations towards AI in health; 4) the new ethical, social and legal challenges that arise from deploying AI in the health sector; and 5) the new evaluative constraints that AI poses to HTA. Thus, AIHTs are perceived as exceptional because of their technological characteristics and potential impacts on society at large. As AI implementation by governments and health organizations carries risks of generating new, and amplifying existing, challenges, there are strong arguments for taking into consideration the exceptional aspects of AIHTs, especially as their impacts on the healthcare system will be far greater than that of drugs and medical devices. As AIHTs begin to be increasingly introduced into the health care sector, there is a window of opportunity for HTA agencies and scholars to consider AIHTs' exceptionalism and to work towards only deploying clinically, economically, socially acceptable AIHTs in the health care system.

Integration of artificial intelligence into clinical patient management: focus on cardiac imaging

Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve-through automation, standardization and data integration-all components of the clinical workflow. This review aims to present a comprehensive view of full integration of artificial intelligence into the standard clinical patient management-with a focus on cardiac imaging, but applicable to all information handling-and to discuss current barriers that remain to be overcome before its widespread implementation and integration.