Dataset Information

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

ABSTRACT: Imaging and cardiology are the healthcare domains which have seen the greatest number of FDA approvals for novel data-driven technologies, such as artificial intelligence, in recent years. The increasing use of such data-driven technologies in healthcare is presenting a series of important challenges to healthcare practitioners, policymakers, and patients. In this paper, we review ten ethical, social, and political challenges raised by these technologies. These range from relatively pragmatic concerns about data acquisition to potentially more abstract issues around how these technologies will impact the relationships between practitioners and their patients, and between healthcare providers themselves. We describe what is being done in the United Kingdom to identify the principles that should guide AI development for health applications, as well as more recent efforts to convert adherence to these principles into more practical policy. We also consider the approaches being taken by healthcare organizations and regulators in the European Union, the United States, and other countries. Finally, we discuss ways by which researchers and frontline clinicians, in cardiac imaging and more broadly, can ensure that these technologies are acceptable to their patients.

SUBMITTER: Fenech ME

PROVIDER: S-EPMC7174604 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Fenech Matthew E ME Buston Olly O

Frontiers in cardiovascular medicine 20200415

Imaging and cardiology are the healthcare domains which have seen the greatest number of FDA approvals for novel data-driven technologies, such as artificial intelligence, in recent years. The increasing use of such data-driven technologies in healthcare is presenting a series of important challenges to healthcare practitioners, policymakers, and patients. In this paper, we review ten ethical, social, and political challenges raised by these technologies. These range from relatively pragmatic co ...[more]

PMID: 32351974

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Project description:BackgroundBiomarker research is gaining increasing attention focusing on the preclinical stages of the disease. Such interest requires special attention for communication and disclosure in clinical contexts. Many countries give dementia a high health policy priority by developing national strategies and by improving guidelines addressing disclosure of a diagnosis; however, risk communication is often neglected.Main textThis paper aims to identify the challenges of disclosure in the context of dementia prediction and to find out whether existing clinical guidelines sufficiently address the issues of disclosing a dementia diagnosis and of disclosing the risk of developing dementia in asymptomatic and MCI stage. We will examine clinical guidelines and recommendations of three countries (USA, Canada and Germany) regarding predictive testing and diagnostic disclosure in dementia and Mild Cognitive Impairment (MCI) to show their potentials and limits. This will provide a background to address ethical implications of predictive information and to identify ways how to proceed further. We will start by examining the guidelines and recommendations by focusing on what there is already and what is missing regarding the challenges of disclosing dementia prediction and MCI. Then, we will highlight the novel ethical issues generated by the shift to identify preclinical stages of the disease by biomarkers. We will argue for the need to develop guidelines for disclosing a risk status, which requires different considerations then disclosing a diagnosis of dementia. Finally, we will make some suggestions on how to address the gap and challenges raised by referring to German Stakeholder Conference, which presents us a good starting point to the applicability of involving stakeholders.ConclusionsThis paper underlines the need to develop empirically based guidelines that address the ethical and social strategies for risk communication of dementia prediction by genetic as well as non-genetic biomarkers. According to our analysis, the guidelines do not address the new developments sufficiently. International efforts should aim for specific guidelines on counseling, communicating risk and disclosing results. We argue that guidelines on (risk) disclosure should be developed by involving various stakeholders and should be informed by socio-empirical studies involving laypersons' needs and wishes regarding risk communication.

Project description: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. 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. 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. 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.

Dataset Information

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

Publications

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

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