The New European Medical Device Regulation: Balancing Innovation and Patient Safety.

The European Union has introduced stricter provisions for medical devices under the new Medical Device Regulation (MDR). The MDR increases requirements for clinical trial testing for many devices before they can legally be placed on the market and extends requirements for rigorous clinical surveillance of benefits and harms to the entire life cycle of devices. New "expert panels" have been established by the European Commission to advise in the assessment of devices toward certification, and the role of previous "notified bodies" (private companies charged by the Commission with ensuring that manufacturers follow the requirements for device testing) is being expanded. The MDR does not contain a grandfathering clause; thus, all existing medical devices must be recertified under the stricter regulation. The recertification deadline has recently been extended to 2027 or 2028, depending on the device's risk class. Whether most device manufacturers can meet these new requirements is uncertain, and the MDR will likely have important consequences for manufacturers, researchers, clinicians, and patients. Enhanced collaborations between the medical device industry and physician partners will be needed to meet the new requirements in a timely manner to avoid shortages of existing devices and to mitigate barriers to development of new devices.

The Regulation of Mitochondrial Replacement Techniques Around the World.

Mitochondrial replacement techniques (MRTs, also referred to as mitochondrial replacement therapies) have given hope to many women who wish to have genetically related children but have mitochondrial DNA mutations in their eggs. MRTs have also spurred deep ethical disagreements and led to different regulatory approaches worldwide. In this review, we discuss the current regulation of MRTs across several countries. After discussing the basics of the science, we describe the current law and policy directions in seven countries: the United Kingdom, the United States, Canada, Australia, Germany, Israel, and Singapore. We also discuss the emerging phenomenon of medical tourism (also called medical travel) for MRTs to places like Greece, Spain, Mexico, and Ukraine. We then pull out some key findings regarding similarities and differences in regulatory approaches around the world.

Artificial intelligence tools in clinical neuroradiology: essential medico-legal aspects.

Commercial software based on artificial intelligence (AI) is entering clinical practice in neuroradiology. Consequently, medico-legal aspects of using Software as a Medical Device (SaMD) become increasingly important. These medico-legal issues warrant an interdisciplinary approach and may affect the way we work in daily practice. In this article, we seek to address three major topics: medical malpractice liability, regulation of AI-based medical devices, and privacy protection in shared medical imaging data, thereby focusing on the legal frameworks of the European Union and the USA. As many of the presented concepts are very complex and, in part, remain yet unsolved, this article is not meant to be comprehensive but rather thought-provoking. The goal is to engage clinical neuroradiologists in the debate and equip them to actively shape these topics in the future.

"I'd feel like someone was watchin' me watching for a good reason": perceptions of data privacy, access, and sharing in the context of real-time PrEP adherence monitoring among HIV-negative MSM with substance use.

Once-daily oral tenofovir/emtricitabine is highly effective as pre-exposure prophylaxis (PrEP) against HIV but is dependent on adherence, which may be challenging for men who have sex with men (MSM) and use substances. Digital pill systems (DPS) permit the direct, real-time measurement of adherence, though user perceptions of data privacy in this context are unknown. Thirty prospective DPS users - HIV-negative MSM with non-alcohol substance use - completed in-depth qualitative interviews exploring preferences around privacy, access, and sharing of DPS adherence data. Participants discussed some concerns about the impact of DPS use on personal privacy, and emphasized the need for robust data protections in the technology. Participants were interested in having on-demand access to their adherence data, and were most willing to share data with primary care providers and long-term relationship partners. Future investigations exploring bioethical frameworks around DPS use are warranted, and user preferences should inform best practices for protecting DPS data.

Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.

Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability. While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence. Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no-fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care.

The need for health AI ethics in medical school education.

Health Artificial Intelligence (AI) has the potential to improve health care, but at the same time, raises many ethical challenges. Within the field of health AI ethics, the solutions to the questions posed by ethical issues such as informed consent, bias, safety, transparency, patient privacy, and allocation are complex and difficult to navigate. The increasing amount of data, market forces, and changing landscape of health care suggest that medical students may be faced with a workplace in which understanding how to safely and effectively interact with health AIs will be essential. Here we argue that there is a need to teach health AI ethics in medical schools. Real events in health AI already pose ethical challenges to the medical community. We discuss key ethical issues requiring medical school education and suggest that case studies based on recent real-life examples are useful tools to teach the ethical issues raised by health AIs.

Ethical and Legal Implications of Remote Monitoring of Medical Devices.

Policy Points Millions of life-sustaining implantable devices collect and relay massive amounts of digital health data, increasingly by using user-downloaded smartphone applications to facilitate data relay to clinicians via manufacturer servers. Our analysis of health privacy laws indicates that most US patients may have little access to their own digital health data in the United States under the Health Insurance Portability and Accountability Act Privacy Rule, whereas the EU General Data Protection Regulation and the California Consumer Privacy Act grant greater access to device-collected data. Our normative analysis argues for consistently granting patients access to the raw data collected by their implantable devices. CONTEXT: Millions of life-sustaining implantable devices collect and relay massive amounts of digital health data, increasingly by using user-downloaded smartphone applications to facilitate data relay to clinicians via manufacturer servers. Whether patients have either legal or normative claims to data collected by these devices, particularly in the raw, granular format beyond that summarized in their medical records, remains incompletely explored. METHODS: Using pacemakers and implantable cardioverter-defibrillators (ICDs) as a clinical model, we outline the clinical ecosystem of data collection, relay, retrieval, and documentation. We consider the legal implications of US and European privacy regulations for patient access to either summary or raw device data. Lastly, we evaluate ethical arguments for or against providing patients access to data beyond the summaries presented in medical records. FINDINGS: Our analysis of applicable health privacy laws indicates that US patients may have little access to their raw data collected and held by device manufacturers in the United States under the Health Insurance Portability and Accountability Act Privacy Rule, whereas the EU General Data Protection Regulation (GDPR) grants greater access to device-collected data when the processing of personal data falls under the GDPR's territorial scope. The California Consumer Privacy Act, the "little sister" of the GDPR, also grants greater rights to California residents. By contrast, our normative analysis argues for consistently granting patients access to the raw data collected by their implantable devices. Smartphone applications are increasingly involved in the collection, relay, retrieval, and documentation of these data. Therefore, we argue that smartphone user agreements are an emerging but potentially underutilized opportunity for clarifying both legal and ethical claims for device-derived data. CONCLUSIONS: Current health privacy legislation incompletely supports patients' normative claims for access to digital health data.

Health Care AI and Patient Privacy-Dinerstein v Google.

This Viewpoint summarizes a recent lawsuit alleging that a hospital violated patients' privacy by sharing electronic health record (EHR) data with Google for development of medical artificial intelligence (AI) and discusses how the federal court's decision in the case provides key insights for hospitals planning to share EHR data with for-profit companies developing medical AI.

Generative AI in Health Care and Liability Risks for Physicians and Safety Concerns for Patients.

This Viewpoint discusses the potential use of generative artificial intelligence (AI) in medical care and the liability risks for physicians using the technology, as well as offers suggestions for safeguards to protect patients.

Prevention of Bias and Discrimination in Clinical Practice Algorithms.

This Viewpoint discusses a proposed DHHS rule to address discrimination in clinical algorithms and the need for additional considerations to ensure the burden of liability for biased algorithms is not disproportionately placed on health care professionals.

The proposed EU Directives for AI liability leave worrying gaps likely to impact medical AI.

Two newly proposed Directives impact liability for artificial intelligence in the EU: a Product Liability Directive (PLD) and an AI Liability Directive (AILD). While these proposed Directives provide some uniform liability rules for AI-caused harm, they fail to fully accomplish the EU's goal of providing clarity and uniformity for liability for injuries caused by AI-driven goods and services. Instead, the Directives leave potential liability gaps for injuries caused by some black-box medical AI systems, which use opaque and complex reasoning to provide medical decisions and/or recommendations. Patients may not be able to successfully sue manufacturers or healthcare providers for some injuries caused by these black-box medical AI systems under either EU Member States' strict or fault-based liability laws. Since the proposed Directives fail to address these potential liability gaps, manufacturers and healthcare providers may have difficulty predicting liability risks associated with creating and/or using some potentially beneficial black-box medical AI systems.

How AI can learn from the law: putting humans in the loop only on appeal.

While the literature on putting a "human in the loop" in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an initial AI/ML judgment has been made. Based on an analogy with appellate processes in judiciary decision-making, we argue that this is, in many respects, a more efficient way to divide the labor between a human and a machine. Human reviewers can add more nuanced clinical, moral, or legal reasoning, and they can consider case-specific information that is not easily quantified and, as such, not available to the AI/ML at an initial stage. In doing so, the human can serve as a crucial error correction check on the AI/ML, while retaining much of the efficiency of AI/ML's use in the decision-making process. In this paper, we develop these widely applicable arguments while focusing primarily on examples from the use of AI/ML in medicine, including organ allocation, fertility care, and hospital readmission.

Ethical perspectives on surgical video recording for patients, surgeons and society: systematic review.

BACKGROUND: Operating-room audiovisual recording is increasingly proposed, although its ethical implications need elucidation. The aim of this systematic review was to examine the published literature on ethical aspects regarding operating-room recording. METHODS: MEDLINE (via PubMed), Embase, and Cochrane databases were systematically searched for articles describing ethical aspects regarding surgical (both intracorporeal and operating room) recording from database inception to the present (the last search was undertaken in July 2022). Medical subject headings used in the search included 'operating room', 'surgery', 'video recording', 'black box', 'ethics', 'consent', 'confidentiality', 'privacy', and more. Title, abstract, and full-text screening determined relevance. The quality of studies was assessed using Centre for Evidence-Based Medicine grading and no formal assessment of risk of bias was attempted given the theoretical nature of the data collected. RESULTS: From 1048 citations, 22 publications met the inclusion criteria, with three more added from their references. There was evident geographical (21 were from North America/Europe) and recency (all published since 2010) bias and an exclusive patient/clinician perspective (25 of 25). The varied methodology (including ten descriptive reviews, seven opinion pieces, five surveys, two case reports, and one RCT) and evidence level (14 level V and 10 level III/IV) prevented meaningful systematic grading/meta-analysis. Publications were narratively analysed for ethical thematic content (mainly education, performance, privacy, consent, and ownership) that was then grouped by the four principles of biomedical ethics of Beauchamp and Childress, accounting for 63 distinct considerations concerning beneficence (22 of 63; 35 per cent), non-maleficence (17 of 63; 27 per cent), justice (14 of 63; 22 per cent), and autonomy (10 of 63; 16 per cent). From this, a set of proposed guidelines on the use of operative data is presented. CONCLUSION: For a surgical video to be a truly valuable resource, its potential benefits must be more fully weighed against its potential disadvantages, so that any derived instruments have a solid ethical foundation. Universal, ethical, best-practice guidelines are needed to protect clinicians, patients, and society.