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

ABSTRACT: 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.

SUBMITTER: Duffourc MN

PROVIDER: S-EPMC10133336 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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

Duffourc Mindy Nunez MN Gerke Sara S

NPJ digital medicine 20230426 1

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 ...[more]

PMID: 37100860

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Project description:INTRODUCTION: The liability crisis may affect residency graduates' practice decisions, yet structured liability education during residency is still inadequate. The objective of this study was to determine the influence of medical liability on practice decisions and to evaluate the adequacy of current medical liability curricula. METHODS: All fourth-year residents (n = 1274) in 264 Accreditation Council for Graduate Medical Education-accredited allopathic and 25 osteopathic US obstetrics and gynecology residency training programs were asked to participate in a survey about postgraduate plans and formal education during residency regarding liability issues in 2006. Programs were identified by the Council on Resident Education in Obstetrics and Gynecology directory and the American College of Osteopathic Obstetricians and Gynecologists residency program registry. Outcome measures were the reported influence of liability/malpractice concerns on postresidency practice decision making and the incidence of formal education in liability/malpractice issues during residency. RESULTS: A total of 506 of 1274 respondents (39.7%) returned surveys. Women were more likely than men to report "region of the country" (P = .02) and "paid malpractice insurance as a salaried employee" (P = .03) as a major influence. Of the respondents, 123 (24.3%) planned fellowship training, and 229 (45.3%) were considering limiting practice. More than 20% had been named in a lawsuit. Respondents cited Pennsylvania, Florida, and New York as locations to avoid. In response to questions about medical liability education, 54.3% reported formal education on risk management, and 65.2% indicated they had not received training on "next steps" after a lawsuit. DISCUSSION: Residents identify liability-related issues as major influences when making choices about practice after training. Structured education on matters of medical liability during residency is still inadequate.

Project description:ObjectiveTo identify lessons learned from the experience of the Agency for Healthcare Research and Quality (AHRQ) Patient Safety and Medical Liability (PSML) Demonstration Program.Data sources/study settingOn September 9, 2009, President Obama directed the Secretary of Health and Human Services to authorize demonstration projects that put "patient safety first" with the intent of reducing preventable adverse outcomes and stemming liability costs. Seven demonstration projects received 3 years of funding from AHRQ in the summer of 2010, and the program formally came to a close in June 2015.Study designThe seven grantees implemented complex, broad-ranging innovations addressing both patient safety and medical liability in "real-world" contexts. Some projects featured novel approaches, while others implemented adaptations of existing models. Each project was funded by AHRQ to collect data on the impact of its interventions. In addition, AHRQ funded a cross-cutting qualitative evaluation focused on lessons learned in implementing PSML interventions.Data collection/extraction methodsSite visits and follow-up interviews supplemented with material abstracted from formal project reports to AHRQ.Principal findingsThe PSML demonstration projects focused on three broad approaches: (1) improving communication around adverse events through disclosure and resolution programs; (2) preventing harm through implementation of clinical "best practices"; and (3) exploring alternative methods of settling claims. Although the demonstration contributed to accumulating evidence that these kinds of interventions can positively affect outcomes, there is also evidence to suggest that these interventions can be difficult to scale.ConclusionsIn addition to producing at least preliminary positive outcomes, the demonstration also lends credence to the idea that targeted interventions that improve some aspect of patient safety or malpractice performance may also contribute more broadly to institutional culture and the alignment of all parties around reducing risk and preventing harm. However, more empirical work needs to be carried out to quantify the effect of such interventions.

Dataset Information

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

Publications

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

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