Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI?
Open accessAbstract
As capabilities of predictive algorithms improve, machine learning will become an important element of physician practice and patient care.Implementation of artificial intelligence (AI) raises complex legal questions regarding health care professionals' and technology manufacturers' liability, particularly if they cannot explain recommendations generated by AI technology.The limited literature on liability for innovation provides opportunities to consider possible implications of AI for medical malpractice and products liability and new legal solutions for addressing liability issues surrounding "black-box" medicine. Liability When Patients Are Injured Through New TechnologiesArtificial intelligence (AI) is widely employed in health care, with a recent report showing that 86% of provider organizations, technology vendors, and life science companies use some form of AI. 1 AI can be broadly defined as machine intelligence that "performs tasks that normally require human intelligence" 2 or "that work[s] to achieve goals." 3 Among the most compelling applications of AI is the use of predictive algorithms in precision medicine.Algorithms in precision medicine guide care by predicting patient risks, making accurate diagnoses, selecting drugs, and even prioritizing patients to preserve or assign limited health resources.4 Significantly, the mechanisms behind such recommendations are unknown and currently undiscoverable; an algorithm that cannot demonstrate the path to its conclusion is ultimately a black box.5,6 The unknowable reasoning of "blackbox" AI, often referred to as its opacity, stems from "deep neural networks," with their "reasoning … embedded in the behavior of thousands of simulated neurons, arranged into dozens or even hundreds of intricately interconnected layers." 5 When provided with input data, for example, such as an MRI brain scan, a neural network trained on a large data set can find a "complex underlying pattern in the data" 7 and produce an output, such as a tumor classification, but is incapable of explaining the reasoning that led to its conclusion.[7][8][9] Modeled after the human brain, the neural network also learns in similar ways, including through self-teaching.When given additional data, the neural network can modify its decision-making process for a more accurate response, without any explanation of how it has done so.Becoming more autonomous with each improvement,
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Studies on physician attitudes toward AI have found that clear explanations and the ability to veto or override AI suggestions are important for acceptance.
“Studies on physician attitudes toward AI have found that clear explanations and the ability to veto or override AI suggestions are important for acceptance [59,60].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [59]
The Abstract discusses the difficulty of explaining black-box AI recommendations in the context of liability, not physician acceptance. The section “Modify the standard of care” discusses clinicians evaluating AI results. Searches for physician attitudes, acceptance, and the ability to veto or override AI suggestions found no passage addressing the claimed findings; page numbers were not available in our copy. (LLM, conf=0.73)
Experimental — accuracy not yet measuredRegulators expect evidence that MASs have been tested for equitable performance across diverse patient groups.
“Regulators expect evidence that MASs have been tested for equitable performance across diverse patient groups [59].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [59]
The “Modify the standard of care” section discusses clinicians’ duty to evaluate black-box algorithms and validate their results. The copy does not address regulators expecting evidence of equitable performance across diverse patient groups; searches for that phrase and for bias testing across patient populations found no such passage. Page numbers were not available. (LLM, conf=0.72)
Experimental — accuracy not yet measuredCommon ethical concerns about the use of AI in medicine include lack of transparency, insufficient knowledge about the application used, or false/misleading results.
“Common ethical concerns about the use of AI in medicine include lack of transparency, insufficient knowledge about the application used, or false/misleading results [15, 16].”
From Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education (2023) · cites this paper as [16]
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“Regulators expect evidence that MASs have been tested for equitable performance across diverse patient groups [59].”
- Authors
- Sullivan, H. R., & Schweikart, S. J.
- Affiliations
- American Medical Association (US)
- ORCID
- Hannah R. Sullivan
- Venue
- AMA Journal of Ethics
- Publisher
- American Medical Association (AMA)
- Published
- 2019-02-01
- Volume
- 21
- Issue
- 2
- Pages
- E160-166
- ISSN
- 1937-7010 (ISSN-L)
- 2376-6980
- OpenAlex
- W2921072211
- Cited by
- 188
- References
- 10
- Citations by year
- 2026: 29 · 2025: 20 · 2024: 26 · 2023: 32 · 2022: 24 · 2021: 29 · 2020: 17 · 2019: 11
- FWCI
- 37.38
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The AMA Journal of Ethic (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Pharmacy › Medical Malpractice and Liability Issues
- Social Sciences › Law › Digitalization, Law, and Regulation
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in healthcare, medical malpractice, product liability, tort liability, algorithmic opacity, deep neural network, predictive algorithms, precision medicine
- MeSH terms
- Artificial Intelligence, Diagnosis, Computer-Assisted, Humans, Malpractice, United States, Liability, Legal, Robotic Surgical Procedures
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