Potential Liability for Physicians Using Artificial Intelligence

Price, W. Nicholson; Gerke, Sara; Cohen, I. Glenn

doi:10.1001/jama.2019.15064

Cited by 291 publications

(202 citation statements)

References 5 publications

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“…including China [25,[45][46][47], and also concerns the members of French regulatory agencies who clearly want to be able to evaluate AI software before going forward. In the US, from the physician's perspective, this issue could even reduce the level of medical innovation if "the "safest" way to use medical AI from a liability perspective is as a confirmatory tool to support existing decision-making processes, rather than as a source of ways to improve care" [48]. For individuals without a conflict of interest, the main concern is to protect the population, for example, by creating a victim compensation fund if necessary.…”

Section: A Strong Need To Define the Responsibilities Of Each Stakehomentioning

confidence: 99%

Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France

2020

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Background: Artificial intelligence (AI), with its seemingly limitless power, holds the promise to truly revolutionize patient healthcare. However, the discourse carried out in public does not always correlate with the actual impact. Thus, we aimed to obtain both an overview of how French health professionals perceive the arrival of AI in daily practice and the perception of the other actors involved in AI to have an overall understanding of this issue. Methods: Forty French stakeholders with diverse backgrounds were interviewed in Paris between October 2017 and June 2018 and their contributions analyzed using the grounded theory method (GTM). Results: The interviews showed that the various actors involved all see AI as a myth to be debunked. However, their views differed. French healthcare professionals, who are strategically placed in the adoption of AI tools, were focused on providing the best and safest care for their patients. Contrary to popular belief, they are not always seeing the use of these tools in their practice. For healthcare industrial partners, AI is a true breakthrough but legal difficulties to access individual health data could hamper its development. Institutional players are aware that they will have to play a significant role concerning the regulation of the use of these tools. From an external point of view, individuals without a conflict of interest have significant concerns about the sustainability of the balance between health, social justice, and freedom. Health researchers specialized in AI have a more pragmatic point of view and hope for a better transition from research to practice. Conclusion: Although some hyperbole has taken over the discourse on AI in healthcare, diverse opinions and points of view have emerged among French stakeholders. The development of AI tools in healthcare will be satisfactory for everyone only by initiating a collaborative effort between all those involved. It is thus time to also consider the opinion of patients and, together, address the remaining questions, such as that of responsibility.

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Section: A Strong Need To Define the Responsibilities Of Each Stakehomentioning

confidence: 99%

Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France

2020

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“…We note in particular a guide to reading the literature [10], an accompanying editorial [11], and a viewpoint review [12] of the National Academy of Medicine's comprehensive exploration of AI in healthcare [13]. Possible biases in the design and development of AI systems in conjunction with EHRs have also been explored [14], as has their remediation [15] and the potential legal liability risk for a provider using AI [16]. Considering the influential regulatory framework in the US on Software as a Medical Device, how should the lifecycle of an AI system be viewed, especially if it is adaptive and-at least in theory-self-improving [17]?…”

Section: Resultsmentioning

confidence: 99%

Review of Clinical Research Informatics

Solomonides

2020

Yearb Med Inform

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Objectives: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports—and sometimes initiates—and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. Methods: Beyond personal awareness of a range of work commensurate with the author’s own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns (“artificial intelligence”, “data models”, “analytics”, etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. Results: The substantive sections of the paper—Artificial Intelligence, Machine Learning, and “Big Data” Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence—provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer’s interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. Conclusions: CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

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“…Currently, physicians are protected as long as they follow ''standard of care,'' but as ML becomes more accurate it may become the ''standard of care'' over previous practices. According to Price et al [108], the safest way to use ML is to use it only as a confirmatory tool to support existing decisionmaking processes and to check with individual malpractice insurers. Physicians are likely to influence how ML is used in practice and when it should be applied in place of human decision.…”

Section: Regulation and Liability In Machine Learningmentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

166

108

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Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

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Potential Liability for Physicians Using Artificial Intelligence

Cited by 291 publications

References 5 publications

Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France

Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France

Review of Clinical Research Informatics

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

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