ChatGPT and Physicians’ Malpractice Risk

Guha, Neel

doi:10.1001/jamahealthforum.2023.1938

Cited by 52 publications

(29 citation statements)

References 7 publications

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“…We speculate that ChatGPT may be more useful as a supplementary tool for SDM under physician supervision than alone because ChatGPT tends to produce factually incorrect outputs, called hallucinations. 24 Future research must be directed at this issue. Fourth, the extent of SDM may have been overestimated because of desirability bias, as some patients completed the questionnaire in the waiting room.…”

Section: Discussionmentioning

confidence: 99%

Impact of online health information-seeking behavior on shared decision-making in patients with systemic lupus erythematosus: The TRUMP²-SLE project

Ichikawa,

Kishida,

Shimojima

et al. 2023

Lupus

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Objective Providing appropriate health information to patients with systemic lupus erythematosus (SLE) is advantageous in the treatment decision-making process. We aimed to investigate how online health information-seeking behaviors affect shared decision-making (SDM) in patients with SLE. Methods This cross-sectional study included 464 patients with SLE from five institutions. The main exposure was time spent on the internet per day, divided into four categories (none, <1 h, 1– < 2 h, ≥2 h). Participants categorized their preferred first source of health information as physicians, the internet, or other media. The outcome was the degree of SDM measured via the 9-item Shared Decision-Making Questionnaire (SDM-Q-9). A general linear model was applied. Results Compared to no internet use, longer internet use was associated with a higher SDM-Q-9 score: <1 h, 6.9 points (95% confidence interval [CI] 0.32 to 13.6) and ≥2 h, 8.75 points, (95% CI 0.61 to 16.9). The SDM-Q-9 did not differ between the individuals who chose physicians and those who chose the Internet as their preferred first source of health information (–2.1 points, 95% CI –6.7 to 2.6). Individuals who chose other media had significantly lower SDM-Q-9 scores than those who chose physicians (–7.6 points, 95% CI –13.2 to –1.9). Conclusions The present study suggests that SDM between physicians and patients is positively associated with online information-seeking behavior, with no negative influence associated with accessing the Internet before clinical consultations. Rheumatologists may need to introduce their patients to websites offering high-quality health information to establish a good physician–patient relationship for SDM.

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Section: Discussionmentioning

confidence: 99%

Impact of online health information-seeking behavior on shared decision-making in patients with systemic lupus erythematosus: The TRUMP²-SLE project

Ichikawa,

Kishida,

Shimojima

et al. 2023

Lupus

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show abstract

“…Such a network could fill a critical gap in an ecosystem dominated by well-meaning but often overexuberant and inexperienced developers who lack the depth of understanding of health care delivery. Given that health AI more broadly, including genAI, is subject to existing liability regulation for health care systems and physicians, it is imperative that mechanisms are developed that use nationwide standards and best practices for testing and evaluation to ensure that the AI models developed for use in health care are trustworthy.…”

Section: Shared Resource For Development and Validationmentioning

confidence: 99%

A Nationwide Network of Health AI Assurance Laboratories

Shah,

Halamka,

Saria

et al. 2024

JAMA

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ImportanceGiven the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed.ObservationsWhile there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings.Conclusion and RelevanceThe need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.

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“…Only after these evaluations are completed should statements be allowed such as an LLM was used for a defined task in this specific workflow, it measured a metric, and observed an improvement (or deterioration) in a prespecified outcome. Such evaluations also are necessary to clarify the medicolegal risks that might occur with the use of LLMs to guide medical care, and to identify mitigation strategies for the models’ tendency to generate factually incorrect outputs that are probabilistically plausible (called hallucinations).…”

Section: Are the Purported Value Propositions Of Using Llms In Medici...mentioning

confidence: 99%

Creation and Adoption of Large Language Models in Medicine

Shah,

Entwistle,

Pfeffer

2023

JAMA

163

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ImportanceThere is increased interest in and potential benefits from using large language models (LLMs) in medicine. However, by simply wondering how the LLMs and the applications powered by them will reshape medicine instead of getting actively involved, the agency in shaping how these tools can be used in medicine is lost.ObservationsApplications powered by LLMs are increasingly used to perform medical tasks without the underlying language model being trained on medical records and without verifying their purported benefit in performing those tasks.Conclusions and RelevanceThe creation and use of LLMs in medicine need to be actively shaped by provisioning relevant training data, specifying the desired benefits, and evaluating the benefits via testing in real-world deployments.

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ChatGPT and Physicians’ Malpractice Risk

Abstract: This JAMA Forum discusses the possibilities, limitations, and risks of physician use of large language models (such as ChatGPT) along with the improvements required to improve the accuracy of the technology.

Cited by 52 publications

References 7 publications

Impact of online health information-seeking behavior on shared decision-making in patients with systemic lupus erythematosus: The TRUMP²-SLE project

Impact of online health information-seeking behavior on shared decision-making in patients with systemic lupus erythematosus: The TRUMP²-SLE project

A Nationwide Network of Health AI Assurance Laboratories

Creation and Adoption of Large Language Models in Medicine

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ChatGPT and Physicians’ Malpractice Risk

Abstract: This JAMA Forum discusses the possibilities, limitations, and risks of physician use of large language models (such as ChatGPT) along with the improvements required to improve the accuracy of the technology.

Cited by 52 publications

References 7 publications

Impact of online health information-seeking behavior on shared decision-making in patients with systemic lupus erythematosus: The TRUMP2-SLE project

Impact of online health information-seeking behavior on shared decision-making in patients with systemic lupus erythematosus: The TRUMP2-SLE project

A Nationwide Network of Health AI Assurance Laboratories

Creation and Adoption of Large Language Models in Medicine

Contact Info

Product

Resources

About

Impact of online health information-seeking behavior on shared decision-making in patients with systemic lupus erythematosus: The TRUMP²-SLE project

Impact of online health information-seeking behavior on shared decision-making in patients with systemic lupus erythematosus: The TRUMP²-SLE project