Overuse and False Restraint in Large Language Model Recommendations for Choosing Wisely Scenarios.

IF 17.2 1区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Dean Ariel, Stav Endelman, Guy Laban
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引用次数: 0

Abstract

Background: The Choosing Wisely initiative made recommendations where screening, testing, or treatment may be overused, with caveats around appropriate use. Clinicians are increasingly using large language models (LLMs).

Objective: To evaluate whether commercial LLMs appropriately call for restraint or action from scenarios based on Choosing Wisely recommendations.

Design: Benchmark evaluation. Fifteen Choosing Wisely recommendations were used to develop 225 scenarios (per recommendation, 10 in which restraint was appropriate and 5 in which action was appropriate). Six commercial LLMs (3 runs each) generated 4050 responses to the 225 scenarios, using a prompt from the clinician perspective.

Setting: Production application programming interfaces of OpenAI, Anthropic, and Google from February to March 2026.

Participants: No human participants were involved.

Measurements: Co-primary outcomes were the overuse rate (endorsing action when restraint was appropriate) and the false restraint rate (withholding an appropriate action).

Results: The rate of LLM-recommended overuse was 5.4% (95% CI, 0.6% to 12.2%); LLMs did not recommend overuse in 8 of 15 recommendations. Two recommendations accounted for most LLM-recommended overuse: thyroid ultrasound for an incidental subcentimeter nodule (overuse recommended in 42.8% of LLM responses) and routine electrocardiography in low-risk adults (overuse recommended in 28.3% of LLM responses). The LLMs recommended overuse in 0.6% to 3.3% of responses for the remaining 5 recommendations. False restraint was rare (0.6% [CI, 0.1% to 1.2%]), was absent for 11 of 15 recommendations, and was highest for prostate-specific antigen screening (3.3%).

Limitations: Single prompts without follow-up; a benchmark rather than real-world use; responses classified by LLM judge with physician adjudication. Findings may not extend to consumer-facing use.

Conclusion: The LLMs rarely withheld recommendations for treatment when it was appropriate. Overuse was concentrated in 2 recommendations.

Primary funding source: None.

大型语言模型中的过度使用和错误约束:明智选择场景的建议。
背景:明智选择倡议对筛查、检测或治疗可能被过度使用的地方提出了建议,并对适当使用提出了警告。临床医生越来越多地使用大型语言模型(llm)。目的:根据《明智的选择》的建议,评估商业法学硕士是否适当地呼吁采取克制或行动。设计:基准评估。15个“明智选择”建议被用于开发225个场景(每个建议中,10个是适当的限制,5个是适当的行动)。六个商业llm(每个运行3次)对225个场景产生了4050个响应,从临床医生的角度使用提示。设定:2026年2月- 3月OpenAI、Anthropic、谷歌的生产应用编程接口。参与者:无人类参与者参与。测量:共同的主要结果是过度使用率(在适当的限制时赞成采取行动)和错误的限制率(拒绝采取适当的行动)。结果:llm推荐的过度用药率为5.4% (95% CI, 0.6% ~ 12.2%);法学硕士在15条建议中有8条不建议过度使用。两项建议占LLM推荐的大多数过度使用:甲状腺超声检查偶发亚厘米结节(42.8%的LLM反应推荐过度使用)和低风险成人常规心电图检查(28.3%的LLM反应推荐过度使用)。法学硕士建议,在剩下的5项建议中,有0.6%至3.3%的回应建议过度使用。假抑制很少见(0.6% [CI, 0.1%至1.2%]),在15项推荐中有11项不存在,在前列腺特异性抗原筛查中最高(3.3%)。局限性:提示单一,无后续;一个基准,而不是真实的使用;回答由法学硕士法官分类与医师裁决。研究结果可能不适用于面向消费者的使用。结论:LLMs在适当的情况下很少保留治疗建议。过量使用集中在两项建议中。主要资金来源:无。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Internal Medicine
Annals of Internal Medicine 医学-医学:内科
CiteScore
23.90
自引率
1.80%
发文量
1136
审稿时长
3-8 weeks
期刊介绍: Established in 1927 by the American College of Physicians (ACP), Annals of Internal Medicine is the premier internal medicine journal. Annals of Internal Medicine’s mission is to promote excellence in medicine, enable physicians and other health care professionals to be well informed members of the medical community and society, advance standards in the conduct and reporting of medical research, and contribute to improving the health of people worldwide. To achieve this mission, the journal publishes a wide variety of original research, review articles, practice guidelines, and commentary relevant to clinical practice, health care delivery, public health, health care policy, medical education, ethics, and research methodology. In addition, the journal publishes personal narratives that convey the feeling and the art of medicine.
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