Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians.

IF 2.6 3区医学 Q1 PRIMARY HEALTH CARE

Family Medicine and Community Health Pub Date : 2023-09-01 DOI:10.1136/fmch-2023-002391

Inbar Levkovich, Zohar Elyoseph

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引用次数: 0

Abstract

Objective: To compare evaluations of depressive episodes and suggested treatment protocols generated by Chat Generative Pretrained Transformer (ChatGPT)-3 and ChatGPT-4 with the recommendations of primary care physicians.

Methods: Vignettes were input to the ChatGPT interface. These vignettes focused primarily on hypothetical patients with symptoms of depression during initial consultations. The creators of these vignettes meticulously designed eight distinct versions in which they systematically varied patient attributes (sex, socioeconomic status (blue collar worker or white collar worker) and depression severity (mild or severe)). Each variant was subsequently introduced into ChatGPT-3.5 and ChatGPT-4. Each vignette was repeated 10 times to ensure consistency and reliability of the ChatGPT responses.

Results: For mild depression, ChatGPT-3.5 and ChatGPT-4 recommended psychotherapy in 95.0% and 97.5% of cases, respectively. Primary care physicians, however, recommended psychotherapy in only 4.3% of cases. For severe cases, ChatGPT favoured an approach that combined psychotherapy, while primary care physicians recommended a combined approach. The pharmacological recommendations of ChatGPT-3.5 and ChatGPT-4 showed a preference for exclusive use of antidepressants (74% and 68%, respectively), in contrast with primary care physicians, who typically recommended a mix of antidepressants and anxiolytics/hypnotics (67.4%). Unlike primary care physicians, ChatGPT showed no gender or socioeconomic biases in its recommendations.

Conclusion: ChatGPT-3.5 and ChatGPT-4 aligned well with accepted guidelines for managing mild and severe depression, without showing the gender or socioeconomic biases observed among primary care physicians. Despite the suggested potential benefit of using atificial intelligence (AI) chatbots like ChatGPT to enhance clinical decision making, further research is needed to refine AI recommendations for severe cases and to consider potential risks and ethical issues.

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在开始治疗时识别抑郁症及其决定因素：ChatGPT与初级保健医生。

目的：将Chat-Generative Pretrained Transformer（ChatGPT）-3和ChatGPT-4生成的抑郁发作评估和建议治疗方案与初级保健医生的建议进行比较。方法：将Vignette输入到ChatGPT接口。这些小插曲主要集中在最初咨询期间出现抑郁症状的假设患者身上。这些小插曲的创作者精心设计了八个不同的版本，系统地改变了患者的属性（性别、社会经济地位（蓝领工人或白领工人）和抑郁症的严重程度（轻度或重度））。每个变体随后被引入到ChatGPT-3.5和ChatGPT-4中。每个小插曲重复10次，以确保ChatGPT响应的一致性和可靠性。结果：对于轻度抑郁症，ChatGPT-3.5和ChatGPT-4分别在95.0%和97.5%的病例中推荐心理治疗。然而，初级保健医生仅在4.3%的病例中建议进行心理治疗。对于严重病例，ChatGPT倾向于采用联合心理治疗的方法，而初级保健医生则建议采用联合方法。ChatGPT-3.5和ChatGPT-4的药理学建议显示，他们更喜欢独家使用抗抑郁药（分别为74%和68%），而初级保健医生通常建议混合使用抗抑郁剂和抗焦虑/催眠药（67.4%）。与初级保健医生不同，ChatGPT在其建议中没有显示出性别或社会经济偏见。结论：ChatGPT-3.5和ChatGPT-4与公认的轻度和重度抑郁症管理指南一致，没有显示出在初级保健医生中观察到的性别或社会经济偏见。尽管使用像ChatGPT这样的人工智能聊天机器人来增强临床决策有潜在的好处，但还需要进一步的研究来完善针对重症病例的人工智能建议，并考虑潜在的风险和道德问题。

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

Family Medicine and Community Health PRIMARY HEALTH CARE-

CiteScore

9.70

自引率

0.00%

发文量

审稿时长

19 weeks

期刊介绍： Family Medicine and Community Health (FMCH) is a peer-reviewed, open-access journal focusing on the topics of family medicine, general practice and community health. FMCH strives to be a leading international journal that promotes ‘Health Care for All’ through disseminating novel knowledge and best practices in primary care, family medicine, and community health. FMCH publishes original research, review, methodology, commentary, reflection, and case-study from the lens of population health. FMCH’s Asian Focus section features reports of family medicine development in the Asia-pacific region. FMCH aims to be an exemplary forum for the timely communication of medical knowledge and skills with the goal of promoting improved health care through the practice of family and community-based medicine globally. FMCH aims to serve a diverse audience including researchers, educators, policymakers and leaders of family medicine and community health. We also aim to provide content relevant for researchers working on population health, epidemiology, public policy, disease control and management, preventative medicine and disease burden. FMCH does not impose any article processing charges (APC) or submission charges.

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