Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide.

IF 2.2 Q2 MEDICINE, GENERAL & INTERNAL

Diagnosis Pub Date : 2023-10-03 eCollection Date: 2024-02-01 DOI:10.1515/dx-2023-0116

Takanobu Hirosawa, Taro Shimizu

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引用次数: 0

Abstract

Objectives: This study aimed to elucidate effective methodologies for utilizing the generative artificial intelligence (AI) system, namely the Chat Generative Pre-trained Transformer (ChatGPT), in improving clinical reasoning abilities among clinicians.

Methods: We conducted a comprehensive exploration of the capabilities of ChatGPT, emphasizing two main areas: (1) efficient utilization of ChatGPT, with a focus on application and language selection, input methodology, and output verification; and (2) specific strategies to bolster clinical reasoning using ChatGPT, including self-learning via simulated clinical case creation and engagement with published case reports.

Results: Effective AI-based clinical reasoning development requires a clear delineation of both system roles and user needs. All outputs from the system necessitate rigorous verification against credible medical resources. When used in self-learning scenarios, capabilities of ChatGPT in clinical case creation notably enhanced disease comprehension.

Conclusions: The efficient use of generative AIs, as exemplified by ChatGPT, can impressively enhance clinical reasoning among medical professionals. Adopting these cutting-edge tools promises a bright future for continuous advancements in clinicians' diagnostic skills, heralding a transformative era in digital healthcare.

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使用Chat Generative预训练Transformer增强临床推理：实用指南。

目的：本研究旨在阐明利用生成人工智能（AI）系统，即聊天生成预训练转换器（ChatGPT），提高临床医生临床推理能力的有效方法。方法：我们对ChatGPT的能力进行了全面的探索，强调了两个主要领域：（1）ChatGPT高效利用，重点是应用和语言选择、输入方法和输出验证；以及（2）使用ChatGPT支持临床推理的具体策略，包括通过模拟临床病例创建和参与已发布的病例报告进行自我学习。结果：有效的基于人工智能的临床推理开发需要清楚地描述系统角色和用户需求。该系统的所有产出都需要对可靠的医疗资源进行严格核查。当用于自学习场景时，ChatGPT在临床病例创建中的能力显著增强了对疾病的理解。结论：以ChatGPT为例，有效使用生成性人工智能可以显著提高医学专业人员的临床推理能力。采用这些尖端工具有望为临床医生诊断技能的不断进步带来光明的未来，预示着数字医疗的变革时代。

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

Diagnosis MEDICINE, GENERAL & INTERNAL-

CiteScore

7.20

自引率

5.70%

发文量

期刊介绍： Diagnosis focuses on how diagnosis can be advanced, how it is taught, and how and why it can fail, leading to diagnostic errors. The journal welcomes both fundamental and applied works, improvement initiatives, opinions, and debates to encourage new thinking on improving this critical aspect of healthcare quality.　 Topics: -Factors that promote diagnostic quality and safety -Clinical reasoning -Diagnostic errors in medicine -The factors that contribute to diagnostic error: human factors, cognitive issues, and system-related breakdowns -Improving the value of diagnosis – eliminating waste and unnecessary testing -How culture and removing blame promote awareness of diagnostic errors -Training and education related to clinical reasoning and diagnostic skills -Advances in laboratory testing and imaging that improve diagnostic capability -Local, national and international initiatives to reduce diagnostic error

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