{"title":"使用Chat Generative预训练Transformer增强临床推理：实用指南。","authors":"Takanobu Hirosawa, Taro Shimizu","doi":"10.1515/dx-2023-0116","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"102-105"},"PeriodicalIF":2.2000,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide.\",\"authors\":\"Takanobu Hirosawa, Taro Shimizu\",\"doi\":\"10.1515/dx-2023-0116\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":11273,\"journal\":{\"name\":\"Diagnosis\",\"volume\":\" \",\"pages\":\"102-105\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2023-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diagnosis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/dx-2023-0116\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/2/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnosis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/dx-2023-0116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide.
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.
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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