{"title":"The potential, limitations, and future of diagnostics enhanced by generative artificial intelligence.","authors":"Takanobu Hirosawa, Taro Shimizu","doi":"10.1515/dx-2024-0095","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>This short communication explores the potential, limitations, and future directions of generative artificial intelligence (GAI) in enhancing diagnostics.</p><p><strong>Methods: </strong>This commentary reviews current applications and advancements in GAI, particularly focusing on its integration into medical diagnostics. It examines the role of GAI in supporting medical interviews, assisting in differential diagnosis, and aiding clinical reasoning through the lens of dual-process theory. The discussion is supported by recent examples and theoretical frameworks to illustrate the practical and potential uses of GAI in medicine.</p><p><strong>Results: </strong>GAI shows significant promise in enhancing diagnostic processes by supporting the translation of patient descriptions into visual formats, providing differential diagnoses, and facilitating complex clinical reasoning. However, limitations such as the potential for generating medical misinformation, known as hallucinations, exist. Furthermore, the commentary highlights the integration of GAI with both intuitive and analytical decision-making processes in clinical diagnostics, demonstrating potential improvements in both the speed and accuracy of diagnoses.</p><p><strong>Conclusions: </strong>While GAI presents transformative potential for medical diagnostics, it also introduces risks that must be carefully managed. Future advancements should focus on refining GAI technologies to better align with human diagnostic reasoning, ensuring GAI enhances rather than replaces the medical professionals' expertise.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"446-449"},"PeriodicalIF":2.2000,"publicationDate":"2024-07-11","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-2024-0095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: This short communication explores the potential, limitations, and future directions of generative artificial intelligence (GAI) in enhancing diagnostics.
Methods: This commentary reviews current applications and advancements in GAI, particularly focusing on its integration into medical diagnostics. It examines the role of GAI in supporting medical interviews, assisting in differential diagnosis, and aiding clinical reasoning through the lens of dual-process theory. The discussion is supported by recent examples and theoretical frameworks to illustrate the practical and potential uses of GAI in medicine.
Results: GAI shows significant promise in enhancing diagnostic processes by supporting the translation of patient descriptions into visual formats, providing differential diagnoses, and facilitating complex clinical reasoning. However, limitations such as the potential for generating medical misinformation, known as hallucinations, exist. Furthermore, the commentary highlights the integration of GAI with both intuitive and analytical decision-making processes in clinical diagnostics, demonstrating potential improvements in both the speed and accuracy of diagnoses.
Conclusions: While GAI presents transformative potential for medical diagnostics, it also introduces risks that must be carefully managed. Future advancements should focus on refining GAI technologies to better align with human diagnostic reasoning, ensuring GAI enhances rather than replaces the medical professionals' expertise.
目的这篇短文探讨了生成式人工智能（GAI）在提高诊断水平方面的潜力、局限性和未来发展方向：本评论回顾了生成式人工智能的当前应用和进展，尤其关注其在医疗诊断中的整合。它通过双过程理论的视角，探讨了生成式人工智能在支持医学访谈、协助鉴别诊断和辅助临床推理方面的作用。讨论以最新实例和理论框架为支撑，说明了 GAI 在医学中的实际和潜在用途：结果：通过支持将患者描述转化为可视化格式、提供鉴别诊断和促进复杂的临床推理，GAI 在增强诊断过程方面显示出巨大的潜力。然而，也存在一些局限性，如可能产生医学误导（即幻觉）。此外，评论还强调了 GAI 与临床诊断中的直觉和分析决策过程的结合，显示了提高诊断速度和准确性的潜力：虽然 GAI 为医疗诊断带来了变革性的潜力，但也带来了必须谨慎管理的风险。未来的进步应集中在改进 GAI 技术，使其更好地与人类诊断推理相一致，确保 GAI 增强而不是取代医疗专业人员的专业知识。
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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