Abstract：
OBJECTIVE: 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.
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.
摘要：
目的：这篇简短的交流探索了潜在的，局限性,以及生成人工智能（GAI）在增强诊断方面的未来方向。
方法：本评论回顾了GAI的当前应用和进步，特别是专注于将其整合到医疗诊断中。它研究了GAI在支持医疗访谈中的作用，协助鉴别诊断，并通过双过程理论的镜头辅助临床推理。讨论得到了最近的例子和理论框架的支持，以说明GAI在医学中的实际和潜在用途。
结果：GAI通过支持将患者描述翻译成视觉格式，在增强诊断过程方面显示出巨大的希望。提供鉴别诊断，并促进复杂的临床推理。然而,限制，如产生医疗错误信息的可能性，被称为幻觉，存在。此外，评论强调了GAI与临床诊断中的直观和分析决策过程的集成，证明了诊断速度和准确性的潜在改善。
结论：虽然GAI为医学诊断提供了变革潜力，它还引入了必须谨慎管理的风险。未来的进步应该集中在完善GAI技术，以更好地与人类诊断推理保持一致。确保GAI增强而不是取代医疗专业人员的专业知识。
方法：本评论回顾了GAI的当前应用和进步，特别是专注于将其整合到医疗诊断中。它研究了GAI在支持医疗访谈中的作用，协助鉴别诊断，并通过双过程理论的镜头辅助临床推理。讨论得到了最近的例子和理论框架的支持，以说明GAI在医学中的实际和潜在用途。
结果：GAI通过支持将患者描述翻译成视觉格式，在增强诊断过程方面显示出巨大的希望。提供鉴别诊断，并促进复杂的临床推理。然而,限制，如产生医疗错误信息的可能性，被称为幻觉，存在。此外，评论强调了GAI与临床诊断中的直观和分析决策过程的集成，证明了诊断速度和准确性的潜在改善。
结论：虽然GAI为医学诊断提供了变革潜力，它还引入了必须谨慎管理的风险。未来的进步应该集中在完善GAI技术，以更好地与人类诊断推理保持一致。确保GAI增强而不是取代医疗专业人员的专业知识。
