将通用大模型适配到医疗场景，提出三步开发框架与应用指南。
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges
- 分三步构建：建模、优化、系统工程，分解复杂医疗任务。
- 覆盖临床试验设计、诊断支持等10+类医疗应用案例。
- 聚焦幻觉、隐私、合规等医疗落地核心挑战，适合研发者参考。
大型语言模型（LLMs）在医疗领域的应用引发广泛关注，涵盖药物研发、临床决策支持、远程医疗、医疗器械及医保等多个环节。本文旨在探讨构建基于LLM的医疗AI应用的内在机制，提出一个全面的开发框架。综述现有文献，梳理专用医疗场景下应用LLM的独特挑战。提出三步框架：1）建模——将复杂医疗流程拆解为可管理步骤以开发医疗专用模型；2）优化——通过精心设计提示词、整合外部知识与工具提升模型性能；3）系统工程——将复杂任务分解为子任务，结合人类专家经验构建医疗AI应用。此外，提供详细的应用案例手册，涵盖优化临床试验设计、增强临床决策支持、推进医学影像分析等。最后讨论关键挑战，包括幻觉处理、数据所有权与合规性、隐私保护、知识产权、算力成本、可持续性及负责任AI要求。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to clinical decision support, assisting telemedicine, medical devices, and healthcare insurance applications. This perspective paper aims to discuss the inner workings of building LLM-powered medical AI applications and introduces a comprehensive framework for their development. We review existing literature and outline the unique challenges of applying LLMs in specialized medical contexts. Additionally, we introduce a three-step framework to organize medical LLM research activities: 1) Modeling: breaking down complex medical workflows into manageable steps for developing medical-specific models; 2) Optimization: optimizing the model performance with crafted prompts and integrating external knowledge and tools, and 3) System engineering: decomposing complex tasks into subtasks and leveraging human expertise for building medical AI applications. Furthermore, we offer a detailed use case playbook that describes various LLM-powered medical AI applications, such as optimizing clinical trial design, enhancing clinical decision support, and advancing medical imaging analysis. Finally, we discuss various challenges and considerations for building medical AI applications with LLMs, such as handling hallucination issues, data ownership and compliance, privacy, intellectual property considerations, compute cost, sustainability issues, and responsible AI requirements.
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