让大模型理解医疗人员需求，提升可信医疗AI应用
Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration
- 让医护人员全程参与模型训练与应用，确保AI符合医疗实际
- 通过融入医学知识和人类指导，提升模型输出的准确性与可靠性
- 适合关注医疗AI落地与人机协作的研究者与临床从业者
大语言模型（LLMs）在医疗领域的广泛应用引发了对其与医疗利益相关方需求对齐的关注。这种对齐是实现高效、安全、负责任医疗AI应用的关键基础。然而，现有模型行为可能与医疗人员的知识、需求和价值观不一致。为实现人机对齐，医疗专业人员需深度参与大模型在医疗中的全生命周期，包括训练数据筛选、模型训练及推理阶段。本文综述了医疗利益相关方与大模型对齐的方法、工具与应用场景，表明通过加强医学知识融合、任务理解与人类引导，可使大模型更贴合人类价值观。文章展望了构建可信真实医疗应用中人机协同的未来方向。
原文摘要 · Abstract (English)
The wide exploration of large language models (LLMs) raises the awareness of alignment between healthcare stakeholder preferences and model outputs. This alignment becomes a crucial foundation to empower the healthcare workflow effectively, safely, and responsibly. Yet the varying behaviors of LLMs may not always match with healthcare stakeholders' knowledge, demands, and values. To enable a human-AI alignment, healthcare stakeholders will need to perform essential roles in guiding and enhancing the performance of LLMs. Human professionals must participate in the entire life cycle of adopting LLM in healthcare, including training data curation, model training, and inference. In this review, we discuss the approaches, tools, and applications of alignments between healthcare stakeholders and LLMs. We demonstrate that LLMs can better follow human values by properly enhancing healthcare knowledge integration, task understanding, and human guidance. We provide outlooks on enhancing the alignment between humans and LLMs to build trustworthy real-world healthcare applications.
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