厘清医学领域大模型的炒作与实际应用，助力医疗人员理性使用AI。
Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals
- 区分大模型在医疗中的真实价值与过度宣传，强调谨慎应用。
- 指出大模型无现实认知能力，存在泄露敏感信息的风险。
- 适合医疗从业者、研究者及政策制定者参考决策。
医疗体系涵盖医学生与研究人员的培养、临床实践以及相关研究领域。这些环节中诸多任务可受益于自动化与程序化辅助。机器学习与人工智能技术，包括大语言模型（LLMs），被承诺能推动医疗创新，提升诊疗速度与准确性，减轻医护人员手动操作负担。然而，大模型缺乏基于现实的客观认知能力，且在临床与研究中使用时可能带来敏感信息泄露的实际风险。因此，人工智能在医学中的应用，特别是大模型的部署，需结合具体场景进行审慎评估，以实现技术红利并规避潜在危害。
原文摘要 · Abstract (English)
The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.
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