剖析大模型在医疗中落地的四大现实挑战，助力负责任应用。
Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare
- 从操作、伦理、评估、合规四方面系统分析医疗大模型瓶颈。
- 指出当前模型在真实场景中存在安全漏洞与评估标准缺失问题。
- 适合关注AI医疗落地的临床医生、政策制定者与技术开发者。
大型语言模型（LLMs）因其接近人类水平的能力，在医疗领域备受关注，推动了其在各类健康应用场景中的探索。然而，尽管前景广阔，其在实际应用中仍面临多重挑战与障碍。本文从四个独特角度探讨了医疗领域中LLMs的关键挑战：操作脆弱性、伦理与社会考量、性能与评估难题，以及法律与监管合规问题。解决这些挑战对于充分发挥LLMs的潜力、确保其在医疗体系中的负责任集成至关重要。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in various healthcare applications. However, despite such a promising future, there are multiple challenges and obstacles that remain for their real-world uses in practical settings. This work discusses key challenges for LLMs in medical applications from four unique aspects: operational vulnerabilities, ethical and social considerations, performance and assessment difficulties, and legal and regulatory compliance. Addressing these challenges is crucial for leveraging LLMs to their full potential and ensuring their responsible integration into healthcare.
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