医疗AI需用真实治疗效果数据替代专家意见，提升模型实用性
Position: Medical AI Neglects Real Treatment Outcomes
- 用真实治疗结果数据替代文献和指南作为训练评估依据
- 当前主流模型因缺乏真实疗效数据而存在决策缺陷
- 适合关注临床落地的医疗AI研究者与开发者
医疗AI在诊断和预后任务上进步迅速，但对治疗本身的理解仍严重依赖人类意见与文本合成（如生物医学论文和临床指南），而非来自观察性数据库或随机试验的真实治疗结果数据。这种忽视严重制约了医疗AI潜力，已在前沿模型和主流基准中暴露缺陷。本文主张将真实治疗结果数据广泛纳入训练与评估，并重新强调改善治疗效果作为所有医疗AI的最终目标。
原文摘要 · Abstract (English)
Medical AI has rapidly improved its ability to perform diagnostic and prognostic tasks that lead to treatment decisions. But understanding of treatment itself is still inadequately trained and evaluated, using human opinions and syntheses (especially texts such as biomedical publications and clinical practice guidelines) rather than actual underlying data on treatment outcomes. This neglect seriously limits the potential of medical AI, and is already causing deficiencies in both frontier models and major benchmarks, as argued in this position paper. Real treatment outcomes, drawn from sources such as observational databases and randomized experiments, should be substantially incorporated into both training and evaluation. Improving these outcomes should be reemphasized as the downstream goal of all medical AI.
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