用逻辑约束提升手术流程识别准确率，减少错误推理。
Hallucinations and Constraints : Regulating surgical workflow recognition beyond accuracy
- 将手术阶段识别问题转化为可验证的逻辑命题，通过概率图模型强制约束。
- 在机器人辅助子宫切除术中提升准确率约10%，基本消除拓扑错误。
- 为医疗AI提供数学保证，适合关注可信医学AI的研究者。
人工智能在医学中的应用面临幻觉问题，尤其在医学图像处理领域尚未充分研究。与自然语言理解不同，从生物医学图像和信号推断出的预测是否正确难以直观判断。本文提出，拓扑错误可作为可量化、可调控的幻觉形式。针对生物医学信号分割等特定问题，部分性质可重述为线性时序逻辑谓词，并通过概率图模型显式施加约束。模拟结果表明，在机器人辅助子宫切除术的自动手术阶段识别中，该方法使准确率提升约10%，同时几乎完全消除拓扑错误，表明数学上的正确性保证可补充现有医疗图像计算与计算机辅助干预中基于经验的监管方式。
原文摘要 · Abstract (English)
Hallucinations are a major concern for the integration of artificial intelligence into medicine, although less explored in the realm of medical image processing. Unlike problems in natural text understanding and reasoning therewith, determining whether or not predictions derived from biomedical images and signals is less intuitively clear. This article suggests that topological errors could constitute hallucinations in a way that can be more readily measured and thus regulated. Certain of these properties for certain types of problems, such as biomedical signal segmentation, can be rephrased as linear temporal logic predicates, a number of which can be explicitly enforced using probabilistic graphical models. Our simulations show the potential of these explicitly constrained predicates for the case of automatic surgical phase recognition in robot-assisted hysterectomy, improving accuracy by approximately 10% while removing the vast majority of topological errors, suggesting that mathematical guarantees of correctness can supplement other empirical forms of regulating machine learning in medical image computing and computer-assisted interventions.
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