Hallucinations and Constraints : Regulating surgical workflow recognition beyond accuracy

2026-08-10

AI Summary

This study addresses the problem of topological hallucinations in AI-based surgical workflow recognition—specifically, implausible predictions that violate clinical temporal logic. The work formally defines such errors as a distinct form of hallucination in medical image understanding and introduces a novel method that enforces formal constraints derived from linear temporal logic. By encoding topological relationships among surgical phases as logical predicates and explicitly integrating them into a probabilistic graphical model, the approach significantly enhances the logical consistency and clinical plausibility of model outputs. Experiments on videos of robot-assisted hysterectomies demonstrate that the proposed method nearly eliminates topological errors while maintaining high recognition accuracy, thereby validating the efficacy of formal logical constraints in regulating medical AI systems.

Abstract

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.

Problem

• hallucinations
• surgical workflow recognition
• topological errors
• medical image processing
• constraint regulation

Innovation

• hallucination
• topological constraints
• linear temporal logic
• probabilistic graphical models
• surgical workflow recognition