Hallucinations and Constraints: Regulating surgical workflow recognition beyond accuracy

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.

Improvements to AI Systems:

1. Add a topological-error constraint layer to segmentation and phase-recognition models. Instead of only optimizing pixel-wise or frame-wise accuracy, the system will enforce linear temporal logic (LTL) predicates (e.g., phase A must precede phase B, no isolated phase segments shorter than X frames) during inference via probabilistic graphical models (e.g., conditional random fields or hidden Markov models with LTL-derived potentials). This removes hallucinated anatomical structures or impossible phase transitions.

2. Implement a hallucination audit module that quantifies topological violations (e.g., broken vessel continuity, spurious holes in organs, non-sequential surgical steps) as a separate, interpretable metric alongside standard accuracy. The system will flag predictions that violate these predicates, allowing clinicians to reject or review them before use.

3. Integrate a hybrid training objective that combines empirical loss (e.g., cross-entropy) with a differentiable penalty for LTL violations. This forces the neural network to learn representations that inherently respect temporal and spatial topology, reducing reliance on post-hoc correction.

4. Build a verification wrapper for any medical image AI pipeline: after model output, run a fast LTL model checker to certify whether the prediction satisfies all hard constraints. If not, the system automatically triggers a fallback (e.g., nearest valid prediction from a constrained search) or requests human intervention.

What the Improved AI System Can Do:

• For robotic surgery (e.g., hysterectomy): Automatically recognize surgical phases with 10% higher accuracy and zero or near-zero topological errors (e.g., never skipping from cutting to suturing without hemostasis, never producing a phase timeline that violates clinical workflow).

• For radiology (e.g., vessel or airway segmentation): Guarantee that segmented structures are connected and free of false holes or breaks, preventing hallucinations of non-existent lesions or missing real ones.

• For real-time monitoring: Provide a certified correct label on each prediction, with a mathematical guarantee that no impossible sequence or structure is output, enabling safe autonomous decision support in operating rooms or ICUs.

• For regulatory compliance: Offer a formal, auditable proof of constraint satisfaction for each case, which can be submitted to medical device regulators as evidence of reduced hallucination risk.