Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification

Researchers developed a neurosymbolic verification framework to audit logical consistency in AI-generated radiology reports, addressing issues where vision-language models produce diagnostic conclusions unsupported by their findings. The system uses formal verification methods to identify hallucinations and missing logical conclusions in medical AI outputs, improving diagnostic accuracy.

• →Vision-language models in medical diagnosis frequently generate logically inconsistent radiology reports with unsupported conclusions.
• →A new verification framework uses formal logic and SMT solvers to mathematically verify diagnostic claims against evidence.
• →Testing across seven VLMs revealed distinct failure modes like conservative observation and stochastic hallucination invisible to traditional metrics.
• →The verification system can systematically eliminate unsupported hallucinations in AI-generated medical reports.
• →This approach provides post-hoc guarantees for clinical reasoning accuracy in generative AI medical assistants.