Neuro-symbolic AI for auditable cognitive information extraction from medical reports.

ABSTRACT:

Background

Large language models (LLMs) such as GPT-4 can interpret free text, but unreliable answers, opaque reasoning, and privacy risks limit their use in healthcare. In contrast, rule-based artificial intelligence (AI) provides transparent and reproducible results but struggles with free text. We aimed to combine the strengths of both approaches to test whether such a hybrid system can autonomously and reliably extract clinical data from diagnostic imaging reports.

Methods

We developed a neuro-symbolic AI that connects GPT-4 with a rule-based expert system through a semantic integration platform. GPT-4 extracted candidate facts from free-text reports, while the expert system verified them against medical rules, producing traceable, deterministic labels. We evaluated the

SUBMITTER: Prenosil GA 

PROVIDER: S-EPMC12638795 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature