ArticleBMC medical informatics and decision making2026
Performance comparison of a neuro-symbolic large language model system versus conventional AI models and human experts in cholangitis management.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLarge language models (LLMs) have shown promising results in medical decision support; Background: Large language models (LLMs) have demonstrated promising outcomes in medical decision support; however, their efficacy in managing complex hepatobiliary conditions remains insufficiently examined. We have developed a genetic neuro-symbolic LLM system that integrates multiple AI agents with neural-symbolic reasoning for the management of cholangitis, and we have compared its performance to that of conventional LLMs and human experts.genetic neuro-symbolic LLM system integrating multiple AI agents with neural-symbolic reasoning for cholangitis management and compared its performance against conventional LLMs and human experts.
methodsThis multi-center cross-sectional study included 30 case-based questions from American Board of Internal Medicine (ABIM) gastroenterology subspecialty examinations covering acute cholangitis. Questions were categorized into diagnosis (n = 10), treatment (n = 10), and complications/prognosis (n = 10). Performance of a genetic neuro-symbolic LLM system orchestrated via LangGraph was compared against Claude 4.5 Sonnet, ChatGPT 5.2, Gemini 2.0 Flash, 10 gastroenterology specialists, and 4 emergency medicine physicians from four tertiary centers in Turkey.
resultsThe genetic neuro-symbolic system achieved the highest overall accuracy (100%, 30/30), significantly outperforming Claude 4.5 Sonnet (90.0%), ChatGPT 5.2 (60.0%), Gemini 2.0 Flash (63.3%), gastroenterology experts (mean 95.7% ± 3.2%), and emergency medicine physicians (mean 84.2% ± 8.8%). The neuro-symbolic system demonstrated superior performance across all categories and cholangitis subtypes. Among human participants, gastroenterologists outperformed emergency physicians in treatment decisions (p = 0.012) and showed non-inferior performance to Gemini 2.0 Flash overall (p = 0.034).
conclusionsThe genetic neuro-symbolic LLM system demonstrated superior accuracy in cholangitis management compared to all conventional AI models and human experts. This proof-of-concept study suggests that multi-agent architectures with neural-symbolic reasoning may offer a promising direction for AI-assisted clinical decision support in complex hepatobiliary conditions, although prospective clinical validation is required before broader implementation claims can be warranted.
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