TrustEndo: A Conformal-Calibrated MLLM With Retrieval-Augmented Reasoning for Trustworthy Gastroscopic Diagnosis

Multimodal Large Language Models (MLLMs) can generate natural language diagnostic descriptions from gastroscopic images, but their clinical use is blocked by two problems: hallucination of plausible-yet-wrong claims, and the lack of statistical guarantees on output reliability. We introduce TrustEndo, a framework that addresses both problems through three modules: (1) a Concept-Anchored Visual-Language Aligner (CAVLA) that grounds MLLM reasoning in clinically verifiable morphological concepts via text-based prompts; (2) an Evidence-Augmented Diagnostic Memory (EADM) that retrieves relevant historical cases via multi-modal RAG; and (3) a Conformal Safety Layer (CSL) that extends conformal prediction to multimodal outputs, providing coverage guarantees over detection predictions under exchangeability, with semantic-level reliability assessment for diagnostic language. Built on Qwen-3.5 and GLM-5.0, TrustEndo also includes a Domain-Adaptive Refinement (DAR) module for cross-center generalization. On LGLDD, Endo21, and HyperKvasir, TrustEndo achieves AP 40.9% (vs. 39.5% best MLLM baseline), hallucination rate 7.8% (vs. 15-20%), and ECE 0.038 (vs. 0.118+), with 95% empirical coverage on detection.