Jul 2026· 2026 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)· pp. 55-61· 0 citations· 33 references
Abstract
Factual reliability remains a critical bottleneck for Large Language Models (LLMs) in specialized medical domains like Systemic Lupus Erythematosus (SLE). While Retrieval-Augmented Generation (RAG) mitigates hallucinations, the qualitative impact of different underlying knowledge sources remains under-explored. This paper addresses this gap by evaluating three distinct repositories—Medical Textbooks, Academic Journals, and Clinical Guidelines—within a multi-path RAG framework using an open-weights, small-scale LLM (Gemma-3:4b). Based on a double-blind assessment of 45 stratified queries by three medical residents, we map the unique competency profiles of each source type. The core contributions of this study are twofold: (1) we provide an empirical characterization of medical source trade-offs, demonstrating that textbooks excel in foundational clarity and complex reasoning, guidelines ensure procedural precision, and journals provide technical depth at the cost of synthesis friction; and (2) we offer initial empirical insights that can help the development of future intent-aware RAG routing mechanisms to enhance clinical safety.
Large Language Models (LLMs) such as ChatGPT and Gemini are increasingly used to answer medical and psychological questions, yet systematic evaluations across domains with differing reasoning demands remain limited. We present a multi-domain expert evaluation of two state-of-the-art models, ChatGPT Pro (v5.2) and Gemin...
Pragna Prahallad, Pranathi Prahallad, Dhrithi P. Desai· 2026 6th International Confe...· 0 citations
Smart Medical Microbiology showed strong domain adaptability in medical microbiology knowledge organization, semantic generation, and retrieval-augmented reasoning, which supports its potential use in educational support, infectious disease knowledge assistance, and retrieval-enhanced medical question answering.
Yong-Qian Gong, Ruiqiang Ma, Xicheng Wang et al.· Applied Informatics· 0 citations
Background/Objectives: Large language models (LLMs) show promise for clinical decision support, yet their accuracy in interpreting specialized medical guidelines remains uncertain. Retrieval-augmented generation (RAG) may enhance performance by grounding responses in authoritative knowledge bases. This study aimed to c...
Andreas Vollmer, Lara K. Schorn, Felix Schrader et al.· Diagnostics· 0 citations
Findings show that clinical LLM explainability has shifted toward fluent generative rationales, but evidence that such explanations reflect model reasoning remains limited, and three regulatory priorities are highlighted: prioritizing explanations that enable independent verification or logic auditing over plausibility...
VITA's advantages in accuracy and completeness persisted under the neutral judge; its communication scores were lower, and this results indicate that a purpose-built clinical RAG system remains competitive with frontier LLMs on an open benchmark, consistent with corpus specificity as a design variable that improves gro...
P. Reddy, C. Mandke, Suvrankar Datta et al.· 0 citations
A dual-view approach that connects clinical practice with computational methods is presented, establishing a five-level competency scheme following Miller’s Pyramid and linking deductive, inductive, and abductive reasoning patterns to common medical goals and tasks.
