Jun 2026· ACM International Conference on Bioinformatics, Computational Biology and Biomedicine· 0 citations· 17 references
Computer Science
TL;DR
Findings highlight significant reliability and safety concerns when applying LLMs to medical classification tasks in their current basic form, and underline the need for improved models and methodologies before such systems can be responsibly deployed in medical informatics.
Abstract
This paper investigates the effectiveness of Large Language Models (LLMs) in handling medical classifications, focusing on two tasks: converting medical descriptions to codes (text-to-code) and generating descriptions from codes (code-to-text). We deliberately study these tasks in a probing setting, without employing fine-tuning or Retrieval-Augmented Generation (RAG) techniques, as access to external knowledge sources would make the tasks largely trivial and would not reflect the models’ intrinsic knowledge acquired during training. Our goal is therefore to evaluate the structured medical knowledge internalized by LLMs during pre-training. Experiments across several widely used medical classifications reveal substantial limitations: although larger models generally perform better, LLMs correctly identify only a limited fraction of codes and descriptions, while frequently producing hallucinated or incorrect outputs. Performance also varies substantially across classification systems, particularly for more complex ontologies. These findings highlight significant reliability and safety concerns when applying LLMs to medical classification tasks in their current basic form, and underline the need for improved models and methodologies before such systems can be responsibly deployed in medical informatics.
It is suggested that domain-adapted encoder models may be preferable for similar structured clinical NER settings, although larger and externally validated benchmarks are needed before generalizing to other languages, clinical corpora, model families, or deployment environments.
L. Elvas, Carolina Carvalho· Scientific Reports· 0 citations
This entry-level tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.
Qiao Jin, Nicholas Wan, Robert Leaman et al.· Nature Protocols· 1 citation
This review focuses specifically on Retrieval-Augmented Generation and fine-tuning, identifying them as the two dominant paradigms for integrating external knowledge: RAG for retrieval-based context injection and fine-tuning for parametric knowledge adaptation.
Soham Mukherjee, John Le, Chau Nguyen· Knowledge· 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-only rationales; preferring inspectable models where regulatory documentation is required; and prospectively validating explanations in clinical workflows before scaling.
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.
This work investigates the impact of recursive self-training on German-language medical text by recursively fine-tuning a medically specialized language model over multiple iterations and suggests that human-anchored recursive training (blending synthetic data with real, human-generated data) constitutes a promising mitigation strategy against recursive degradation.