Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?

2026-01-19

AI Summary

This work proposes MEDASSESS-X, a novel framework that addresses the limitations of costly and narrowly generalizable medical-domain fine-tuning in clinical question-answering systems. For the first time, it achieves cross-model medical consistency during inference by aligning model activations through lightweight steering vectors, eliminating the need for fine-tuning while maintaining compatibility with both general-purpose and medical-specialized large language models. By effectively mitigating “specialization hallucinations,” the method consistently enhances performance across multiple model families, yielding up to a 6% improvement in accuracy, a 7% gain in factual consistency, and a 50% reduction in safety-related errors.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Clinical Question-Answering (CQA) industry systems are increasingly rely on Large Language Models (LLMs), yet their deployment is often guided by the assumption that domain-specific fine-tuning is essential. Although specialised medical LLMs such as BioBERT, BioGPT, and PubMedBERT remain popular, they face practical limitations including narrow coverage, high retraining costs, and limited adaptability. Efforts based on Supervised Fine-Tuning (SFT) have attempted to address these assumptions but continue to reinforce what we term the SPECIALISATION FALLACY-the belief that specialised medical LLMs are inherently superior for CQA. To address this assumption, we introduce MEDASSESS-X, a deployment-industry-oriented CQA framework that applies alignment at inference time rather than through SFT. MEDASSESS-X uses lightweight steering vectors to guide model activations toward medically consistent reasoning without updating model weights or requiring domain-specific retraining. This inference-time alignment layer stabilises CQA performance across both general-purpose and specialised medical LLMs, thereby resolving the SPECIALISATION FALLACY. Empirically, MEDASSESS-X delivers consistent gains across all LLM families, improving Accuracy by up to +6%, Factual Consistency by +7%, and reducing Safety Error Rate by as much as 50%.

Problem

• Clinical Question Answering
• Large Language Models
• Domain-specific Fine-tuning
• Specialisation Fallacy
• Medical LLMs

Innovation

• inference-time alignment
• steering vectors
• clinical question answering
• specialisation fallacy
• medical LLMs