Papers MedQA
“MedQA” 태그가 달린 논문 80편 · 필터 해제
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
We introduce Biomed-Enriched, a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, as…
ArticlesContinual PretrainingLanguage ModelingLanguage Modelling+4Gazal-R1: Achieving State-of-the-Art Medical Reasoning with Parameter-Efficient Two-Stage Training
We present Gazal-R1, a 32-billion-parameter language model that achieves state-of-the-art performance in medical reasoning while providing transparent, step-by-step explanations for clinical decision-making. Built upon Q…
MedQAMMLULoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing
Recent efforts to combine low-rank adaptation (LoRA) with mixture-of-experts (MoE) for adapting large language models (LLMs) to multiple tasks still exhibit prevailing limitations: they either swap entire attention/feed-…
ARCCoLAGSM8KHumanEval+4Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs
Large language models (LLMs) have shown great potential in medical question answering (MedQA), yet adapting them to biomedical reasoning remains challenging due to domain-specific complexity and limited supervision. In t…
Medical Question AnsweringMedQAMultiple-choicePrompt Engineering+1Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards
Large language models have shown promise in clinical decision making, but current approaches struggle to localize and correct errors at specific steps of the reasoning process. This limitation is critical in medicine, wh…
DiagnosticMedQARetrieval-augmented GenerationMed-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection
Large reasoning models have recently made significant strides in mathematical and code reasoning, yet their success has not transferred smoothly to the medical domain. While multiple factors contribute to this disparity,…
Medical Question AnsweringMedQAQuestion AnsweringEnabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation
Large Language Models (LLMs) have significant impact on the healthcare scenarios but remain prohibitively large for deployment in real-time, resource-constrained environments such as edge devices. In this work, we introd…
MedQAQuantizationRaspberry Pi 5Second Opinion Matters: Towards Adaptive Clinical AI via the Consensus of Expert Model Ensemble
Despite the growing clinical adoption of large language models (LLMs), current approaches heavily rely on single model architectures. To overcome risks of obsolescence and rigid dependence on single model systems, we pre…
Decision MakingMedQATAGS: A Test-Time Generalist-Specialist Framework with Retrieval-Augmented Reasoning and Verification
Recent advances such as Chain-of-Thought prompting have significantly improved large language models (LLMs) in zero-shot medical reasoning. However, prompting-based methods often remain shallow and unstable, while fine-t…
MedQAWiNGPT-3.0 Technical Report
Current Large Language Models (LLMs) exhibit significant limitations, notably in structured, interpretable, and verifiable medical reasoning, alongside practical deployment challenges related to computational resources a…
DiagnosticMedQAreinforcement-learningReinforcement Learning+1Synthetic Data RL: Task Definition Is All You Need
Reinforcement learning (RL) is a powerful way to adapt foundation models to specialized tasks, but its reliance on large-scale human-labeled data limits broad adoption. We introduce Synthetic Data RL, a simple and genera…
AllGSM8KMathMedQA+1Disentangling Reasoning and Knowledge in Medical Large Language Models
Medical reasoning in large language models (LLMs) aims to emulate clinicians' diagnostic thinking, but current benchmarks such as MedQA-USMLE, MedMCQA, and PubMedQA often mix reasoning with factual recall. We address thi…
DiagnosticMedQAMedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports
Doctors and patients alike increasingly use Large Language Models (LLMs) to diagnose clinical cases. However, unlike domains such as math or coding, where correctness can be objectively defined by the final answer, medic…
DiagnosticMathMedical DiagnosisMedQA+1What Does Neuro Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs
In this paper, we introduce S-MedQA, an English medical question-answering (QA) dataset for benchmarking large language models in fine-grained clinical specialties. We use S-MedQA to check the applicability of a popular …
AllBenchmarkingMedical Question AnsweringMedQA+1A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage
Sanitizing sensitive text data typically involves removing personally identifiable information (PII) or generating synthetic data under the assumption that these methods adequately protect privacy; however, their effecti…
MedQACliniChat: A Multi-Source Knowledge-Driven Framework for Clinical Interview Dialogue Reconstruction and Evaluation
Large language models (LLMs) hold great promise for assisting clinical interviews due to their fluent interactive capabilities and extensive medical knowledge. However, the lack of high-quality interview dialogue data an…
MedQAEvaluation of the phi-3-mini SLM for identification of texts related to medicine, health, and sports injuries
Small Language Models (SLMs) have potential to be used for automatically labelling and identifying aspects of text data for medicine/health-related purposes from documents and the web. As their resource requirements are …
4kMedQASusceptibility of Large Language Models to User-Driven Factors in Medical Queries
Large language models (LLMs) are increasingly used in healthcare, but their reliability is heavily influenced by user-driven factors such as question phrasing and the completeness of clinical information. In this study, …
DiagnosticMedQAMisinformationBias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems
Medical Question Answering systems based on Retrieval Augmented Generation is promising for clinical decision support because they can integrate external knowledge, thus reducing inaccuracies inherent in standalone large…
counterfactualDecision MakingFairnessMedical Question Answering+7MDTeamGPT: A Self-Evolving LLM-based Multi-Agent Framework for Multi-Disciplinary Team Medical Consultation
Large Language Models (LLMs) have made significant progress in various fields. However, challenges remain in Multi-Disciplinary Team (MDT) medical consultations. Current research enhances reasoning through role assignmen…
MedQA