Papers with medical LLMs
Med-CoDE: Medical Critique based Disagreement Evaluation Framework (2025.naacl-srw)
Copied to clipboard
| Challenge: | Existing evaluation methods for large language models lack robustness and accuracy in medical contexts. |
| Approach: | They propose an evaluation framework for medical LLMs that measures disagreement between model-generated responses and established medical ground truths. |
| Outcome: | The proposed evaluation framework captures accuracy and reliability in medical settings. |
Interactive Evaluation for Medical LLMs via Task-oriented Dialogue System (2025.coling-main)
Copied to clipboard
| Challenge: | In typical medical scenarios, doctors often ask a set of questions to gain a comprehensive understanding of patients’ conditions. |
| Approach: | They propose to use multi-turn medical dialogue evaluation to evaluate proactive communication and diagnostic capabilities of medical Large Language Models (LLMs) . |
| Outcome: | The proposed model outperforms existing models on multi-turn question-answering datasets and is therefore cost-effective. |
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences (2024.acl-long)
Copied to clipboard
| Challenge: | Current large language models (LLMs) are ineffective in learning domain knowledge and aligning with human preference. |
| Approach: | They propose a benchmark LLM for Chinese medical domain that uses pre-training, supervised fine-tuning and RLHF to train LLMs. |
| Outcome: | The proposed LLM performs better than existing LLMs in the Chinese medical domain. |
Do Large Language Models Align with Core Mental Health Counseling Competencies? (2025.findings-naacl)
Copied to clipboard
Viet Cuong Nguyen, Mohammad Taher, Dongwan Hong, Vinicius Konkolics Possobom, Vibha Thirunellayi Gopalakrishnan, Ekta Raj, Zihang Li, Heather J. Soled, Michael L. Birnbaum, Srijan Kumar, Munmun De Choudhury
| Challenge: | Large language models are promising for mental health, but their alignment with core counseling competencies remains underexplored. |
| Approach: | They propose a benchmark to evaluate 22 general-purpose and medical-finetuned LLMs across five key competencies. |
| Outcome: | The proposed model outperforms generalist models in Intake, Assessment & Diagnosis but struggles with core counseling attributes and professional practice & ethics. |
Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards? (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances have seen large language models (LLMs) achieve remarkable performance across high-stakes specialized domains. |
| Approach: | They propose a diagnostic framework that evaluates legal reasoning against medical baselines along four axes (knowledge recall, grounding, confidence, and robustness) they uncover a sharp domain asymmetry when applied to a benchmark that encodes temporal validity and normative relationships. |
| Outcome: | The proposed framework evaluates legal reasoning against medical baselines along four axes (knowledge recall, grounding, confidence, and robustness) it shows that legal LLMs struggle to assess when retrieved citations are useful or misleading, exhibiting overconfidence in perturbed contexts and sensitivity to superficial formatting cues. |
MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in medical Large Language Models have demonstrated powerful reasoning and diagnostic capabilities. |
| Approach: | They propose a modular multi-agent framework for multi-modal medical diagnosis . they decompose the medical diagnostic process into specialized roles . |
| Outcome: | The framework decomposes the medical diagnostic process into specialized roles . it achieves significant performance improvements ranging from 18% to 365% compared to baseline models. |
DiaLLMs: EHR-Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing medical LLMs focus primarily on diagnosis recommendation, limiting their clinical applicability. |
| Approach: | They propose a medical LLM that integrates heterogeneous EHR data into clinically grounded dialogues. |
| Outcome: | The proposed model outperforms baselines in clinical test recommendation and diagnosis prediction. |