The field of medical language models is rapidly evolving, with a focus on improving clinical applications such as diagnostic accuracy, patient data analysis, and medical document summarization. Recent research has explored the use of large language models (LLMs) for tasks like abstractive summarization of medical reports, development of rule-based clinical NLP systems, and generation of clinical note summaries. These models have demonstrated significant potential in enhancing medical decision-making and streamlining clinical workflows. Notably, the integration of LLMs with electronic health records (EHRs) has improved diagnostic performance and clinical test recommendation. Furthermore, multimodal language models have shown promise in medical visual question answering, disease diagnosis, and medical image analysis. Overall, the advancements in medical language models are paving the way for more accurate, efficient, and personalized healthcare services. Noteworthy papers in this area include DistillNote, which presents a framework for LLM-based clinical note summarization, and MAM, a modular multi-agent framework for multi-modal medical diagnosis via role-specialized collaboration. These papers highlight the innovative applications of LLMs in clinical settings and demonstrate their potential to transform the field of medical research.
Advancements in Medical Language Models for Clinical Applications
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Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reports
Initial Investigation of LLM-Assisted Development of Rule-Based Clinical NLP System
DistillNote: LLM-based clinical note summaries improve heart failure diagnosis
Enhancing Step-by-Step and Verifiable Medical Reasoning in MLLMs
MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models
MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports
MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications
Automatic Posology Structuration : What role for LLMs?
LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and Differential Diagnosis
Evaluating Rare Disease Diagnostic Performance in Symptom Checkers: A Synthetic Vignette Simulation Approach
MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration
DiaLLMs: EHR Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction
MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations
A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Evidence-based diagnostic reasoning with multi-agent copilot for human pathology
Class-Agnostic Region-of-Interest Matching in Document Images
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning