The field of medical text understanding and clinical decision support is rapidly evolving, with a focus on developing more accurate, scalable, and globally applicable solutions. Recent research has highlighted the importance of adaptive learning strategies, such as Threshold-Adaptive Curriculum Learning, to improve the performance of automated systems in understanding medical texts. Additionally, the integration of multi-source knowledge and external evidence has been shown to enhance the accuracy and interpretability of clinical decision support systems. The use of large language models and reinforcement learning techniques has also demonstrated significant potential in advancing clinical decision-making and healthcare analytics. Noteworthy papers in this area include TACL, which presents a novel framework for enhancing medical text understanding, and TraceCoder, which proposes a framework for traceable ICD coding via multi-source knowledge integration. Other notable papers include BiomedXPro, which introduces an evolutionary framework for prompt optimization, and ReclAIm, which presents a multi-agent framework for degradation-aware performance tuning of medical imaging AI.
Advancements in Medical Text Understanding and Clinical Decision Support
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TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding
TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration
BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models
Fusion-Augmented Large Language Models: Boosting Diagnostic Trustworthiness via Model Consensus
ReclAIm: A multi-agent framework for degradation-aware performance tuning of medical imaging AI
Leveraging Group Relative Policy Optimization to Advance Large Language Models in Traditional Chinese Medicine
BenCao: An Instruction-Tuned Large Language Model for Traditional Chinese Medicine
Timely Clinical Diagnosis through Active Test Selection
Prior-informed optimization of treatment recommendation via bandit algorithms trained on large language model-processed historical records
XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography
MedReason-R1: Learning to Reason for CT Diagnosis with Reinforcement Learning and Local Zoom
Enhancing Diagnostic Accuracy for Urinary Tract Disease through Explainable SHAP-Guided Feature Selection and Classification
Beyond MedQA: Towards Real-world Clinical Decision Making in the Era of LLMs
Enhancing Reasoning Skills in Small Persian Medical Language Models Can Outperform Large-Scale Data Training
Assessing the Feasibility of Early Cancer Detection Using Routine Laboratory Data: An Evaluation of Machine Learning Approaches on an Imbalanced Dataset
PSO-XAI: A PSO-Enhanced Explainable AI Framework for Reliable Breast Cancer Detection