The field of medical AI is rapidly evolving, with a focus on developing more accurate and reliable clinical decision support systems. Recent research has highlighted the importance of multimodal reasoning, incorporating both textual and visual data to improve diagnostic accuracy. Large Language Models (LLMs) are being fine-tuned for specific medical tasks, such as disease diagnosis and patient outcome prediction, with promising results. However, challenges remain, including addressing hallucinations, ensuring fairness and transparency, and integrating human-AI collaboration to support complex clinical decision-making. Noteworthy papers have introduced innovative benchmarks, such as KokushiMD-10, and frameworks like CHECK, which detect and eliminate hallucinations in LLMs. Other notable works include the development of 3D-RAD, a comprehensive 3D radiology Med-VQA dataset, and RadFabric, an agentic AI system for radiology diagnosis. These advancements have the potential to significantly enhance clinical decision support and patient outcomes, but require careful evaluation and validation to ensure safe and effective deployment.
Advances in Medical AI: Enhanced Clinical Decision Support and Diagnosis
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KokushiMD-10: Benchmark for Evaluating Large Language Models on Ten Japanese National Healthcare Licensing Examinations
Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK
3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks
Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning
LLM-as-a-Fuzzy-Judge: Fine-Tuning Large Language Models as a Clinical Evaluation Judge with Fuzzy Logic
From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review
The NordDRG AI Benchmark for Large Language Models
Enhancing Clinical Decision Support and EHR Insights through LLMs and the Model Context Protocol: An Open-Source MCP-FHIR Framework
Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
Abstract Meaning Representation for Hospital Discharge Summarization
Interpreting Biomedical VLMs on High-Imbalance Out-of-Distributions: An Insight into BiomedCLIP on Radiology
RadFabric: Agentic AI System with Reasoning Capability for Radiology
MedSyn: Enhancing Diagnostics with Human-AI Collaboration
From Chat to Checkup: Can Large Language Models Assist in Diabetes Prediction?
Enhancing the prediction of publications' long-term impact using early citations, readerships, and non-scientific factors
Dissecting the gender divide: Authorship and acknowledgment in scientific publications
DeVisE: Behavioral Testing of Medical Large Language Models
Multimodal Large Language Models for Medical Report Generation via Customized Prompt Tuning