The field of medical research is witnessing significant advancements in AI-powered medical diagnosis and data analysis. Recent developments have focused on improving the accuracy and reliability of medical diagnosis, with a particular emphasis on leveraging large language models (LLMs) and machine learning algorithms to analyze complex medical data. One of the key trends in this area is the integration of clinical practice guidelines and electronic health records (EHRs) to develop more accurate and personalized diagnosis models. Additionally, researchers are exploring the use of multi-agent frameworks and knowledge graph-enhanced reasoning approaches to improve the reliability and interpretability of medical diagnosis predictions. The application of AI-powered tools is also being extended to areas such as medical data quality evaluation, cohort curation, and conversational AI for health. Notable papers in this area include: Sequential Diagnosis with Language Models, which introduces a sequential diagnosis benchmark and a model-agnostic orchestrator to simulate a panel of physicians and achieve high diagnostic accuracy. GDC Cohort Copilot, which presents an open-source copilot tool for curating cohorts from the Genomic Data Commons using large language models.
Advancements in AI-Powered Medical Diagnosis and Data Analysis
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Refine Medical Diagnosis Using Generation Augmented Retrieval and Clinical Practice Guidelines
Sequential Diagnosis with Language Models
Bridging the Gap with Retrieval-Augmented Generation: Making Prosthetic Device User Manuals Available in Marginalised Languages
Harnessing AI Agents to Advance Research on Refugee Child Mental Health
Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning
Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis
Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept
Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America
The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
GDC Cohort Copilot: An AI Copilot for Curating Cohorts from the Genomic Data Commons
DynamiCare: A Dynamic Multi-Agent Framework for Interactive and Open-Ended Medical Decision-Making
Medical Data Pecking: A Context-Aware Approach for Automated Quality Evaluation of Structured Medical Data
KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs