y0news
AnalyticsDigestsSourcesTopicsRSSAICrypto

#clinical-decision-support News & Analysis

29 articles tagged with #clinical-decision-support. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

29 articles
AINeutralarXiv – CS AI · May 126/10
🧠

Medical Model Synthesis Architectures: A Case Study

Researchers propose MedMSA, a framework combining language models with formal probabilistic models to enable AI systems to make transparent, calibrated clinical predictions under uncertainty. The approach addresses critical limitations in current medical AI by producing verifiable differential diagnoses that explain patient symptoms with uncertainty weighting, marking progress toward safer clinical decision support.

AINeutralarXiv – CS AI · May 16/10
🧠

AI Models for Depressive Disorder Detection and Diagnosis: A Review

A comprehensive review of 55 studies examines AI methods for detecting and diagnosing Major Depressive Disorder, revealing trends toward graph neural networks for brain connectivity analysis, large language models for linguistic data, and multimodal fusion approaches. The survey highlights how AI can address the subjectivity in clinical depression diagnosis while advancing computational psychiatry through improved explainability and fairness.

AIBullisharXiv – CS AI · Mar 36/108
🧠

MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval

Researchers have developed MED-COPILOT, an AI-powered clinical decision-support system that combines GraphRAG retrieval with similar patient case analysis to assist healthcare professionals. The system uses structured knowledge graphs from WHO and NICE guidelines along with a 36,000-case patient database to outperform standard AI models in clinical reasoning accuracy.

AIBullisharXiv – CS AI · Feb 276/107
🧠

Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots

Researchers developed a framework for analyzing AI diagnostic systems in clinical settings by preserving original AI inferences and comparing them with physician corrections. The study of 21 dermatological cases showed 71.4% exact agreement between AI and physicians, with 100% comprehensive concordance when using structured analysis methods.

← PrevPage 2 of 2