AIBullisharXiv – CS AI · May 297/10
🧠SURGENT is a multi-agent AI system designed to assist surgical teams throughout the perioperative workflow by combining large language models with specialized reasoning, memory management, and clinical knowledge retrieval. The system addresses critical limitations of standard LLMs—including token constraints and poor context retention—and demonstrates superior performance across five surgical tasks compared to existing medical AI frameworks.
AIBearisharXiv – CS AI · May 287/10
🧠Researchers introduced MedAgentAudit, a framework that reveals critical safety failures in medical multi-agent AI systems, finding that collaborative AI architectures frequently exhibit unsupported observations, evidence avoidance, and decision-making biases rather than genuine reasoning. The study across 14,400 cases and six AI architectures demonstrates that consensus-based medical AI systems are unreliable for clinical use without fundamental process-level improvements.
AINeutralarXiv – CS AI · May 127/10
🧠Researchers have developed AI co-clinician, a multimodal conversational AI system that processes real-time audio and video data to assist with clinical decision-making in telemedicine settings. In simulated consultations with medical residents, the system approached physician-level performance on diagnostic tasks while significantly outperforming text-only AI models, though physicians still maintained superior overall clinical reasoning.
🧠 Gemini
AIBullisharXiv – CS AI · Mar 117/10
🧠Researchers developed Sentinel, an autonomous AI agent that achieves 95.8% emergency sensitivity in clinical triage for remote patient monitoring, outperforming individual clinicians while costing only $0.34 per triage. The AI system addresses the core scalability issues that caused previous remote monitoring trials to fail due to data overload.
AINeutralarXiv – CS AI · Mar 97/10
🧠Researchers evaluated 34 large language models on radiology questions, finding that agentic retrieval-augmented reasoning systems improve consensus and reliability across different AI models. The study shows these systems reduce decision variability between models and increase robust correctness, though 72% of incorrect outputs still carried moderate to high clinical severity.
AIBullisharXiv – CS AI · Mar 37/103
🧠Researchers developed LA-CDM, a language agent that uses reinforcement learning to support clinical decision-making by iteratively requesting tests and generating hypotheses for diagnosis. The system was trained using a hybrid approach combining supervised and reinforcement learning, and tested on real-world data covering four abdominal diseases.
AIBullisharXiv – CS AI · 5d ago6/10
🧠Researchers developed an interpretable AI framework combining deep learning and statistical modeling to predict osteoarthritis features from knee MRIs and identify pain progression patterns. The system achieved significant accuracy improvements and revealed that bone marrow lesions, cartilage loss, and meniscal extrusion are strong predictors of rapid pain progression in osteoarthritis patients.
AINeutralarXiv – CS AI · 5d ago6/10
🧠PAMF is a new machine learning framework that addresses incomplete multimodal time series data in healthcare by distinguishing between two types of missing data patterns and coupling imputation with downstream prediction tasks. The method uses flow matching with type-specific priors and weight sharing to achieve superior performance on healthcare benchmarks compared to existing approaches.
AIBullisharXiv – CS AI · 5d ago6/10
🧠Researchers developed Binary Gaussian Copula Synthesis (BGCS), an LLM-augmented data augmentation method that addresses severe class imbalance in chronic kidney disease datasets to improve early dialysis prediction. Tested on 15,169 CKD patients, BGCS outperformed existing methods like SMOTE and CTGAN, achieving 78-87% minority-class recall and enabling deployment in interpretable clinical decision-support systems.
AINeutralarXiv – CS AI · Jun 36/10
🧠Traj-Evolve introduces a self-evolving multi-agent system that models patient trajectories from longitudinal electronic health records for lung cancer early detection. The system combines an Experience Pool for retrieval-augmented few-shot learning with multi-agent reinforcement learning to optimize collaboration, outperforming nine baselines on both general and never-smoker populations.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers introduce CAREAgent, an AI system designed to generate executable clinical orders by combining structured reasoning with tool integration. The model uses a two-stage training approach combining supervised fine-tuning and reinforcement learning, achieving 5.05% F1 score improvement over existing methods on clinical benchmarks.
AINeutralarXiv – CS AI · Jun 16/10
🧠Researchers introduce FAM-Bench, a multimodal benchmark dataset containing 2,500 expert-verified instances designed to evaluate AI models' ability to assess food suitability for specific health conditions. The benchmark addresses a gap in existing food AI systems by testing health-aware reasoning through dish suitability assessment and comparative analysis tasks across 13 diet-related conditions.
AINeutralarXiv – CS AI · Jun 16/10
🧠SEMA-RAG introduces a multi-agent framework that decouples medical reasoning tasks into three specialized agents to improve retrieval-augmented generation for clinical question answering. The approach achieves 6.46 percentage point accuracy improvements over existing baselines by addressing hallucinations and knowledge obsolescence through iterative, evidence-driven retrieval rather than single-round static lookups.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers have developed a speech analysis framework that uses acoustic and linguistic features to support mental health assessment for depression, anxiety, and ADHD. The approach combines interpretable machine learning with clinically grounded speech markers like prosody and vocal quality, demonstrating consistent relationships between speech patterns and symptom severity across multiple datasets.
AIBullisharXiv – CS AI · May 286/10
🧠A new framework argues that AI in biomedicine is transitioning from predictive systems based on historical data to interventional intelligence that can model biological responses to novel therapies. The shift reflects a fundamental architectural limitation: traditional AI cannot reason about unseen interventions, making disease-level models that simulate outcomes under perturbation essential for clinical decision-making.
AINeutralarXiv – CS AI · May 275/10
🧠Rwanda's healthcare system conducted a stakeholder assessment to evaluate readiness for implementing big data analytics and machine learning in diabetes management. The study identified both opportunities and challenges in deploying these technologies within the country's expanding electronic medical records infrastructure, proposing a practical framework using explainable machine learning models.
AIBullisharXiv – CS AI · May 276/10
🧠Researchers have developed an explainable AI framework that jointly assesses lung and cardiovascular health from low-dose chest CT scans by modeling cross-disease physiological interactions. The system achieves 91.9% AUC for cardiovascular disease screening and outperforms cardiac-specific baselines by explicitly reasoning through pulmonary findings to inform heart risk predictions.
AIBullisharXiv – CS AI · May 126/10
🧠A study demonstrates that interactive dialogue between physicians and large language models significantly improves diagnostic accuracy in emergency medicine, with residents showing a 12.5% improvement on hard cases and standardized metrics confirming medium effect sizes across 52 clinical scenarios.
AINeutralarXiv – CS AI · May 126/10
🧠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
🧠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
🧠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
🧠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.
