#clinical-safety News & Analysis

4 articles tagged with #clinical-safety. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · 5d ago7/10

🧠

Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents

Researchers propose a reinforcement learning framework that enables medical AI agents to achieve synergistic tool use by selecting appropriate diagnostic and treatment tools on a per-instance basis rather than relying on single fixed tools. The approach addresses the critical challenge that individual medical tools frequently fail on difficult cases, which conventional task-level selection cannot overcome, potentially improving safety and reliability in clinical AI systems.

AIBearisharXiv – CS AI · May 17/10

🧠

Auditing Frontier Vision-Language Models for Trustworthy Medical VQA: Grounding Failures, Format Collapse, and Domain Adaptation

Researchers audited five frontier vision-language models (including GPT-5, Gemini 2.5 Pro, and Qwen 2.5 VL) on medical visual question answering tasks and found critical failures in anatomical localization and grounding that pose clinical safety risks. While supervised fine-tuning improved VQA accuracy to 85.5% on benchmark datasets, the underlying perception bottleneck—poor object detection and format compliance issues—remains largely unresolved.

🧠 GPT-5🧠 Gemini

AINeutralarXiv – CS AI · 3d ago6/10

🧠

SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation

Researchers introduce SafeRx-Agent, a multi-agent AI framework designed to improve medication recommendation systems by integrating clinical knowledge, safety verification, and explainability. The system addresses limitations in existing approaches by using fine-grained drug classification (ATC codes) and demonstrating improved accuracy while controlling for drug interactions and contraindications on MIMIC datasets.

AIBearishThe Register – AI · Apr 156/10

🧠

Don't let the bot play doctor! AI gets early diagnoses wrong 80% of the time

A new study reveals that AI diagnostic systems achieve early disease detection accuracy rates of only 20%, getting diagnoses wrong 80% of the time. This significant limitation raises serious concerns about the reliability and safety of deploying AI in critical healthcare applications without substantial improvements.