#patient-safety News & Analysis

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

AIBearisharXiv – CS AI · Apr 67/10

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When AI Gets it Wrong: Reliability and Risk in AI-Assisted Medication Decision Systems

A research paper examines reliability issues in AI-assisted medication decision systems, finding that even systems with good aggregate performance can produce dangerous errors in real-world healthcare scenarios. The study emphasizes that single incorrect AI recommendations in medication management can cause severe patient harm, highlighting the need for human oversight and risk-aware evaluation approaches.

AIBearishcrypto.news · 5d ago6/10

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AI Therapy Chatbots Face Growing State Bans as Maine Advances Bill and Missouri Follows

Maine and Missouri are advancing legislative bans on AI therapy chatbots, reflecting growing state-level regulatory skepticism toward AI-driven mental health services. This trend signals potential restrictions on a developing sector, though the movement remains fragmented across individual states without federal coordination.

AIBearishThe Register – AI · Mar 47/10

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AI doctor's assistant is easily swayed to change prescriptions, give bad medical advice

Research reveals that AI-powered medical assistant systems can be easily manipulated to change prescriptions and provide harmful medical advice. The study highlights significant vulnerabilities in AI healthcare tools that could pose serious risks to patient safety.

AIBearisharXiv – CS AI · Mar 27/1019

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Beyond Accuracy: Risk-Sensitive Evaluation of Hallucinated Medical Advice

Researchers propose a new risk-sensitive framework for evaluating AI hallucinations in medical advice that considers potential harm rather than just factual accuracy. The study reveals that AI models with similar performance show vastly different risk profiles when generating medical recommendations, highlighting critical safety gaps in current evaluation methods.

AIBearisharXiv – CS AI · Feb 276/107

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ClinDet-Bench: Beyond Abstention, Evaluating Judgment Determinability of LLMs in Clinical Decision-Making

Researchers developed ClinDet-Bench, a new benchmark that reveals large language models fail to properly identify when they have sufficient information to make clinical decisions. The study shows LLMs make both premature judgments and excessive abstentions in medical scenarios, highlighting safety concerns for AI deployment in healthcare settings.