Tag: clinical safety
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Checklist Advances Trustworthy Mental Health Chatbots, Addressing Rising Global Concerns
Beyond Boundaries: Why a Checklist for Mental Health Chatbots Matters As mental health needs rise worldwide, researchers and practitioners are turning to AI-powered chatbots as scalable, accessible support tools. But the rapid deployment of these digital assistants raises critical questions about safety, privacy, efficacy, and equity. A growing body of work, including research from the…
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How Google’s Confident AI Overviews Risk Public Health
Introduction: The rise of AI Overviews in health queries Search engines increasingly rely on artificial intelligence to summarize complex medical topics. When users ask a question like Do I have the flu or COVID? or Why do I wake up tired?, AI-powered Overviews can present concise answers designed to feel confident and definitive. While speed…
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How Google AI Overviews Threaten Public Health and What to Do About It
Introduction: A new frontier in health information Google’s AI-driven features, including what some researchers call AI Overviews, promise quick, authoritative answers to health questions. In theory, a concise AI-generated overview could save time and guide people toward reliable care. In practice, these overviews can unintentionally mislead, oversimplify complex medical guidance, and even influence public health…
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Understanding food challenge risks in asthmatic children
Understanding the risk factors of oral food challenges in asthmatic children Oral food challenges (OFCs) are a cornerstone of diagnosing and managing pediatric food allergies. A recent retrospective study published in Nutrients analyzes 205 OFCs performed in a single pediatric center to understand which preexisting conditions raise the likelihood of a reaction during testing. The…
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Evaluating LLMs and Retrieval-Augmented Generation for Guideline-Adherent Cardiovascular Nutrition Guidance
Introduction: Digital Diet Guidance Meets Cardiovascular Health As digital health tools proliferate, large language models (LLMs) and retrieval-augmented generation (RAG) offer promising avenues to deliver scalable, guideline-based nutrition information for cardiovascular disease (CVD) prevention. Grounded in the American Heart Association’s (AHA) dietary recommendations, these technologies aim to translate complex nutrition science into accessible advice that…
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Enhancing Cardiovascular Nutrition Guidance: A Cross-Sectional Evaluation of LLMs with Retrieval-Augmented Generation
Introduction: The Promise and Peril of AI in Cardiovascular Nutrition As digital health tools proliferate, large language models (LLMs) and generative AI offer the potential to scale evidence-based nutrition education for cardiovascular disease (CVD) prevention. Grounded in the American Heart Association’s (AHA) guideline framework, these technologies aim to improve health literacy while ensuring information reliability.…