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626 stories · page 41 of 42

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Advancing healthcare AI governance through a comprehensive maturity model based on systematic review

Artificial Intelligence (AI) deployment in healthcare is accelerating, yet governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 35 frameworks for AI implementation in healthcare (published 2019-2024), we identified seven critical domains of healthcare AI governance. While existing frameworks provide valuable guidance, the resource requirements create barriers for smaller healthcare organizations. To address this gap, we organized key findings from the revie…

npj Digital Medicine

Evidence-backed problem

ChatGPT Health performance in a structured test of triage recommendations

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The role of agentic artificial intelligence in healthcare: a scoping review

Evidence-backed problem

Training language models to be warm can reduce accuracy and increase sycophancy

Evidence-backed problem

From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms to Foster Dignified Human-AI Interaction

In the future of work discourse, AI is touted as the ultimate productivity amplifier. Yet, beneath the efficiency gains lie subtle erosions of human expertise and agency. This paper shifts focus from the future of work to the future of workers by navigating the AI-as-Amplifier Paradox: AI’s dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise. We present a year-long study on the longitudinal use of AI in a high-stakes workplace among cancer specialists. In…

Evidence-backed problem

Large Language Model Performance and Clinical Reasoning Tasks

Importance: Large language models (LLMs) are increasingly marketed for clinical use, yet their ability to replicate full-spectrum clinical reasoning remains uncertain. Existing evaluations often rely on multiple-choice examinations that do not reflect the complexity of patient care. Objectives: To evaluate the longitudinal clinical reasoning ability of state-of-the-art LLMs and to introduce a multidimensional, clinically meaningful benchmark for clinical-grade artificial intelligence (AI). Design, Setting, and P…

Evidence-backed gain

ZigBee Based Low Latency IoT and AI Integrated Framework for Real Time Telehealth Monitoring

The Internet of Things (IoT) and Artificial Intelligence (AI) have opened up new frontiers in remote health monitoring with the integration of technologies and transformative solutions in order to detect real-time health monitoring and disparities. This article shows an innovative and integrated wireless health surveillance system, which is aimed at auxiliary environments, especially for elderly and chronically ill patients. The system links IoT sensors to monitor heart rate, body temperature, and oxygen level w…

Evidence-backed problem

Who Is in the Room? Stakeholder Perspectives on AI Recording in Pediatric Emergency Care

Artificial intelligence systems that record voice and video during pediatric emergencies are emerging as human-computer interaction (HCI) technologies with direct implications for clinical work, promising improvements in documentation, team performance, and post-event debriefing. Yet the perspectives of those most affected, including clinicians, parents, and child patients, remain largely absent from the design and governance of these technologies. This position paper argues that this has direct consequences for…

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Young people’s perceptions and recommendations for conversational generative artificial intelligence in youth mental health

Conversational generative artificial intelligence agents (or genAI chatbots) could benefit youth mental health, yet young people's perspectives remain underexplored. We examined the Mental health Intelligence Agent (Mia), a genAI chatbot originally designed for professionals in Australian youth services. Following co-design, 32 young people participated in online workshops exploring their perceptions of genAI chatbots in youth mental health and to develop recommendations for reconceptualising Mia for consumers a…

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Ethical Concerns in Medical and Health-Related AI

This perspective introduces the range of ethical concerns entailed by the widespread adoption of AI, particularly as they impact human health. It begins by (1) illustrating risks associated with all large-scale AI systems, then moves to (2) corporate and governmental applications of AI that affect human health. It overviews the ways (3) that patient usage of AI has affected human health; (4) that “passive” medical AI (like recording documents) and (5) “active” medical AI (like diagnosing and prescribing) may aff…

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Leveraging social media footprints for predicting college student anxiety: a machine learning approach

Background Anxiety is one of the most prevalent mental health concerns among college students worldwide, yet traditional assessment methods relying on self-report questionnaires are time-consuming, susceptible to response bias, and difficult to scale. Social media platforms, which students use extensively, generate rich behavioral and linguistic data that may reflect underlying psychological states. This study investigates whether passively collected social media footprints are associated with anxiety scores amo…

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Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective

Artificial intelligence (AI) is increasingly being integrated into healthcare, particularly in data-intensive chronic diseases that rely on longitudinal monitoring and shared decision-making. Multiple sclerosis is a prototypical example of such care, but real-world benefit will depend on whether people accept AI support in different clinical roles. We conducted a cross-sectional, web-based survey among 241 people with MS (pwMS) to assess comfort with AI across eight clinical domains and to identify predictors of…

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Key challenges for delivering clinical impact with artificial intelligence

BACKGROUND: Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. MAIN BODY: Key challe…

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The Ethical and Legal Complexities of Regulating Companion AI Chatbots

Companion AI chatbots are increasingly used to provide friendship, emotional support, and quasi-romantic relationships, with reported benefits for loneliness and mental health. At the same time, recent suicides and other serious harms allegedly linked to such systems expose gaps in existing ethical and legal frameworks. This article interrogates these gaps through four lenses: anthropomorphism,...

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Blue Ortho 22 Chemin Du Vieux Chene Meylan France recalls an AI-enabled medical device

Predict+ is a data-driven, clinical decision support tool that uses machine learning to provide predictions of individual patient outcomes after shoulder replacement surgery Recall reason: Predict+ is being recalled for lack of pre-market clearance/approval.