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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Evidence-backed gain

Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout

Importance: While in short supply and high demand, ambulatory care clinicians spend more time on administrative tasks and documentation in the electronic health record than on direct patient care, which has been associated with burnout, intention to leave, and reduced quality of care. Objective: To examine whether ambient AI scribes are associated with reducing clinician administrative burden and burnout. Design, Setting, and Participants: This quality improvement study used preintervention and 30-day postinterv…

JAMA Network Open · Health

Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout

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Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping

Artificial intelligence (AI) has moved from being a specialized technological tool to an intimate presence in everyday life. Smart assistants organize our schedules, predictive systems anticipate our needs, and therapeutic chatbots promise to listen when no human is available (Zhang & Wang, 2024). The diffusion of AI into mental health care is often framed in highly optimistic terms: technologies that reduce stigma, democratize access, and provide affordable, always-on support (M & N, 2025;Sivasubramanian Balasu…

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AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions

Cancer's staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81-0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particu…

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Evidence-backed gain

An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology

BACKGROUND/OBJECTIVES: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. METHODS: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: "cardiovascular disease spectrum", "multi-omics", "precision cardiology", "machine learning", and "artificial intel…

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Evidence-backed problem

Enabling access or automating empathy? Using chatbots to support GBV survivors in conflicts and humanitarian emergencies

Abstract Interest in the use of chatbots powered by large language models (LLMs) to support women and girls in conflicts and humanitarian crises, including survivors of gender-based violence (GBV), appears to be increasing. Chatbots could offer a last-resort solution for GBV survivors who are unable or unwilling to access relevant information and support in a safe and timely manner. With the right investment and guard-rails, chatbots might also help treat some symptoms related to mental health and psychosocial c…

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Evidence-backed gain

Generative AI in healthcare: an implementation science informed translational path on application, integration and governance

BACKGROUND: Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medi…

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Artificial intelligence in undergraduate medical education: an updated scoping review

BACKGROUND: The irrevocable alteration of medical education due to widespread access to large language models (LLMs) in 2022, and the concomitant surge in AI-related literature, has prompted us to update the evolving impact of AI on undergraduate medical education (UGME). METHODS: The scoping review adhered to the framework of Arksey and O'Malley. A literature search was conducted in April 2024 on PubMed, Scopus, Web of Science Core Collection, ERIC, and Google Scholar using the terms "UGME", "medical students",…

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The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysi…

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Ethical and regulatory challenges of large language models in medicine

With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from th…

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Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance

Numerous measures have been proposed to illustrate the performance of predictive artificial intelligence (AI) models. Selecting appropriate performance measures is essential for predictive AI models intended for use in medical practice. Poorly performing models are misleading and may lead to wrong clinical decisions that can be detrimental to patients and increase financial costs. In this Viewpoint, we assess the merits of classic and contemporary performance measures when validating predictive AI models for med…

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Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance

Breaking or Repairing Long-Term Care for Older People?

From robots to chatbots, AI technologies in care (nursing) homes have gained policymakers’ attention amid critical issues like staffing shortages. Concurrently, the long-term care sector has become a prime target for technologists due to its global market potential given the growing ageing population. Drawing conceptually on ideas of breakdown and repair, we explore socio-technical discourses of AI-based care for later life. We combine Bruno Latour’s concept of delegation and Madeleine Akrich’s notion of user re…

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A comprehensive review on application of cognitive behavioral therapy in emotional AI solutions for mental well-being

The integration of Cognitive Behavioral Therapy (CBT) into emotional AI systems has revolutionized the mental health care domain in detecting various issues. This paper reviews the evolution and application of AI agents that leverage CBT to provide personalized, accessible, and effective emotional support. By utilizing CBT methods like cognitive restructuring and guided self-reflection, the agents administer specific therapies that reduce negative thought patterns and enhance emotional responses. This paper outl…

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A comprehensive review on application of cognitive behavioral therapy in emotional AI solutions for mental well-being

Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education

BACKGROUND: Simulated patient systems are vital in medical education and research, providing safe, integrative training environments and supporting clinical decision-making. Progressive Artificial Intelligence (AI) technologies, such as Large Language Models (LLM), could advance simulated patient systems by replicating medical conditions and patient-doctor interactions with high fidelity and low cost. However, effectiveness and trustworthiness remain challenging. METHODS: We developed AIPatient, a simulated pati…

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AI-enabled drug and molecular discovery: computational methods, platforms, and translational horizons

The integration of artificial intelligence (AI) with bioinformatics has initiated a transformative shift in drug discovery, redefining how pharmaceutical research and development are conducted. This review examines both the current state and emerging prospects of AI-driven strategies across the drug discovery pipeline, from target identification and molecular design to clinical applications. Advances in machine learning, deep learning, graph neural networks, transformers, foundation models, and quantum computing…

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ChatGPT Provides Satisfactory but Occasionally Inaccurate Answers to Common Patient Hip Arthroscopy Questions

PURPOSE: To assess the ability of ChatGPT to answer common patient questions regarding hip arthroscopy, and to analyze the accuracy and appropriateness of its responses. METHODS: Ten questions were selected from well-known patient education websites, and ChatGPT (version 3.5) responses to these questions were graded by 2 fellowship-trained hip preservation surgeons. Responses were analyzed, compared with the current literature, and graded from A to D (A being the highest, and D being the lowest) in a grading sca…

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ChatGPT Provides Satisfactory but Occasionally Inaccurate Answers to Common Patient Hip Arthroscopy Questions