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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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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…

Science & Technology Studies · Health

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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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Evidence-backed gain

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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Evidence-backed problem

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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Both readings

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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Evidence-backed problem

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

BACKGROUND: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine pract…

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A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions

With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes. Considering this rapid technical progress, in this survey, we trace the recent advances of Medical Large Language Models (Med-LLMs), including the background, key findings, and mainstream techniques, especially for the evolution from general-purpose models to…

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User perceptions and experiences of an AI-driven conversational agent for mental health support

Background: The increasing prevalence of artificial intelligence (AI)-driven mental health conversational agents necessitates a comprehensive understanding of user engagement and user perceptions of this technology. This study aims to fill the existing knowledge gap by focusing on Wysa, a commercially available mobile conversational agent designed to provide personalized mental health support. Methods: A total of 159 user reviews posted between January, 2020 and March, 2024, on the Wysa app's Google Play page we…

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A foundation model for clinical-grade computational pathology and rare cancers detection

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achievin…

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A foundation model for clinical-grade computational pathology and rare cancers detection

Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of these data present substantial challenges in data modeling and analysis, which have been addressed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of large language models (LLMs), su…

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Recent Advances in Microfluidic Chip Technology for Laboratory Medicine: Innovations and Artificial Intelligence Integration

Microfluidic chip technologies, also known as lab-on-a-chip systems, have profoundly transformed laboratory medicine by enabling the miniaturization, automation, and rapid processing of complex diagnostic assays using minimal sample volumes. Recent advances in chip design, fabrication methods-including 3D printing, modular and flexible substrates-and biosensor integration have significantly enhanced the performance, sensitivity, and clinical applicability of these devices. Integration of advanced biosensors allo…

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Recent Advances in Microfluidic Chip Technology for Laboratory Medicine: Innovations and Artificial Intelligence Integration

Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges

BACKGROUND: The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies. However, concerns regarding algorithmic accountability, interpretability, and safety have necessitated human oversight in AI systems. Human in the loop artificial intelligence represents a collaborative paradigm where human expertise and machine intelligence converge to enhance decision making while maintaining ethical standards and clinical safety. AIM: This review synthesizes curre…

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The AI-IARA framework: How to cultivate human agency before artificial intelligence optimizes it a(ny)way

We are the last generation that will influence what wellbeing means before AI systems optimize that definition for us. While AI promises unprecedented scalability in mental health support, emerging evidence reveals a troubling pattern: the systems designed to enhance human flourishing may systematically erode the capacities required to achieve. This paper introduces the AI-IARA framework, identifying six irreducible human capacities essential for wellbeing under algorithmic conditions: Awareness (the ability to…

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Hallucinating with AI: Distributed Delusions and “AI Psychosis”

Abstract There is much discussion of the false outputs that generative AI systems such as ChatGPT, Claude, Gemini, DeepSeek, and Grok create. In popular terminology, these have been dubbed “AI hallucinations”. However, deeming these AI outputs “hallucinations” is controversial, with many claiming this is a metaphorical misnomer. Nevertheless, in this paper, I argue that when viewed through the lens of distributed cognition theory, we can better see the dynamic ways in which inaccurate beliefs, distorted memories…

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Hallucinating with AI: Distributed Delusions and “AI Psychosis”

Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook

In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types, including medical images (eg, MRI and CT scans), time-series data (eg, sensor data from wearable devices and electronic health records), audio recordings (eg, heart and respiratory sounds and patient interviews), text (eg, clinical notes and research articles), videos (eg, surgical procedures), and omics data (eg, genomics and prote…

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