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A Conversational Agent for Providing Personalized Preexposure Prophylaxis (PrEP) Support: Protocol for Chatbot Implementation and Evaluation

Background Chatbots have the potential to reduce barriers to preexposure prophylaxis (PrEP), including lack of awareness, misconceptions, and stigma, by providing anonymous and continuous support. However, in the context of PrEP, chatbots are still nascent; they lack personalized informational expertise, peer experiential expertise, and human-like emotional support to facilitate future PrEP uptake. These personalized and relatable forms of support are crucial for increasing engagement, influencing health decisio…

JMIR Research Protocols

Evidence-backed gain

An expert routing-driven graph framework from tongue multi-task features to liver fibrosis prediction

Evidence-backed gain

Cost-Effectiveness of AI in Breast Cancer Screening and Lung Cancer Diagnostics: A Case Study From Costa Rica

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Reliability, Readability, and Safety of Generative AI Chatbots for Osteoporosis Health Counseling: A Multidimensional Cross-Sectional Comparative Study

Evidence-backed problem

Most patients won't endorse AI medical responses without a clinician's review, study finds

In a Columbia University School of Nursing study surveying 1,185 U.S.-based adults about AI use in secure medical portals, 63% of patients said they would not endorse AI-generated medical responses without a clinician's review. This suggests that while patients see value in using AI for certain health care tasks, they prefer a human review when medical advice is involved. The study, "Public Perceptions of Timeliness for AI Use and Human Oversight," was published in the JAMA Network Open on Oct. 14, 2026.

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Artificial Intelligence applied to surgical pathology in the management of cutaneous melanoma

Surgical pathology remains the cornerstone for the diagnosis, staging and prognostic stratification of cutaneous melanoma. The emergence of digital pathology and artificial intelligence (AI), notably deep learning, is transforming this field. Convolutional neural networks trained on large repositories of digitised histological slides demonstrate diagnostic capabilities comparable, and in specific tasks superior, to board-certified dermatopathologists. Current applications encompass the automated classification o…

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AI-assisted characterization of psychedelic clinical trials from ClinicalTrials.gov

Abstract Background The registry record of psychedelic and related rapid-acting compounds is large, heterogeneous and laborious to synthesize by hand. We tested whether a large language model (LLM) with live access to the ClinicalTrials.gov application programming interface (API) could produce a timely, structured and reproducible characterization of this landscape. Methods Cross-sectional analysis of interventional trials on ClinicalTrials.gov as of 13 January 2026. The core-set comprised classic serotonin 5-HT…

Evidence-backed gain

Artificial Intelligence for Cardiac Ischemia Detection: A Review of Biosensor, Biomarker, and Imaging Applications

Ischemic heart disease is the leading cause of global mortality, yet accurate and timely detection remains constrained by incomplete signal capture, noise, operator dependence, and imperfect reference standards. Artificial intelligence (AI) may enhance cardiovascular sensing by extracting latent diagnostic information from routinely acquired signals. This narrative review examines AI-enabled approaches to ischemia detection across three sensing modalities: the electrocardiogram as a bioelectric sensor, cardiac t…

Health

Repurposing a commercial artificial intelligence vision platform for automated surgical instrument identification: a proof-of-concept study

Background: Retained surgical items are classified as never events but continue to occur, and the count meant to prevent them is often recorded as correct even when an item is retained. Computer vision could provide an independent check on what is on the instrument table, but purpose-built surgical systems are scarce. Industrial vision platforms offer a faster route, although their surgical performance is unknown. Methods: In a prospective experimental study, a commercial artificial intelligence vision platform…

Health

Agreement and classification performance of ChatGPT-5-generated and nurse-led emergency department triage using an expert reference decision: A retrospective study

This study evaluated the agreement and classification performance of ChatGPT-5-generated triage compared with nurse triage using an expert reference decision in adult emergency department patients. In this retrospective cross-sectional study, 1564 adult patients with documented yellow or green triage classifications were included. Standardized triage-level patient information was entered into ChatGPT-5 using an identical prompt for all patients. Agreement was assessed using Cohen's κ, and classification performa…

Health

Conceptualising Health Economic Models Using a Graph Retrieval-Augmented, Agentic AI Framework with Human-in-the-Loop Validation: Proof-of-Concept Study

Objective: Health economic models (HEMs) are crucial for assessing the cost-effectiveness of healthcare interventions, informing policy decisions and guiding reimbursement strategies. This study examines the feasibility of employing generative artificial intelligence (Gen-AI) for the conceptualisation of HEMs. Two proof-of-concept studies were conducted, implementing a first-of-its-kind agentic Gen-AI-augmented approach for model conceptualisation. Methods: An agentic Gen-AI framework was developed, incorporatin…

Health

An artificial intelligence algorithm characterizes the deterioration of body composition from systemic therapy for patients with advanced prostate cancer

Skeletal muscle is a major determinant of functional status. The impact of advanced prostate cancer (aPC) systemic therapies on body composition is unknown. We identified aPC patients with serial PET-CTs receiving androgen deprivation therapy (ADT), androgen receptor pathway inhibitors (ARPI), chemotherapy, and/or Lutetium-177. An artificial intelligence algorithm quantified body composition changes from PET-CT images. Linear mixed effects models examined body composition changes over time by treatment type. The…

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Large language models' performance in answering common patient questions about colonoscopy: an expert-based evaluation

Background This study aimed to compare the performance of two large language models (LLMs) the ChatGPT-4 and the Google Bard in delivering medical information about colonoscopy. It also sought to evaluate the models' responses to common patient and caregiver questions in terms of accuracy, applicability, comprehensiveness, and communication quality, as assessed by experts using a rubric-based method. Methods This comparative, expert-based observational study analyzed the responses of both models to 11 colonoscop…

Health

Performance Evaluation of an Artificial Intelligence-Assisted Digital Morphology System for Bone Marrow Aspirate Examination

Introduction: Morphological examination of bone marrow aspirates (BMA) remains essential for the diagnosis of hematologic diseases, but conventional microscopy is labor-intensive and subject to interobserver variability. Methods: A total of 158 samples from patients were analyzed. Differential counts obtained by optical microscopy were compared with those of the CellaVision BMA Application used according to its intended workflow of automated preclassification followed by expert review. The two methods were compa…

Health

Applications of Artificial Intelligence for Facial Aesthetic Surgery: a Systematic Review

Artificial intelligence (AI) offers opportunities to improve assessment, visualization, and communication in facial aesthetic surgery; however, its clinical reliability remains uncertain. This study aimed to synthesize contemporary evidence on AI applications, performance, methodological quality, and clinical relevance in facial aesthetic surgery. A systematic review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines searched PubMed/MEDLIN…