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1289 published stories · page 13 of 86

Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care
HealthEvidence-backed gain

Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care

Chronic kidney disease (CKD) presents a growing public health challenge in China, exacerbated by low patient awareness and limited nephrology resources. This study evaluated the potential of large language models (LLMs) to support CKD diagnosis in primary care using time-series electronic health record (EHR) data. Longitudinal data were extracted from the EHR system of Weinan, China. Among 29 963 adults meeting inclusion criteria, 2300 participants were randomly sampled to generate clinical vignettes. CKD status…

BMJ Health & Care Informatics
Do bots provide correct and adequate guidance regarding acidity: A blinded comparison rated by patients and physicians
Evidence-backed problem

Do bots provide correct and adequate guidance regarding acidity: A blinded comparison rated by patients and physicians

Background Large language models (LLMs) are increasingly accessed by patients for gastrointestinal health information. Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs. Aim To assess the responses generated by ChatGPT-5, Gemini-2.5, and Claude-4 for common patient questions on "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease). Methods Thirty-nine frequently asked questions were submitted to each model. Responses w…

Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application
Evidence-backed problem

Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application

Artificial intelligence in ophthalmology encounters a continual challenge: Systems proficient in picture classification seldom yield quantifiable enhancements in patient outcomes. The primary concern is the disparity between pixel-level performance metrics and their clinical significance. Primary obstacles encompass data bias, domain shift, and label noise, exacerbated by the lack of prospective, randomized deployment trials. The frequent disregard for patient-centered objectives, cost-effectiveness, and equity…

Inogen one g5 oxygen concentrator: FDA malfunction report involving AI
Evidence-backed problem

Inogen one g5 oxygen concentrator: FDA malfunction report involving AI

The FDA received a malfunction report involving Inogen one g5 oxygen concentrator, made by Inogen, Inc.. Received from Apria Healthcare under Medicare. 20 minutes - unit powered off - no oxygen.Called Inogen - "not their problem - call Apria Healthcare". Said I would file because it is2 XS same problem with same unit model Inogen G5. We are awaiting anotheroxygenator portable now - stuck in house now for 3rd day with no back up option. /Product details: Had 2 Inogen G5 units delivered & replaced- worked 20 min t…

Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis
Evidence-backed gain

Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis

Objective We systematically evaluated the diagnostic performance of artificial intelligence (AI)-assisted interpretation versus independent physician assessment for fracture detection. Materials and methods Adhering to PRISMA-DTA guidelines, we searched PubMed and Web of Science for original studies published up to September 17, 2025. Quality was assessed utilizing the QUADAS-3 framework. A bivariate random-effects model pooled diagnostic metrics. Accuracy was assessed by summary receiver operating characteristi…

Health

Artificial Intelligence in Extended Reality and Digital Rehabilitation Technologies for Fall Prevention in Older Adults: A Rapid Review of the Evidence

BackgroundFall-related injuries in older adults remain a major health concern, contributing to functional dependence and healthcare losses. Traditional prevention programs, while effective, remain underutilized due to poor adherence and participation. Extended reality (XR) technologies, combined with artificial intelligence (AI), are emerging as promising solutions to mitigate fall-related outcomes.ObjectiveTo evaluate existing literature on extended (XR) and virtual reality (VR) based interventions and the role…

Artificial Intelligence in Extended Reality and Digital Rehabilitation Technologies for Fall Prevention in Older Adults: A Rapid Review of the Evidence
Climate

Gene-environment interactions shape cytokine-mediated inflammation and cardiovascular risk

Background Chronic inflammation is a major driver of cardiovascular diseases, but mechanisms linking systemic inflammation to cardiac remodelling remain incompletely understood. We aimed to evaluate the role of cytokine-mediated signalling and the influence of environmental and genetic factors on inflammation and cardiac phenotypes in a large population-based cohort. Methods We analysed subsets of 488 079 UK Biobank participants with metabolomic and proteomic profiling, cardiac magnetic resonance (CMR) imaging,…

Gene-environment interactions shape cytokine-mediated inflammation and cardiovascular risk
Health

Can't see the forest for the trees? Statistical considerations for disease macroecology

Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk. These datasets span local to global host-parasite interactions and often integrate diverse host or parasite traits across evolutionary histories. Selecting appropriate statistical approaches requires first asking key questions about the study system: How well is the system understood? How important is predictor accuracy? How much bias is present…

Can't see the forest for the trees? Statistical considerations for disease macroecology
Health

Large Language Model Data Abstraction Demonstrates Accuracy and Reliability for NSQIP

Background National Surgical Quality Improvement Program (NSQIP) data collection depends on labor-intensive manual chart abstraction, limiting efficiency, increasing cost, and necessitating patient sampling. This study evaluated whether a large language model (LLM) could accurately abstract unstructured NSQIP breast reconstruction variables compared with conventional human abstraction. Study design Clinical notes from patients enrolled in the NSQIP Breast Reconstruction pilot program (July 1, 2024-February 28, 2…

Large Language Model Data Abstraction Demonstrates Accuracy and Reliability for NSQIP
Health

Artificial Intelligence-Based Hypernasality Diagnosis Using CAPS-A-AM Rated Speech Samples in Pediatric Velopharyngeal Dysfunction

ObjectivePerceptual evaluation by speech-language pathologists (SLPs) is essential for initial evaluation of velopharyngeal dysfunction (VPD). Machine learning (ML) offers a promising avenue for developing accessible speech assessment tools when SLP expertise is limited. We aimed to develop a workflow and preliminary algorithm for AI-based hypernasality detection based on the Cleft Audit Protocol for Speech-Augmented-Americleft Modification (CAPS-A-AM), a standardized framework for auditory perceptual speech ass…

Artificial Intelligence-Based Hypernasality Diagnosis Using CAPS-A-AM Rated Speech Samples in Pediatric Velopharyngeal Dysfunction
Health

Multivariate Machine Learning Model for Long-Term Risk Prediction of Acute Coronary Syndrome in Patients With Heart Failure With Preserved Ejection Fraction and Obstructive Sleep Apnea

Background Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructive sleep apnea further increases the risk of acute coronary syndrome. However, effective tools for long-term acute coronary syndrome risk stratification in this population remain limited. This study aimed to develop and externally validate a machine learning-based prognostic model for predicting acute coronary syndrome risk at multiple time points in patients with heart failure with preserved ejection fra…

Multivariate Machine Learning Model for Long-Term Risk Prediction of Acute Coronary Syndrome in Patients With Heart Failure With Preserved Ejection Fraction and Obstructive Sleep Apnea
Health

Artificial Intelligence in Endohepatology: Toward an Intelligent One-Stop Shop for Liver-Directed Endoscopy

Endohepatology (the implementation of contemporary advanced endoscopy in hepatology) has reached a point where endoscopic ultrasound (EUS)-guided liver biopsy, portal pressure gradient measurement, parenchymal elastography, and variceal screening and therapy can be integrated into a single procedural session of liver-directed endoscopy. Concurrently, artificial intelligence (AI) has revolutionized luminal endoscopy and is advancing rapidly across hepatology imaging, digital pathology, and outcome prediction, yet…

Artificial Intelligence in Endohepatology: Toward an Intelligent One-Stop Shop for Liver-Directed Endoscopy