+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
TruaceTracing the truth around AIMonday, September 14, 2026
Subscribe

Health

AllDiagnostics & ImagingDrug DiscoveryMental HealthHospitals & Care

454 stories · page 15 of 31

Both readings

AI-based augmentation of oncology clinical trials

Oncology clinical trials are often characterized by slow accrual, high failure rates and limited generalizability, reflecting both biological complexity and operational inefficiencies. Advances in artificial intelligence (AI) - enabled by large-scale electronic health record datasets and machine learning methods - offer new opportunities to address these challenges across the clinical trial lifecycle. In this Review, we discuss applications of AI across pre-trial design, trial conduct, and post-trial inference a…

Nature Reviews Clinical Oncology · Health

AI-based augmentation of oncology clinical trials
Deep learning-based physical exercise assessment of older adults using single-camera videos
Evidence-backed gain

Deep learning-based physical exercise assessment of older adults using single-camera videos

Author summary Staying physically active is essential for older adults to maintain their independence, but many residents in care homes do not receive the individualized exercise supervision they need. We explored how artificial intelligence can help fill this gap. In our study, we developed a computer system that can watch a person exercise through a single video camera and automatically evaluate how well the exercises are performed. Specifically, the system identifies which exercise is being done and estimates…

Health
Patient Harm From Orthodontic Care (Part II): Incident Analysis of Contributory Factors and Perceived Avoidability of Harm
Both readings

Patient Harm From Orthodontic Care (Part II): Incident Analysis of Contributory Factors and Perceived Avoidability of Harm

Background Patient safety, defined as the absence of avoidable harm, in orthodontics is still in its early stages of development. Knowledge on the subject is limited. Aim (1) To conduct an incident analysis, (2) to measure avoidability, (3) to investigate the relationship between contributory factors and perceived avoidability of harm from orthodontic care, and (4) to suggest methods to mitigate potential patient harm incidents. Materials and methods Data were retrospectively collected from consecutively filed o…

Health
Ethical considerations for multimodal artificial intelligence in healthcare
Evidence-backed problem

Ethical considerations for multimodal artificial intelligence in healthcare

Multimodal artificial intelligence (MMAI) is transforming biomedicine by integrating heterogeneous data, e.g., images, speech, behavior, physiological signals, and text, into unified representational spaces. This enables powerful cross-modal inference and data synthesis, with potential gains in diagnostic accuracy, early detection, and patient support. However, these capabilities introduce ethical challenges that exceed existing AI governance frameworks. MMAI can infer sensitive information without patient aware…

Health
First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method
Evidence-backed gain

First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method

Aim This study represents the first methodological stage of a broader AI-assisted Cameriere European dental age estimation workflow. The aim of this first stage was to evaluate the performance of YOLOv8-based deep learning models in automatically detecting the anatomical landmarks and apical structures required for the Cameriere European method. Rather than directly estimating dental age, the proposed system was designed to automate the measurement-related inputs needed for subsequent Cameriere European-based ag…

Health

Data science and AI in medicine and global health: The need for inter-philosophies dialogue, cross-cultural ethics and ecocentricity

Advances in data science and medical artificial intelligence (AI) raise complex philosophical and ethical quandaries about what it means to know a person or a community through data and what kinds of people and societies we are becoming in this era of predictive data science. Drawing on four lightly fictional but reality-informed case studies in mental health, radiology, genomics and environmental public health, we reflect on how AI technologies, largely built on Western biomedical traditions, may conflict with…

Health
Data science and AI in medicine and global health: The need for inter-philosophies dialogue, cross-cultural ethics and ecocentricity

Identification of obesity risk factors in 3-12-year-old children and adolescents with prior respiratory tract infections via interpretable machine and deep learning models

Childhood obesity and respiratory tract infections (RTIs) are 2 major global public health issues that frequently co-occur and are closely interrelated. Early detection of children with prior RTIs who are at high obesity risk is crucial for targeted interventions. This study integrates interpretable machine learning (ML) models and a deep learning network to develop an obesity risk prediction model in a large pediatric cohort. Cross-sectional data from 6509 children and adolescents aged 3-12 years with prior RTI…

Health
Identification of obesity risk factors in 3-12-year-old children and adolescents with prior respiratory tract infections via interpretable machine and deep learning models

Assessing the Diagnostic Performance of ChatGPT-5.0 versus Machine Learning in Orthodontics: A Comparative Analysis for Extraction Treatment Planning

To make accurate orthodontic extraction decisions, various clinical and cephalometric variables must be evaluated. This study aims to evaluate ChatGPT-5.0's performance in distinguishing orthodontic extraction decisions and to compare it with five supervised machine learning (ML) algorithms. Of 550 retrospectively evaluated orthodontic records, 30 were reserved for calibration, leaving 520 for the main analysis. The reference standard was the consensus treatment decision of three expert orthodontists with more t…

Health
Assessing the Diagnostic Performance of ChatGPT-5.0 versus Machine Learning in Orthodontics: A Comparative Analysis for Extraction Treatment Planning

Machine learning-assisted prediction of 5-year mortality in chronic kidney disease: the KoreaN cohort study for Outcome in patients With Chronic Kidney Disease (KNOW-CKD)

Mortality prediction models for patients with non-dialysis chronic kidney disease (CKD) remain limited despite their clinical importance. While machine learning (ML) offers the potential to improve prediction accuracy, its "black-box" nature has hindered clinical adoption. This study aimed to develop and validate an interpretable ML model for predicting 5-year all-cause mortality in patients with non-dialysis CKD and to deploy it as a user-friendly web-based risk stratification tool. We analyzed 1,858 patients (…

Health
Machine learning-assisted prediction of 5-year mortality in chronic kidney disease: the KoreaN cohort study for Outcome in patients With Chronic Kidney Disease (KNOW-CKD)

An unsupervised machine learning analysis of biopsychosocial characteristics and treatment outcome of alcohol use disorder

Background: Alcohol Use Disorder is a heterogeneous condition where standard severity measures often fail to predict individual treatment responses. Precision medicine requires identifying distinct biopsychosocial profiles to guide targeted interventions.Objectives: To identify clinically meaningful Alcohol Use Disorder profiles using k-means clustering based on eight baseline biopsychosocial variables and validate their prognostic utility by comparing treatment outcomes.Methods: A retrospective observational st…

Health
An unsupervised machine learning analysis of biopsychosocial characteristics and treatment outcome of alcohol use disorder

Power, privilege and moral responsibility: learning from I.M. Young's Social Connection Model in the context of AI-driven healthcare

Artificial intelligence (AI) in healthcare is assumed to introduce risks that are not easily addressed by dominant philosophical models for thinking about responsibility. When an AI tool makes an error that results in patient harm, the question of who is responsible is rarely straightforward. Dominant models of responsibility work when harm can be traced to a single actor, but they fail in socio-technical systems where decisions and actions are distributed across multiple human and technological agents. Iris Mar…

Health
Power, privilege and moral responsibility: learning from I.M. Young's Social Connection Model in the context of AI-driven healthcare

'I've talked to ChatGPT about my issues last night.': Examining Mental Health Conversations with Large Language Models through Reddit Analysis

We investigate the role of large language models (LLMs) in supporting mental health by analyzing Reddit posts and comments about mental health conversations with ChatGPT. Our findings reveal that users value ChatGPT as a safe, non-judgmental space, often favoring it over human support due to its accessibility, availability, and knowledgeable responses. ChatGPT provides a range of support, including actionable advice, emotional support, and validation, while helping users better understand their mental states. Ad…

Health
'I've talked to ChatGPT about my issues last night.': Examining Mental Health Conversations with Large Language Models through Reddit Analysis

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis

Background Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated chol…

Health
The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis