Truace
Subscribe

Health

626 stories · page 3 of 42

Repurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study
HealthEvidence-backed gain

Repurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study

Despite growing clinical adoption of artificial intelligence in radiology, fewer than one in five commercially available models are cleared for paediatric use. Existing systems are trained predominantly on adult data and generalize poorly to neonates, yet efficient strategies for repurposing them without data sharing or large-scale local data curation are lacking. Here we present an international retrospective study of neonatal chest radiographs from 30 neonatal intensive care units across 20 countries and terri…

Nature Biomedical Engineering
Machine learning-based source-level EEG functional network biomarkers of antidepressant treatment response in major depressive disorder
Evidence-backed gain

Machine learning-based source-level EEG functional network biomarkers of antidepressant treatment response in major depressive disorder

Major depressive disorder (MDD) is highly prevalent, yet antidepressant selection remains largely trial and error because of limited predictive biomarkers. Resting-state electroencephalography (EEG) combined with graph theory-based functional networks offers a noninvasive approach to probing brain dysconnectivity. This study aimed to delineate source-level functional network differences between antidepressant responders and non-responders, and to evaluate whether machine learning models using nodal network featu…

Automating Injury Severity Score Calculation From Free-Text Discharge Diagnoses Using Domain-Specific Retrieval-Augmented Large Language Models: Retrospective Validation Study
Both readings

Automating Injury Severity Score Calculation From Free-Text Discharge Diagnoses Using Domain-Specific Retrieval-Augmented Large Language Models: Retrospective Validation Study

Background: Trauma registries are crucial for evaluating the prognosis of injuries and the quality of trauma care. However, the calculation of Injury Severity Score (ISS) remains a predominantly manual process, which is labor-intensive, potentially error-prone, and difficult to scale. Automating this process is a critical step toward enhancing the efficiency and quality of trauma registry data. Objective: This study conducts a feasibility test on the use of large language models (LLMs) to automatically calculate…

The OR black box in urology: A survey on patient acceptance and confidence in AI-assisted surgery
Evidence-backed gain

The OR black box in urology: A survey on patient acceptance and confidence in AI-assisted surgery

Objectives This study aims to assess patient acceptance of intraoperative video recording with Operating Room (OR) black box technology, characterise consent conditions and use-case preferences, and identify predictors of patient confidence in artificial intelligence (AI)-assisted surgery in urological patients. Patients and methods This prospective cross-sectional survey was conducted at the urology department of a university hospital from March 2024 to March 2025. Consecutive adults scheduled for elective proc…

Evaluation of six generative large language models answering pre-analytical questions in medical microbiology
Evidence-backed problem

Evaluation of six generative large language models answering pre-analytical questions in medical microbiology

Objectives: Large language models (LLMs) have the potential to be integrated into diagnostics workflows, yet their reliability in pre-analytical diagnostic stewardship remains uncertain. We aimed to evaluate the performance of six LLMs answering common pre-analytical questions in the domain of medical microbiology. Next, we propose a blueprint to evaluate LLMs in medical microbiology. Methods: We compared general-purpose models from OpenAI (GPT-3.5, GPT-4) and Meta AI (Llama2-7B, Llama2-13B), alongside two domai…

Health

Comparing Diagnostic Ability of Two Off-the-Shelf Large Language Models with That of Endodontists for Teeth Requiring Initial Root Canal Treatment: A Multicenter Retrospective Investigation

Introduction: Despite growing interest in large language models (LLMs) for endodontic diagnosis, prior studies have used simulated cases, examination formats, or small selected series. This multicenter retrospective study compared the diagnostic accuracy of ChatGPT-5.2 and Gemini 3.0 with that of calibrated endodontists across 645 teeth requiring initial root canal treatment. Methods: Cases were evaluated across four endpoints: pulpal diagnosis, periapical diagnosis, treatment planning, and case difficulty. Stan…

Comparing Diagnostic Ability of Two Off-the-Shelf Large Language Models with That of Endodontists for Teeth Requiring Initial Root Canal Treatment: A Multicenter Retrospective Investigation
Health

Multimodal artificial intelligence in dental diagnosis: influence of reasoning depth and domain-specific medical model on diagnostic performance

Large language models are increasingly explored as clinical decision-support tools, but the relationship between configurable reasoning effort and multimodal diagnostic performance remains unclear. This study evaluated the association between API-configured reasoning effort and multimodal oral and dental diagnostic performance in GPT-5 and compared its outputs with those of the compact open-weight medical-domain model MedGemma-1.5-4b-it. Eight anonymized multimodal case reports representing dental trauma, odonto…

Multimodal artificial intelligence in dental diagnosis: influence of reasoning depth and domain-specific medical model on diagnostic performance
Health

OsteoCHAT: real-world patient evaluation and benchmarking of a guideline-grounded osteoporosis chatbot

Patients with osteoporosis have information needs that routine care inconsistently addresses. In this multicenter real-world evaluation, the LLM-based, guideline-grounded chatbot OsteoCHAT showed high participant acceptability and achieved expert-rated answer quality comparable to general-purpose LLMs. Despite challenges in complex treatment and safety-related topics, guideline-grounded chatbots may represent useful educational adjuncts. Purpose Patients with osteoporosis have information needs that routine care…

OsteoCHAT: real-world patient evaluation and benchmarking of a guideline-grounded osteoporosis chatbot
Health

Discovery of dietary polyphenols targeting the autoinducer-2 quorum sensing system of pathogenic bacteria

Targeting autoinducer-2 (AI-2) quorum sensing (QS) systems with dietary compounds represents a promising strategy to combat pathogens, yet mechanisms remain elusive. Here, we develop a machine learning-driven framework combining computational screening with multi-level experimental analysis to identify AI-2 quorum sensing interference molecules (QSIMs). A graph neural network (GNN)-based classifier (AI2IMpred) screens over 7000 phytochemicals and identifies dietary polyphenols as potent QSIMs. Using Salmonella T…

Discovery of dietary polyphenols targeting the autoinducer-2 quorum sensing system of pathogenic bacteria
Health

Generative Artificial Intelligence Use Among Medical Students: Learner Perspectives for Clinical Educators

Background: Generative artificial intelligence (AI) is readily available to medical students, yet educators may not know how students use these tools or what guidance students perceive as missing. We examined student-reported AI use, confidence, concerns and institutional support to inform practical guidance. Methods: We conducted an online survey of medical students recruited primarily through US allopathic medical school peer networks from 15 July to 1 September 2025. We used non-probability sampling; because…

Generative Artificial Intelligence Use Among Medical Students: Learner Perspectives for Clinical Educators
Health

Assessing AI Feasibility for Pain Evaluation in Hospitalized Cognitively Impaired Older Adults

Purpose The objectives for this study were to examine the acceptability and feasibility of using the artificial intelligence-driven application PainChek Ⓡ to identify pain within older adults with cognitive impairment in the inpatient setting within Ontario. Design A prospective cohort design was used. Methods Six nurses were recruited and completed standardized training. Following the recruitment and onboarding of nurse participants, 25 patient participants were enrolled and assessed using the application. Once…

Assessing AI Feasibility for Pain Evaluation in Hospitalized Cognitively Impaired Older Adults
Health

Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review

Machine learning (ML) informs nursing practice, but prediction cannot establish intervention or workflow effects. Aim To map ML use within causal effect estimation and causal evaluations of ML-enabled interventions in nursing and midwifery. Eight electronic sources were searched for reports published January 2015-June 2026, followed by backward citation searching. Eligibility required a causal estimand, corresponding identification strategy, substantive ML role, and nursing or midwifery relevance. Two reviewers…

Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review