± HEALTH Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine manage…+ HEALTH This multicenter study developed and validated an interpretable machine learning model integrating granular n…+ HEALTH Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find th…+ HEALTH Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microe…+ HEALTH Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for…+ HEALTH Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulat…+ POLICY Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decisio…± HEALTH Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including…
TruaceTracing the truth around AIFriday, August 28, 2026
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877 published stories · page 21 of 59

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Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker for diagnostic accuracy in digital cytology

Background Traditionally, cytology expertise has been equated with professional experience. However, the transition to whole-slide imaging and artificial intelligence (AI) necessitates a shift from exhaustive screening to rapid verification. The goal of this study was to identify cognitive biomarkers associated with diagnostic accuracy and evaluate their modifiability. Methods In phase 1, 100 cytotechnologists with 1-40 years of experience diagnosed 30 digital cytology images using eye-tracking. Gaze metrics acr…

Cancer Cytopathology · Health

Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker for diagnostic accuracy in digital cytology
The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis
Evidence-backed gain

The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis

Objective Evaluate the importance of specific variables contributing to a recently reported Artificial Intelligence (AI) prediction model called Sydney Triage to Admission Risk Tool with Artificial Intelligence (START-AI) to predict inpatient admission from the Emergency Department (ED). Methods A model explainability analysis was undertaken using single-centre ED electronic medical record data over 2 years. The START-AI model, which comprises ensemble machine learning and a transformer-based algorithm to enhanc…

Health
[Artificial intelligence in hypertension: where do we stand?]
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[Artificial intelligence in hypertension: where do we stand?]

Artificial intelligence (AI) is entering the study of hypertension, serving both primarily clinical purposes - to assist practicing physicians - and research objectives. Hypertension presents certain specific characteristics that should be carefully considered when using AI. The measured value of blood pressure is an extremely variable parameter that is difficult to standardize and measure with precision. At present, AI is able to provide very simple, clear, and well-documented answers to clinical questions rega…

Health
Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study
Evidence-backed gain

Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study

Scalable, non-invasive tools are critically needed to improve early lung cancer detection and optimize primary care referral pathways. We evaluated Inflammacheck, a point-of-care device utilizing exhaled breath condensate (EBC) H 2 O 2 and physiological parameters with machine learning for non-invasive lung cancer detection in a real-world screening population. Exhaled Hydrogen Peroxide for Early Lung Cancer Detection (ExPeL) study participants, from the UK Targeted Lung Health Check (TLHC) programme, included i…

Health
Multiomics Profiling Identifies Blood-Based Diagnostic Markers for Sepsis
Evidence-backed gain

Multiomics Profiling Identifies Blood-Based Diagnostic Markers for Sepsis

Sepsis, characterized by a rapid transition to systemic immune dysregulation and multiorgan failure, poses a formidable clinical challenge. The lack of spatiotemporally stable biomarkers severely impedes early diagnosis and risk stratification. By integrating large-scale transcriptomic profiling with machine learning algorithms, this study identified a robust three-gene diagnostic signature (TLR5, HMGB2, and C19orf59). Single-cell RNA sequencing precisely localized the sepsis-induced specific upregulation of the…

Health
Bridging the Gap: Translated Medical Education to Support Cystic Fibrosis Centers From Non-English Speaking Countries
Evidence-backed gain

Bridging the Gap: Translated Medical Education to Support Cystic Fibrosis Centers From Non-English Speaking Countries

Background The European Cystic Fibrosis Society (ECFS) develops education resources to support members; however, these are almost exclusively in English. Many barriers to translation exist, including cost and time. Artificial intelligence (AI) provides an opportunity to support translation and address such barriers. This study aimed to pilot the use of AI-generated translation of ECFS e-learning modules and evaluate the quality. Methods An AI translation program was used to create subtitles of ECFS peer-reviewed…

Education
Large Language Models for Individualized Psychoeducational Tools for Psychosis: A Cross-Sectional Study
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Large Language Models for Individualized Psychoeducational Tools for Psychosis: A Cross-Sectional Study

Objective This study aimed to evaluate the quality of GPT-4-generated responses to commonly asked psychosis-related psychoeducational questions from patients, caregivers and relatives in a first-episode psychosis programme. Evaluation focused on accuracy, clarity, inclusivity, completeness, clinical utility and overall quality. Design This cross-sectional study employed a qualitative evaluation design. GPT-4, accessed via the ChatGPT interface, generated responses to 20 psychosis-related psychoeducational questi…

Health

Advancing Radiology Education with Artificial Intelligence: Curriculum Planning, Implementation, and Evaluation

Artificial intelligence (AI) is rapidly integrating into clinical radiology, creating a continuous emphasis on the necessity of teaching its principles to radiology trainees. However, the potential of AI to transform radiology education remains underexplored. The authors review how AI can be leveraged to enhance radiology education, from curriculum planning to its implementation and evaluation. Guided by Harden's 10-step framework for curriculum development, they systematically examine current and potential futu…

Health
Advancing Radiology Education with Artificial Intelligence: Curriculum Planning, Implementation, and Evaluation

Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome prediction remains challenging. This study systematically evaluated the performance of machine learning (ML) models in predicting key adverse outcomes (hematoma expansion [HE], poor functional outcome, mortality) in ICH, aiming to provide consolidated evidence for future research and clinical translation. We systematically searched PubMed, Embase, Web of Science, and Cochrane Libra…

Health
Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

Classification of tau status with machine learning models in amyloid-positive cohorts

Although tau positron emission tomography (PET) imaging is effective for staging tau pathology, it is limited clinically by cost and availability. Machine learning models based on magnetic resonance imaging (MRI)- and amyloid PET-derived features may serve as useful screening tools for tau pathology. Multiple machine learning models were developed to classify tau positivity in the Braak III/IV region using structural MRI, amyloid PET, and demographic features. Alzheimer's Disease Neuroimaging Initiative (ADNI) (…

Health
Classification of tau status with machine learning models in amyloid-positive cohorts

Black people in the US: have you used AI for health-related questions?

Artificial intelligence is growing in influence in the healthcare space, and more people are seeking guidance from AI tools, including AI-generated social media influencers. Black people in the US, who have been long subject to racism in the healthcare system, are among some of those who are leaning on AI for health-related matters and information. We would like to hear from Black respondents about their experiences with Black AI-generated health and spiritual influencers.

Health
Black people in the US: have you used AI for health-related questions?