Prevention of medical errors

Longitudinal Prediction of BMI using Explainable AI: Integrating Polygenic Scores, Maternal, Early-Life and Familial Factors

This study aimed to predict body mass index (BMI) trajectories from childhood to early adulthood using explainable artificial intelligence, integrating polygenic scores (PGS), maternal, early-life, and familial factors to identify key predictors of obesity risk and inform prevention strategies. We analysed longitudinal data from the Raine Study Gen2 cohort, recruiting 2 868 participants. This obse...

Beyond Accuracy: Multidimensional Evaluation of Large Language Models in Hepatocellular Carcinoma Management Emphasizing Prompting

Hepatocellular carcinoma is the most common type of primary liver cancer and remains a major global health challenge. In resource-limited settings, patients often face barriers such as low screening rates, poor adherence, and limited access to medical information. Despite comprehensive clinical guidelines, issues like inadequate patient education and ineffective communication persist. While large ...

Development of a novel musculoskeletal hypothesis using sparse Group Factor Analysis: the ADVANCE cohort

Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly follow...

Evaluating anti-LGBTQIA+ medical bias in large language models

Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While th...

External Validation of a Machine Learning Model to Predict Postpartum Hemorrhage in a US Northeastern Healthcare System

Postpartum hemorrhage (PPH) is a major cause of maternal morbidity and mortality. Timely prediction may prevent adverse maternal outcomes, and efforts...

Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models

Effective risk communication is essential to shared decision-making in prostate cancer care. However, the quality of physician communication of key tr...

Identifying Key Predictive Features for Opioid Use Disorder Using Machine Learning

Opioid Use Disorder (OUD) continues to pose a pressing public health challenge across the United States, highlighting the critical need for early and ...

Artificial Intelligence-Guided Molecular Determinants of PI3K Pathway Alterations in Early-Onset Colorectal Cancer Among High-Risk Groups Receiving FOLFOX

Early-onset colorectal cancer (EOCRC), defined as diagnosis before age 50, is rising rapidly and disproportionately affects high-risk populations, par...

Limited Predictability of Client Attendance in a Support Program for HIV Vertical Transmission Prevention: A Comparison of Machine Learning and Community Health Worker Predictions

Client attendance is vital for the success of HIV vertical transmission prevention programs, yet 23.4% of clients missed follow-up appointments after ...

Clinically meaningful combined improvements of sleep, physical activity, and nutrition (SPAN) in relation to major adverse cardiovascular events

Sleep, physical activity, and nutrition (SPAN) are major modifiable risk factors for cardiovascular disease, yet the minimum and optimal combined impr...

CardiacGPT™: A Real-Time AI Assistant for Intraoperative Guidance and Postoperative Decision Support in Cardiac Surgery

Cardiac surgery is one of the most complex and high-stakes areas of medicine, where intraoperative decisions must be made within seconds and incomplet...

Predictive Modelling’s role in Improving Pre-exposure Prophylaxis (PrEP) Uptake in High-Risk HIV Groups in Africa: An Integrative Scoping Review

This scoping review explores how predictive modelling can strengthen pre-exposure prophylaxis (PrEP) uptake among high-risk populations in Africa, whe...

Enhancing the Reliability of Resting ECGs via Deep Learning–Driven Motion Artifact Detection

This study presents a novel two-stage framework to enhance the reliability of resting electrocardiogram (ECG) signals by addressing motion artifacts t...

Validation of an AI-powered mobile application for personalizing medical note explanations

Almost half of adults struggle to understand written health information, making medical communication a critical barrier to patient care. While AI sho...

Internal and External Validation of Machine Learning Algorithms Versus FINDRISC for Incident Type 2 Diabetes: A Transparent, Explainable Benchmark Using SHAP

Type 2 diabetes mellitus (T2DM) affects almost half a billion people, and the projected cost is $2.25 trillion by 2030; early detection strategies are...

An Artificial Intelligence Approach to Augmentative and Assistive Communication for Patients with Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) progressively impairs motor functions, making communication increasingly difficult for affected individuals. Howev...

Advancing cardiovascular disease risk prediction beyond conventional methods: a systematic review of multimodal machine learning models integrating traditional clinical factors and multi-omics data

Cardiovascular disease (CVD) is a leading global health burden. Traditional risk prediction models, though widely used, often overlook genetic predisp...

AI-Simulated Clinical Consultations: Assessing the Potential of ChatGPT to Support Medical Training

Simulated medical scenarios are useful for evaluating and developing clinical competencies but scheduling them is expensive and time-consuming. Large ...

Improving Doctor-Patient Communication Using Large Language Models - Results from an Experimental Study

Medical jargon poses significant barriers to patient comprehension of healthcare information, potentially affecting treatment adherence and health out...

Enhancing MRI Safety: Real-Time Thermal Imaging Integrated with Deep Learning for Burn Prevention

Radiofrequency (RF)-induced burns are the most common MRI-related adverse event. Standard safety practices such as visual checks and patient communica...