Patient safety / Risk Management

Possible misplaced perception of moral shame in AI-use disclosure.

The concept of a moral shame associated with the disclosure of artificial intelligence (AI) use in research, as articulated by Bao and Zeng ("AI disclosure, moral shame, and the punishment of honesty. Accountability in Research, https://doi-org/10.1080/08989621.2025.2542197) is in line with other related notions such as AI guilt, and calls for improvements in AI-use policies and guidelines. Here, ...

Patient Perceptions and preferences for the disclosure of artificial intelligence generated draft replies to electronic messages - A qualitative study.

BACKGROUND: Since their public release, artificial intelligence-generated draft replies (GDRs) to patient portal messages have been rapidly adopted across healthcare systems. Concurrently, debate has arisen regarding the extent to which GDRs should be disclosed to patients. This qualitative interview study, using vignettes, examines adult patients' reactions to and preferences regarding written AI...

Toward an Ethical Framework for Patient-Facing Artificial Intelligence in Clinical Settings.

Patient-facing artificial intelligence in clinical settings raises distinct ethical challenges that require explicit attention as patients transition ...

Expert Evaluation of an AI Governance and Ethics Framework Implemented at a University Hospital.

BACKGROUND: Artificial intelligence (AI) is increasingly becoming an integral part of everyday life, including in the healthcare sector. When used in ...

CONORM-DEID: Robustness Evaluation of a Multilingual De-Identification System for Clinical Texts.

Clinical narratives contain rich, detailed information that is essential for medical research but often locked behind privacy constraints due to the p...

Assessing Clinical Decision-Making Aided by a RAG-Based Dialog in Pre-Examination on Non-Odontogenic Tooth Pain.

Differentiating Non-Odontogenic Tooth Pain, a potential symptom of life-threatening conditions like Ischemic Heart Disease, is a critical challenge fo...

TrainTracks - federated learning for reproducible research on sensitive medical data.

BACKGROUND: Reproducibility of computational algorithms is a challenging but crucial requirement for medical research and an important component of tr...

AJNR Study-Specific Guidelines for AI in Medical Imaging: Bridging Gaps in Reference Standard and Clinical Evaluation.

The rapid proliferation of artificial intelligence (AI) applications in neuroradiology can lead to heterogeneous study design and reporting that imped...

Quantitative Analysis of Retinal Fluid by a Deep Learning Model in Uveitic Macular Edema.

OBJECTIVE: To assess whether artificial intelligence (AI)-derived fluid volume provides prognostic value for visual outcomes in uveitic macular edema ...

Validity of AI-generated multiple-choice questions in medical education: a systematic review.

Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs) in medical education. We conducted a systematic review...

Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.

Timely activation of massive hemorrhage protocols (MHP) is critical to prevent exsanguination and improve survival in trauma patients. Current clinica...

Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data.

BACKGROUND: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD)....

Machine Learning to Predict Outcomes of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration.

OBJECTIVE: To develop machine learning models using OCT fluid metrics to predict long-term anti-VEGF treatment intensity and visual acuity (VA) outcom...

Machine Learning-Based Prediction of Long-Term Intraocular Pressure Fluctuations in Open-Angle Glaucoma.

OBJECTIVE: To investigate the predictability of long-term intraocular pressure (IOP) fluctuations in open-angle glaucoma eyes implanted with a telemet...

Evaluating the Accuracy, Quality, and Reproducibility of AI Chatbot-Generated Diabetes Self-Management Education and Support Plans Aligned With ADCES7.

BACKGROUND: The rapid development of artificial intelligence, particularly large language models (LLMs) such as ChatGPT, Gemini, and Claude, offers ne...

Using large language models for automated assessment of reporting quality and completeness of prediction model studies.

BACKGROUND AND OBJECTIVES: Transparent and complete reporting in scientific papers is important for interpretation of study results and for downstream...

Evaluating the Methodological Quality of Artificial Intelligence-Assisted Systematic Reviews: Protocol for a Mixed Methods Meta-Research Study.

BACKGROUND: Artificial intelligence (AI), including large language models (LLMs), is increasingly integrated into systematic review (SR) workflows. AI...

Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals' perspectives.

Explainable Artificial Intelligence (XAI) has the potential to enhance clinical decision support (CDS) systems however, it remains unclear how XAI sys...

Equitable Digital Frailty Screening for Marginalized Older Adults Using Audio Computer-Assisted Self-Interview: Collaborative Development Guide and User Testing Study.

BACKGROUND: Older adults facing social or structural marginalization for reasons such as lower literacy, digital exclusion, financial constraints, res...

Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning.

OBJECTIVE: To evaluate the impact of training and testing deep learning (DL) models for visual field (VF) forecasting using input-target pairs in whic...