Patient safety / Risk Management

Constructing digital professionalism in the generative AI era: A qualitative study with nursing students and faculty.

BACKGROUND: Generative artificial intelligence (AI) is entering coursework, simulation, and assessment in nursing programs. Conventional digital professionalism focused on online conduct now requires visible accountability for algorithmic assistance to protect learning, integrity, and equity. AIM: To explore how pre-licensure nursing students and faculty conceptualize and enact digital professiona...

Patterns of AI Use in Clinical Work by Hospitalists: Survey Study.

BACKGROUND: Artificial intelligence (AI) tools are widely and freely available for clinical use. Understanding hospitalists' real-world adoption patterns in the absence of organizational endorsement is essential for health care institutions to develop governance frameworks and optimize AI integration. OBJECTIVE: The objective of this study was to investigate hospitalists' use of AI, examining the ...

Digital Skills Assessments in Occupational Therapy: A Scoping Review.

IMPORTANCE: Digital skills are increasingly essential in performing daily activities. Occupational therapy practitioners require valid and accessible ...

Impact of quality routine health data utilisation on health service delivery outcomes in low-income and middle-income countries: a systematic review protocol.

BACKGROUND: Evidence-based decision-making in healthcare relies heavily on routine health information. However, in many low-income and middle-income c...

Replication of the Association between Retinal Aging Clock Susceptibility Genes and Retinal Age Gap in an Asian Population: The Nagahama Study.

PURPOSE: To replicate previously reported genetic associations for the retinal aging clock-quantified as the retinal age gap-in an East Asian populati...

Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline.

PURPOSE: To develop the REporting checklist for FoundatIon and large laNguagE models (REFINE), an international reporting guideline for transparent an...

Assessing the completeness of reporting in imaging studies using artificial neural network models for cancer diagnosis: Adherence to the TRIPOD-AI guideline.

BACKGROUND: Developments in artificial neural networks (ANNs) offer significant promise for cancer screening and risk prediction, with the potential t...

Scalable Agile Framework for Execution in AI for Medical AI Ethics Policy Design in Small- and Medium-Sized Enterprises.

Artificial intelligence (AI) is transforming patient care, but it also raises ethical questions, such as bias and transparency. While a range of well-...

Responsible use of large language models in manuscript authorship, peer review, and editorial processes: a Delphi consensus among editors-in-chief of anaesthesia and pain medicine journals (RULE-AP).

This article presents a Delphi consensus developed by a panel of editors-in-chief of anaesthesiology and pain medicine journals to guide the responsib...

Generative artificial intelligence in dementia care: a scoping review protocol on nursing roles, experiences and ethical perspectives.

INTRODUCTION: Generative artificial intelligence (GAI), including large language models and multimodal generative systems, is rapidly emerging in heal...

Development and proof-of-concept of a treatment target recommendation algorithm in the context of cognitive processing therapy.

Background: Cognitive Processing Therapy (CPT) is an effective, widely supported treatment for PTSD, but patient response varies considerably. Optimal...

Accurate Prediction of Humphrey 10-2 Visual Fields in Glaucoma from a Single Rapid IMOvifa 24plus(1-2).

PURPOSE: Predicting the Humphrey Field Analyzer (HFA) 10-2 visual field (VF) using machine learning (ML) based on IMOvifa 24plus(1-2) VF data. DESIGN:...

Neurofeedback for autism spectrum disorder: Current evidence, challenges, and future directions.

Neurofeedback therapy (NFT) has emerged as a promising noninvasive intervention for autism spectrum disorder (ASD), targeting core symptoms such as so...

Novel Systemic Associations of Idiopathic Epiretinal Membrane Identified via Machine Learning.

PURPOSE: To discover novel systemic associations that may lead to idiopathic epiretinal membrane (iERM) using interpretable machine learning models. D...

AI Disclosure Is a Two-Way Street: Commentary on an article by Josh Major, BS, et al.: "Exploring the Endorsement and Implementation of Artificial Intelligence Guidelines in Leading Orthopaedic and Sports Medicine Journals. A Cross-Sectional Study".

Improving Fairness and Mitigating Bias in Multicenter Electronic Health Records Models to Predict Glaucoma Outcomes.

PURPOSE: To evaluate the effectiveness and generalizability of bias mitigation methods in glaucoma progression prediction models across a multicenter ...

The impact of generative AI on social media: an experimental study.

Generative Artificial Intelligence (AI) tools are increasingly deployed across social media platforms, yet their implications for user behavior and ex...

CLAIRE: a unified framework for reporting and assessing artificial intelligence in diagnostic imaging.

Artificial intelligence (AI) models for diagnostic imaging face reproducibility challenges due to inconsistent reporting. Existing guidelines also lac...

Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis.

BACKGROUND: The response of resectable non-small cell lung cancer (NSCLC) to neoadjuvant immunotherapy is heterogeneous. Machine learning can integrat...

Enhancing fairness and standardization in AI-versus-physician diagnostic comparisons: A scoping review.

OBJECTIVE: The growing number of studies directly comparing artificial intelligence (AI) to physicians in diagnostic tasks often focuses on performanc...