AI Medical Compendium Journal:
JMIR formative research

Showing 21 to 30 of 71 articles

Using a Hybrid of AI and Template-Based Method in Automatic Item Generation to Create Multiple-Choice Questions in Medical Education: Hybrid AIG.

JMIR formative research
BACKGROUND: Template-based automatic item generation (AIG) is more efficient than traditional item writing but it still heavily relies on expert effort in model development. While nontemplate-based AIG, leveraging artificial intelligence (AI), offers...

AI-Enhanced Virtual Reality Self-Talk for Psychological Counseling: Formative Qualitative Study.

JMIR formative research
BACKGROUND: Access to mental health services continues to pose a global challenge, with current services often unable to meet the growing demand. This has sparked interest in conversational artificial intelligence (AI) agents as potential solutions. ...

Perceived Trust and Professional Identity Threat in AI-Based Clinical Decision Support Systems: Scenario-Based Experimental Study on AI Process Design Features.

JMIR formative research
BACKGROUND: Artificial intelligence (AI)-based systems in medicine like clinical decision support systems (CDSSs) have shown promising results in health care, sometimes outperforming human specialists. However, the integration of AI may challenge med...

Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study.

JMIR formative research
BACKGROUND: Medical abstract sentence classification is crucial for enhancing medical database searches, literature reviews, and generating new abstracts. However, Chinese medical abstract classification research is hindered by a lack of suitable dat...

Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study.

JMIR formative research
BACKGROUND: Recent advancements in rehabilitation sciences have progressively used computational techniques to improve diagnostic and treatment approaches. However, the analysis of high-dimensional, time-dependent data continues to pose a significant...

Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models.

JMIR formative research
BACKGROUND: Rapid integration of large language models (LLMs) in health care is sparking global discussion about their potential to revolutionize health care quality and accessibility. At a time when improving health care quality and access remains a...

Machine Learning-Based Prediction of Delirium and Risk Factor Identification in Intensive Care Unit Patients With Burns: Retrospective Observational Study.

JMIR formative research
BACKGROUND: The incidence of delirium in patients with burns receiving treatment in the intensive care unit (ICU) is high, reaching up to 77%, and has been associated with increased mortality rates. Therefore, early identification of patients at high...

The Feasibility of Large Language Models in Verbal Comprehension Assessment: Mixed Methods Feasibility Study.

JMIR formative research
BACKGROUND: Cognitive assessment is an important component of applied psychology, but limited access and high costs make these evaluations challenging.

Exploring the Effect of an 8-Week AI-Composed Exercise Program on Pain Intensity and Well-Being in Patients With Spinal Pain: Retrospective Cohort Analysis.

JMIR formative research
BACKGROUND: Spinal pain, one of the most common musculoskeletal disorders (MSDs), significantly impacts the quality of life due to chronic pain and disability. Physical activity has shown promise in managing spinal pain, although optimizing adherence...

Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study.

JMIR formative research
BACKGROUND: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial costs. While protocols exist to reduce wrong-site surgery, underreporting and inconsistent documentatio...