JMIR AI

Translating evidence-based therapies from “bench to bedside” remains challenging, and implementation science (IS) experts are crucial for this process. Qualitative analyses are essential, but require extensive time and cost for manual coding. Now, many turn to artificial intelligence (AI) to accelerate the pace of qualitative analysis, but significant questions remain about the quality, validity, and ethics of applying large language models like ChatGPT (OpenAI) to qualitative data. To this end, we have developed a method for AI-assisted rapid qualitative analysis that addresses these concerns.

Effective patient education is crucial in preventing venous thromboembolism (VTE), improving patient outcomes, and reducing health care costs. However, traditional educational methods often lack engagement and fail to address individual patient needs comprehensively.

Artificial intelligence (AI) technologies continue to transform how we research human disease, diagnose and treat patients, and operate hospitals. However, emerging ethical dilemmas surrounding their design, use, and oversight demand both policy attention and empirical research.

Health disparities such as morbidity and mortality among childbearing women remain high in the United States, especially among those with risks associated with criminal legal system involvement. These underserved women are often managed through community supervision such as probation. They have many needs and could benefit from easily accessible mobile health (mHealth) apps that specifically target their health and safety using artificial intelligence (AI).

Machine learning (ML) can be used to predict clinical outcomes. Training predictive models typically requires data for hundreds or thousands of patients. Lowering this requirement to a few tens of patients would enable new applications in clinical trials (eg, optimizing the design of a phase III trial by training a predictive model on phase II data and applying it to synthetic phase III patients) or in clinical decision support systems (for rare diseases or narrow indications). Large language models (LLMs) have recently been shown to outperform conventional ML algorithms for predictions on tabular data when the train dataset is small.

The adoption of artificial intelligence (AI) in health care has accelerated; however, physicians continue to face substantial legal, ethical, and regulatory uncertainties when considering AI integration into clinical practice. Although the literature on AI in health care is expanding, there is limited insight into the real-world concerns voiced by clinicians navigating these uncharted territories.

Over half of US adults with Alzheimer disease and related dementias (ADRD) remain undiagnosed. Speech-based screening algorithms offer a scalable approach, but the relative value of large language model (LLM) adaptation strategies is unclear.

Fuzzy logic has been progressively investigated as a viable alternative to traditional statistical and machine learning methods in health care modeling, especially in environments marked by uncertainty, nonlinearity, and missing information. Although its use in prediction, classification, and risk stratification is well established, its application to explicit causal inference remains limited, varied, and methodologically premature.

The workload that stems from writing clinical histories is one of the main sources of stress and overload for primary care professionals, accounting for up to 43% of the working day. The introduction of technology, specifically artificial intelligence (AI), in the field of health could significantly reduce the time spent writing clinical reports without compromising the quality of care.

Despite the growing potential of large language models (LLMs) in mental health services, evidence on its capabilities in diagnostic processes remains limited.

Antimicrobial resistance (AMR) poses a critical global health threat, undermining the efficacy of antibiotics and complicating clinical decision-making. Although scientific literature on AMR is extensive, retrieving and synthesizing relevant evidence remains time-consuming for clinicians and researchers. Recent advances in large language models (LLMs) offer opportunities to enhance access to domain-specific knowledge. However, the diversity of available models, ranging from open-source to commercial, necessitates a systematic comparison of their performance, cost, and scalability in real-world biomedical applications.

evaluation (ISE) methods create a digital twin or a computer simulation of actual care pathways, enabling a broader assessment of the potential impact of algorithm-based clinical decision support systems (CDSS) before implementation. A programmatic search of several academic research databases showed at least 886 CDSS development and evaluation studies in the past 3 decades. However, fewer than 3% applied ISE to evaluate the potential impact on broader clinical care pathways.