JMIR AI

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Artificial intelligence (AI) can enhance diagnostic accuracy, efficiency, and decision-making in health care, but real-world impact depends on practitioners’ acceptance and readiness to use AI in clinical workflows. The United Arab Emirates offers a policy-driven context to study these factors, given active national AI strategies and rapid health system digitization.

Large language models (LLMs) are increasingly used by employees at university hospitals for information retrieval or decision support. Self-hosted on-premise systems provide a secure environment and conform to data privacy and security regulations for handling sensitive personal data. Automation of standard procedures using an LLM application can substantially reduce time-consuming administrative tasks and facilitate the analysis of large datasets.

Accurate tumor node metastasis (TNM) staging is fundamental for treatment planning and prognosis in non-small cell lung cancer (NSCLC). However, its complexity poses significant challenges. Traditional rule-based natural language processing methods are constrained by their reliance on manually crafted rules and are susceptible to inconsistencies in clinical reporting.

Oropharyngeal dysphagia (OD) commonly occurs in patients with COVID-19 disease, posing diagnostic challenges due to isolation protocols.

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.