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There is no guidance to support the reporting of systematic reviews of mobile health (mhealth) apps (app reviews), so authors attempt to use/modify the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). There is a need for reporting guidance, building on PRISMA where appropriate, tailored to app reviews. The objectives were to describe the reporting quality of published mHealth app reviews, identify the need for, and develop potential candidate [...]

The continuous integration of artificial intelligence (AI) into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations.

Statistical and artificial intelligence algorithms are increasingly being developed for use in healthcare. These algorithms may reflect biases that magnify disparities in clinical care, and there is a growing need for understanding how algorithmic biases can be mitigated in pursuit of algorithmic fairness. We conducted a scoping review on algorithmic individual fairness (IF) to understand the current state of research in the metrics and methods developed to achieve IF and [...]

To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English.

To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.

To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.

To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).

We aimed to demonstrate the importance of establishing best practices in large language model research, using repeat prompting as an illustrative example.

Event capture in clinical trials is resource-intensive, and electronic medical records (EMRs) offer a potential solution. This study develops algorithms for EMR-based death and hospitalization capture and compares them with traditional event capture methods.