Latest AI and machine learning research in health policy for healthcare professionals.
Adolescence and young adulthood (10-25 years) constitute a sensitive developmental period marked by rapid biological, psychological, and social changes, during which preventative health interventions can shape long-term outcomes. Mobile health (mHealth) tools offer opportunities for tailored support but often with limited adaptation to adolescents’ dynamic contexts, resulting in inconsistent engag...
For accurate medication usage statistics and medication adherence calculations, we need to have an accurate days’ supply (DS) for each prescription. Unfortunately, often the DS or information needed for calculating the DS is not provided. Therefore, other methods need to be applied to acquire missing values or substituting incorrect values. The aim of this study is to apply a variety of methods fo...
Structured recording of key information such as diagnoses is essential for safe, efficient patient care, but is currently done incompletely because it...
Unplanned hospital admissions impose substantial strain on healthcare systems, yet predictive models for these events remain underexplored in practice...
This scoping review explores how predictive modelling can strengthen pre-exposure prophylaxis (PrEP) uptake among high-risk populations in Africa, whe...
Neurological development between the ages of 3 to 11 is crucial to the shaping of infrastructural capabilities like the executive functions that enabl...
Substantial geographic disparities in cardiovascular disease (CVD) mortality persist across the United States. The extent to which “place” reflects un...
GAI tools are increasingly used informally for health, yet evidence from low- and middle-income countries (LMICs) is limited. This study generates ear...
Persistent socioeconomic and caste inequalities in India drive disparities in healthcare access. Machine learning (ML) models offer promise for foreca...
To evaluate the performance of open and proprietary LLMs, with and without Retrieval-Augmented Generation (RAG), on cardiology board-style questions a...
This study investigates how historical disparities in healthcare access influence machine learning (ML) predictions of healthcare utilization among ol...
The availability of effective antiretroviral therapy has made HIV manageable, provided patients have consistent access to routine viral load (VL) test...
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system. Early detection of the prodromal phase could enable timely inte...
Conversational agents based on large language models (LLMs) have shown moderate efficacy in reducing depressive and anxiety symptoms. However, most ex...
Information from electronic health records (EHRs) may be incorporated into computable phenotype algorithms in efforts to overcome inaccuracies of algo...
Large language models (LLMs) have demonstrated rapid advancements in natural language understanding and generation, prompting their integration into b...
Large language models (LLMs) show promise on healthcare tasks, yet most evaluations emphasize multiple-choice accuracy rather than open-ended reasonin...
Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for healthcare providers to summarize and synthesize re...
Depression affects millions worldwide with both pharmacological and psychological therapies widely applied, both with limited treatment success. Many ...
Echocardiography serves as a cornerstone of cardiovascular diagnostics through multiple standardized imaging views. While recent AI foundation models ...