The Application of Artificial Intelligence in Healthcare Practice: An Umbrella Review

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and November 2024. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 181 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2024. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 70% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years. Ethical concerns were reported in 64.6% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2024. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or Human-AI-Human paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption. Article highlightsO_LIThis umbrella review summarizes 181 systematic reviews on AI applications across healthcare practice fields. C_LIO_LI70% of the reviews reported AI models trained on diagnostic imaging, EHR, and biomarker data. C_LIO_LIThere has been recent growth in AI using wearable, sensor, and other health data. C_LIO_LIDiagnosis, prognosis, and treatment comprised over 80% of the applications in all the reviews. C_LIO_LIEthical concerns, including privacy, accuracy, bias, and explainability, were raised in over half of the reviews. C_LI