Artificial intelligence in healthcare: mapping research trends, economic implications, and future research directions through a bibliometric and systematic review

This paper describes a Bibliometric Systematic Literature Review (BSLR) focusing on artificial intelligence (AI) in health care research between 2016 and 2025. The review utilizes the Theory–Context–Characteristics–Methods (TCCM) framework created by Justin Paul and Alejandro Rosado-Serrano, as well as the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines for article selection. We analyzed 13,536 peer-reviewed articles from Scopus, using the R-bibliometrix package to evaluate bibliometric performance and science mapping, and synthesized 312 high-impact studies through TCCM coding. The findings reveal a rapid growth rate of 71.42% per annum in AI health care journal publications; ongoing strong collaboration between the United States and China; and three predominant themes within AI in Healthcare – AI techniques, clinical applications, and machine learning methods. The study finds significant theoretical and methodological limitations; there is a limited amount of research from developing regions, and most of the studies are limited to retrospective single-site supervised learning, indicating a need for much more diverse and externally validated AI research in health care.