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BACKGROUND: Artificial intelligence (AI) is reshaping healthcare by improving diagnosis and treatment planning, increasing operational efficiency, and streamlining administrative workflows. This paper integrates findings from an extensive PubMed search (2015-2025) with bibliometric analysis using RStudio and VOSviewer to investigate the comparative applications of AI methods in healthcare, collaborative networks, and emerging trends. METHODS: A total of 1,243 records were identified through the PubMed search, and after removal of 143 duplicates, 1,100 records were screened. Following full-text assessment and exclusion of ineligible studies, 986 articles were included in the final bibliometric analysis. RESULTS: The main research areas included robotic-assisted surgery, predictive analytics, diagnostic imaging, and precision medicine, with particular emphasis on the prevalence of machine learning and deep learning in imaging and the increasing application of natural language processing to unstructured medical information. CONCLUSION: The review emphasizes the need for greater budgetary allocation to scalable and pragmatic AI technologies and for interdisciplinary cooperation among researchers, industry, and healthcare providers. Despite this growth, challenges such as algorithmic bias, data integration, and ethical concerns persist. The paper also highlights the importance of equitable collaboration, accountable AI, and multinational partnerships in ensuring that AI can be used ethically and efficiently in healthcare over the long term to improve patient care and biomedical innovation. It does so by mapping international and regional trends, identifying the most influential authors, institutions, and funding sources, and evaluating methodological approaches.
Kumar et al. (Thu,) studied this question.