ArticleOsong public health and research perspectives2026
Collaborative networks, trends, and comparative analysis of artificial intelligence techniques in healthcare research: a narrative review.
Article in Osong public health and research perspectives, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
2 authors.
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Abstract
backgroundArtificial 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.
methodsA 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.
resultsThe 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.
conclusionThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.