Ajibade, Samuel-Soma M.Lee, Angela Siew HoongJasser, Muhammed BasheerAkinola, Tomiwa Faith2024-10-122024-10-122024979-835035815-5https://doi.org/10.1109/SEB4SDG60871.2024.10630087https://hdl.handle.net/11467/87952024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024 -- 2 April 2024 through 4 April 2024 -- Omu-Aran -- 201895This bibliometric study examines the worldwide progression of machine learning applications in medical and healthcare research between 2010 and 2023. A large dataset of published articles pertaining to machine learning applications in the medical and healthcare sectors is mined for useful information in this study. The data extraction procedure involves retrieving pertinent information from primary sources such as journals, books, and conference proceedings. Subsequently, the retrieved data is subjected to analysis to discern the patterns and tendencies in the use of machine learning in medical and healthcare research. A total of 1,220 publications were found in the Scopus database over the past 14 years. In addition, the study demonstrated that most AIHS research has concentrated on artificial intelligence applications to address a wide range of problems, including patient data security and chronic medical difficulties (such as cardiovascular disorders). Policymakers, healthcare practitioners, and researchers around the world may find this study's conclusions helpful. Emerging ethical concerns, integration, and real-world uses in smart healthcare systems, and the Internet of Things (IoT), could be the focus of future research. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessartificial intelligencebibliometric analysishealthcare systemsmachine learningmedical researchConference ObjectN/A2-s2.0-8520292211610.1109/SEB4SDG60871.2024.10630087