Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity
Abstract Background Artificial intelligence (AI) will have a lasting and drastic impact on medical research and healthcare. In addition to the benefits, the associated risks are also the subject of controversial debate and there are fears of serious consequences. There is an urgent need for action,...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | Globalization and Health |
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| Online Access: | https://doi.org/10.1186/s12992-025-01128-1 |
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| author | Doris Klingelhöfer Markus Braun Janis Dröge David A. Groneberg Dörthe Brüggmann |
| author_facet | Doris Klingelhöfer Markus Braun Janis Dröge David A. Groneberg Dörthe Brüggmann |
| author_sort | Doris Klingelhöfer |
| collection | DOAJ |
| description | Abstract Background Artificial intelligence (AI) will have a lasting and drastic impact on medical research and healthcare. In addition to the benefits, the associated risks are also the subject of controversial debate and there are fears of serious consequences. There is an urgent need for action, which must be underpinned by scientific information. Methods By analyzing temporal and geographic patterns, including national readiness for access to AI, we therefore identified incentives and barriers to global research under socioeconomic conditions. Results The explosive increase in annual publications started in 2017. The main players in AImed research were the USA, China, the UK, Germany, and South Korea. There was a significant correlation between the publication output on AI in medicine (AImed) and the metrics for economy and innovation. Additionally, citation patterns show the disadvantage of the Global South compared to the North American and European countries. In several weaker economies: Jordan, Pakistan, Egypt, Bangladesh, and Ethiopia, a more positive position was found in relation to the number of articles suggesting a better ranking in AImed research in the future. Conclusion The results show the need for advanced global networking to ensure all relevant aspects for equitable development and the beneficial use of AImed without promoting racial or regional inequities and to enforce this not only in the AI systems of economically strong countries but also for the participation of economically weaker countries. |
| format | Article |
| id | doaj-art-2d3800a15b7b469f92bd1d81880af2a6 |
| institution | DOAJ |
| issn | 1744-8603 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Globalization and Health |
| spelling | doaj-art-2d3800a15b7b469f92bd1d81880af2a62025-08-20T02:39:48ZengBMCGlobalization and Health1744-86032025-06-0121111810.1186/s12992-025-01128-1Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equityDoris Klingelhöfer0Markus Braun1Janis Dröge2David A. Groneberg3Dörthe Brüggmann4Institute of Occupational, Social and Environmental Medicine, Goethe University FrankfurtInstitute of Occupational, Social and Environmental Medicine, Goethe University FrankfurtInstitute of Occupational, Social and Environmental Medicine, Goethe University FrankfurtInstitute of Occupational, Social and Environmental Medicine, Goethe University FrankfurtInstitute of Occupational, Social and Environmental Medicine, Goethe University FrankfurtAbstract Background Artificial intelligence (AI) will have a lasting and drastic impact on medical research and healthcare. In addition to the benefits, the associated risks are also the subject of controversial debate and there are fears of serious consequences. There is an urgent need for action, which must be underpinned by scientific information. Methods By analyzing temporal and geographic patterns, including national readiness for access to AI, we therefore identified incentives and barriers to global research under socioeconomic conditions. Results The explosive increase in annual publications started in 2017. The main players in AImed research were the USA, China, the UK, Germany, and South Korea. There was a significant correlation between the publication output on AI in medicine (AImed) and the metrics for economy and innovation. Additionally, citation patterns show the disadvantage of the Global South compared to the North American and European countries. In several weaker economies: Jordan, Pakistan, Egypt, Bangladesh, and Ethiopia, a more positive position was found in relation to the number of articles suggesting a better ranking in AImed research in the future. Conclusion The results show the need for advanced global networking to ensure all relevant aspects for equitable development and the beneficial use of AImed without promoting racial or regional inequities and to enforce this not only in the AI systems of economically strong countries but also for the participation of economically weaker countries.https://doi.org/10.1186/s12992-025-01128-1Medical AIResearch areasPublication outputRisksBig dataGlobal equity |
| spellingShingle | Doris Klingelhöfer Markus Braun Janis Dröge David A. Groneberg Dörthe Brüggmann Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity Globalization and Health Medical AI Research areas Publication output Risks Big data Global equity |
| title | Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity |
| title_full | Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity |
| title_fullStr | Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity |
| title_full_unstemmed | Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity |
| title_short | Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity |
| title_sort | research on artificial intelligence machine and deep learning in medicine global characteristics readiness and equity |
| topic | Medical AI Research areas Publication output Risks Big data Global equity |
| url | https://doi.org/10.1186/s12992-025-01128-1 |
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