The global geography of artificial intelligence in life science research.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
12 Sep 2024
12 Sep 2024
Historique:
received:
08
06
2023
accepted:
15
08
2024
medline:
13
9
2024
pubmed:
13
9
2024
entrez:
12
9
2024
Statut:
epublish
Résumé
Artificial intelligence (AI) promises to transform medicine, but the geographic concentration of AI expertize may hinder its equitable application. We analyze 397,967 AI life science research publications from 2000 to 2022 and 14.5 million associated citations, creating a global atlas that distinguishes productivity (i.e., publications), quality-adjusted productivity (i.e., publications stratified by field-normalized rankings of publishing outlets), and relevance (i.e., citations). While Asia leads in total publications, Northern America and Europe contribute most of the AI research appearing in high-ranking outlets, generating up to 50% more citations than other regions. At the global level, international collaborations produce more impactful research, but have stagnated relative to national research efforts. Our findings suggest that greater integration of global expertize could help AI deliver on its promise and contribute to better global health.
Identifiants
pubmed: 39266506
doi: 10.1038/s41467-024-51714-x
pii: 10.1038/s41467-024-51714-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7527Informations de copyright
© 2024. The Author(s).
Références
Matheny, M. E., Whicher, D. & Israni, S. T. Artificial intelligence in health care: a report from the National Academy of Medicine. JAMA 323, 509–510 (2020).
doi: 10.1001/jama.2019.21579
pubmed: 31845963
Copeland, B. Artificial Intelligence. In: Encyclopedia Britannica (2024).
Turbé, V. et al. Deep learning of HIV field-based rapid tests. Nat. Med. 27, 1165–1170 (2021).
doi: 10.1038/s41591-021-01384-9
pubmed: 34140702
pmcid: 7611654
Leite, M. L. et al. Artificial intelligence and the future of life sciences. Drug Discov. Today 26, 2515–2526 (2021).
doi: 10.1016/j.drudis.2021.07.002
pubmed: 34245910
Noorbakhsh-Sabet, N., Zand, R., Zhang, Y. & Abedi, V. Artificial intelligence transforms the future of health care. Am. J. Med. 132, 795–801 (2019).
doi: 10.1016/j.amjmed.2019.01.017
pubmed: 30710543
pmcid: 6669105
Bohr, A. & Memarzadeh, K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 25–60 https://doi.org/10.1016/B978-0-12-818438-7.00002-2 (2020).
Wuchty, S., Jones, B. F. & Uzzi, B. The increasing dominance of teams in production of knowledge. Science 316, 1036–1039 (2007).
doi: 10.1126/science.1136099
pubmed: 17431139
Adams, J. The fourth age of research. Nature 497, 557–560 (2013).
doi: 10.1038/497557a
pubmed: 23719446
Jones, B. F., Wuchty, S. & Uzzi, B. Multi-university research teams: Shifting impact, geography, and stratification in science. Science 322, 1259–1262 (2008).
doi: 10.1126/science.1158357
pubmed: 18845711
Coccia, M. & Wang, L. Evolution and convergence of the patterns of international scientific collaboration. Proc. Natl. Acad. Sci. 113, 2057–2061 (2016).
doi: 10.1073/pnas.1510820113
pubmed: 26831098
pmcid: 4776471
Beam, A. L. et al. Artificial intelligence in medicine. N. Engl. J. Med. 388, 1220–1221 (2023).
doi: 10.1056/NEJMe2206291
pubmed: 36988598
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B., Chen, I. Y. & Ghassemi, M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176–2182 (2021).
doi: 10.1038/s41591-021-01595-0
pubmed: 34893776
pmcid: 8674135
Ricci Lara, M. A., Echeveste, R. & Ferrante, E. Addressing fairness in artificial intelligence for medical imaging. Nat. Commun. 13, 4581 (2022).
doi: 10.1038/s41467-022-32186-3
pubmed: 35933408
pmcid: 9357063
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
doi: 10.1038/s41591-021-01614-0
pubmed: 35058619
Wahl, B., Cossy-Gantner, A., Germann, S. & Schwalbe, N. R. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob. Health 3, e000798 (2018).
doi: 10.1136/bmjgh-2018-000798
pubmed: 30233828
pmcid: 6135465
AlShebli, B. et al. Beijing’s central role in global artificial intelligence research. Sci. Rep. 12, 21461 (2022).
doi: 10.1038/s41598-022-25714-0
pubmed: 36509790
pmcid: 9744801
Abadi, H. H. N., He, Z. & Pecht, M. Artificial intelligence-related research funding by the US national science foundation and the national natural science foundation of China. IEEE Access 8, 183448–183459 (2020).
doi: 10.1109/ACCESS.2020.3029231
Klinger, J., Mateos-Garcia, J. & Stathoulopoulos, K. Deep learning, deep change? Mapping the evolution and geography of a general purpose technology. Scientometrics 126, 5589–5621 (2021).
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V. & Biancone, P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med. Inform. Decis. Mak. 21, 1–23 (2021).
doi: 10.1186/s12911-021-01488-9
Xu, D., Liu, B., Wang, J. & Zhang, Z. Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers. Front. Bioeng. Biotechnol. 10, 998298 (2022).
doi: 10.3389/fbioe.2022.998298
pubmed: 36277390
pmcid: 9585160
Fernandes, J. M., Costa, A. & Cortez, P. Author placement in computer science: a study based on the careers of ACM Fellows. Scientometrics 127, 351–368 (2022).
doi: 10.1007/s11192-021-04035-5
Lerchenmüller, C., Lerchenmueller, M. J. & Sorenson, O. Long-term analysis of sex differences in prestigious authorships in cardiovascular research supported by the national institutes of health. Circulation 137, 880–882 (2018).
doi: 10.1161/CIRCULATIONAHA.117.032325
pubmed: 29459476
UN. Definition of World Regions. (ed Affairs DoEaS). United Nations (2022).
Haynes, R. B., McKibbon, K. A., Wilczynski, N. L., Walter, S. D. & Werre, S. R. Optimal search strategies for retrieving scientifically strong studies of treatment from Medline: analytical survey. BMJ 330, 1179 (2005).
doi: 10.1136/bmj.38446.498542.8F
pubmed: 15894554
pmcid: 558012
Del Fiol, G., Michelson, M., Iorio, A., Cotoi, C. & Haynes, R. B. A deep learning method to automatically identify reports of scientifically rigorous clinical research from the biomedical literature: comparative analytic study. J. Med. Internet Res. 20, e10281 (2018).
doi: 10.2196/10281
pubmed: 29941415
pmcid: 6037944
Merton, R. K. The Matthew effect in science. The reward and communication systems of science are considered. Science 159, 56–63 (1968).
doi: 10.1126/science.159.3810.56
pubmed: 5634379
Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).
Singh, J. & Fleming, L. Lone inventors as sources of breakthroughs: myth or reality? Manag. Sci. 56, 41–56 (2010).
doi: 10.1287/mnsc.1090.1072
Baruffaldi, S. et al. Identifying and measuring developments in artificial intelligence: Making the impossible possible. OECD Science, Technology and Industry Working Papers, No. 2020/05, 1–68 (2020).
Allison, G. & Schmidt, E. Is China Beating the US to AI Supremacy? Harvard Kennedy School, Belfer Center for Science and International Affairs, 1–24 (2020).
Ye, J. China targets 50% growth in computing power in race against the U.S. Reuters, 9 October. Available at: https://www.reuters.com/technology/china-targets-30-growth-computing-power-race-against-us-2023-10-09/ (2023).
Lundvall, B.-Å. & Rikap, C. China’s catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems. Res. Policy 51, 104395 (2022).
doi: 10.1016/j.respol.2021.104395
Beraja, M., Kao, A., Yang, D. Y. & Yuchtman, N. AI-tocracy. Q. J. Econ. 138, 1349–1402 (2023).
doi: 10.1093/qje/qjad012
Lerchenmueller, M. J. & Sorenson, O. The gender gap in early career transitions in the life sciences. Res. Policy 47, 1007–1017 (2018).
doi: 10.1016/j.respol.2018.02.009
Vos, T. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet 388, 1545–1602 (2016).
doi: 10.1016/S0140-6736(16)31678-6
Tat, E., Bhatt, D. L. & Rabbat, M. G. Addressing bias: artificial intelligence in cardiovascular medicine. Lancet Digital Health 2, e635–e636 (2020).
doi: 10.1016/S2589-7500(20)30249-1
pubmed: 33328028
Lerchenmueller, M. J., Sorenson, O. & Jena, A. B. Gender differences in how scientists present the importance of their research: observational study. BMJ 367, l6573 (2019).
doi: 10.1136/bmj.l6573
pubmed: 31843745
pmcid: 7190066
Alperin, J. P., Portenoy, J., Demes, K., Larivière, V. & Haustein, S. An analysis of the suitability of OpenAlex for bibliometric analyses. arXiv preprint arXiv:240417663 (2024).
Wang, K. et al. Microsoft academic graph: when experts are not enough. Quant. Sci. Stud. 1, 396–413 (2020).
doi: 10.1162/qss_a_00021
Priem, J., Piwowar, H., & Orr, R. OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. arXiv preprint arXiv:2205.01833 (2022).
Liu, N., Shapira, P. & Yue, X. Tracking developments in artificial intelligence research: constructing and applying a new search strategy. Scientometrics 126, 3153–3192 (2021).