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Global Health Economics and
Sustainability
AI in antibiotic prescribing in Nigeria

(ii) Grant funding from research bodies and Author contributions
foundations focused on AMR and healthcare
innovation. Conceptualization: Ismail Rabiu, Fatima Garba Rabiu
(iii) Cost-recovery mechanisms through user fees Writing – original draft: Ismail Rabiu, Abdulazeez
or integration into existing healthcare financing Muhammed, Halima Tukur Ibrahim, Fatima Garba
Rabiu
systems.
• Resources: Develop robust funding proposals, identify Writing – review & editing: All authors
potential funding partners, and demonstrate the cost- Ethics approval and consent to participate
effectiveness of AI-CIPS (World Bank, 2017; Anon, 2021).
Not applicable.
11.2. Training and retraining of personnel involved
Consent for publication
• Purpose: Ensure health-care professionals have the
knowledge and skills to effectively utilize and adapt to Not applicable.
AI-CIPS.
1. Activities: Availability of data
(i) Develop comprehensive training programs Not applicable.
on AI-CIPS for health-care providers,
pharmacists, and IT personnel. References
(ii) Provide ongoing training and support to Ali, A.R., Alhumaid, S., Al Mutair, A., Garout, M.,
address evolving technologies and user Abulhamayel, Y., Halwani, M.A., et al. (2022). Application
needs. of artificial intelligence in combating high antimicrobial
(iii) Foster a culture of continuous learning and resistance rates. Antibiotics (Basel), 11(6):784.
adaptation within healthcare settings.
2. Resources: Partner with universities, professional https://doi.org/10.3390/antibiotics11060784
associations, and AI-CIPS developers to provide Anon. (2020). Alliance for Responsible Use of Antibiotics
training programs. (ARUA). Community Engagement toolkit for Antimicrobial
Resistance.
12. Conclusion Anon. (2021). Global Fund to Fight AIDS, Tuberculosis and
The integration of AI in antibiotic prescribing and clinical Malaria. Investment Case for Antimicrobial Resistance.
support presents a transformative opportunity for Nigerian Available from: https://www.theglobalfund.org/en/
tuberculosis [Last accessed on 2023 Nov 14].
health-care settings. By addressing budgetary constraints
through strategic investments, overcoming challenges, and Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial
capitalizing on the prospects offered by AI technologies, intelligence in healthcare: Transforming the practice of
Nigeria can refine its antibiotic management practices, medicine. Future Healthcare Journal, 8(2):e188-e194.
ultimately improving public health outcomes. With the https://doi.org/10.7861/fhj.2021-0095
use of machine learning applications for infectious disease Baker, R.E., Mahmud, A.S., Miller, I.F., Rajeev, M.,
management, the potential impact of AI in healthcare is Rasambainarivo, F., Rice, B.L., et al. (2022). Infectious
extensive, promising advancements in decision support, disease in an era of global change. Nature Reviews
combating antibiotic resistance, and achieving efficiencies Microbiology, 20(4):193-205.
in new antimicrobial development, diagnostics, https://doi.org/10.1038/s41579-021-00639-z
therapeutics, and cost reduction in both economic and
health personnel aspects. Brownstein, J.S., Rader, B., Astley, C.M., & Tian, H. (2023).
Advances in artificial intelligence for infectious-
Acknowledgments disease surveillance. New England Journal of Medicine,
388(17):1597-1607.
None.
https://doi.org/10.1056/NEJMra2119215
Funding Cavallaro, M., Moran, E., Collyer, B., McCarthy, N.D., Green, C.,
None. & Keeling, M.J. (2023). Informing antimicrobial stewardship
with explainable AI. PLOS Digital Health, 2(1):e0000162.
Conflict of interest https://doi.org/10.1371/journal.pdig.0000162
The authors declare no conflicts of interest. Chang, A., & Chen, J.H. (2022). BSAC Vanguard Series:

Volume 2 Issue 3 (2024) 8 https://doi.org/10.36922/ghes.2602