Artificial Intelligence and Corruption: Opportunities and Challenges in the Health Sector

Paula del Rey‐Puech, Dina Balabanova and Martin McKee

International Journal of Health Planning and Management, 2025, vol. 40, issue 6, 1341-1347

Abstract: Corruption in health systems diverts resources, erodes trust, and reduces service quality. Traditional oversight methods struggle to detect fraudulent patterns, but Artificial Intelligence (AI) offers new possibilities. AI can analyse large datasets to predict corruption risks and detect irregularities in procurement, insurance claims, and counterfeit medicines. Successful applications include AI‐powered tools that flag suspicious transactions, expose bid‐rigging in procurement, and identify fraudulent medical billing. AI can also complement other analytical tools to help track counterfeit drug supply chains through image recognition and network analysis. However, AI's impact depends on how it is deployed. Government‐led AI initiatives may enhance transparency but risk reinforcing power imbalances or enabling authoritarian control. In contrast, civil society‐driven efforts can empower citizens to hold authorities accountable but face challenges like limited data access and misinformation risks. Moreover, AI can also facilitate corruption in the health system through biased algorithms, deepfake propaganda, or manipulated AI‐driven decision‐making in resource allocation. Maximising AI's anti‐corruption potential in healthcare requires investments in skilled personnel and data systems. AI should complement human oversight, with transparent auditing mechanisms to mitigate biases. Integrating blockchain and AI technologies may enhance accountability by securing procurement records and preventing data manipulation. While AI presents significant opportunities, its application to anti‐corruption remains a political issue as much as a technological one. Careful governance, ethical and legal safeguards, and balanced implementation will determine whether AI combats corruption or exacerbates abuses.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/hpm.70002

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:ijhplm:v:40:y:2025:i:6:p:1341-1347

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0749-6753

Access Statistics for this article

International Journal of Health Planning and Management is currently edited by Calum Paton

More articles in International Journal of Health Planning and Management from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().