ArticleThe International journal of health planning and management2025
Artificial Intelligence and Corruption: Opportunities and Challenges in the Health Sector.
Article in The International journal of health planning and management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Understanding informal payments in the health sector in Malawi: results of a multidistrict household survey.Health policy and planning · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.