ReviewPublic health challenges2026
Challenges of Artificial Intelligence in Medical Diagnosis in Congolese Hospitals: A Literature Review.
Review in Public health challenges, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Artificial intelligence (AI) is rapidly transforming medical diagnosis worldwide, but its adoption remains limited in Africa, particularly in the Democratic Republic of Congo (DRC). This narrative review aims to analyze the contributions, challenges, and prospects for integrating AI into medical diagnosis in the DRC. Methodology: A comprehensive literature review was conducted in February 2025 in PubMed, Web of Science, Scopus, and Google Scholar databases, as well as reports from international organizations. Studies on the use of AI in medical diagnosis in resource-limited countries, particularly in Africa, were included without language restrictions. The selection followed a two-step process (title/abstract then full text); 103 articles were retained for qualitative synthesis. Results: Studies show that AI enables a 12%-15% improvement in diagnostic accuracy in radiology and a 20% reduction in exam interpretation time. It also helps accelerate epidemic detection (30%-50% faster than conventional methods) and overcome the shortage of specialists in rural areas. However, its implementation in the DRC is hampered by the lack of digital infrastructure, insufficient training, and the absence of an appropriate regulatory framework. Maintenance and financing issues still limit the effective use of available systems. Conclusion: AI represents a major opportunity to strengthen medical diagnosis in the DRC, improving the speed and quality of care. However, effective integration requires targeted investments in infrastructure, training, and regulation. The development of national pilot projects and a solid ethical framework are essential steps for gradual and sustainable adoption.
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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.