SynthesisMalaria journal2025
The role of artificial intelligence (AI) in the elimination of malaria: a systematic literature review.
Synthesis in Malaria journal, 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.
- Community-Centric Malaria Prevention in Sub-Saharan Africa: A Narrative Review of Pathways to Elimination and Global Health Impact.Health science reports · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rising incidence of malaria on a global scale poses a substantial public health concern, necessitating effective and innovative ways for tackling it. The review paper explores the capability of Artificial Intelligence (AI) in aiding India's attempt to eliminate malaria by 2030. Totally, 62 research articles published between 2014 and 2025 are systematically reviewed, covering AI applications in diagnosis (e.g., image-based parasite detection), treatment optimization (predictive analytics), and outbreak prediction. The main findings from research articles indicate that AI models have attained better diagnostic accuracies and significantly decreased diagnostic time as well as human error. Even though there are developments, poor data interoperability, limited rural infrastructure, and gaps in healthcare worker training are some of the major limitations. The review also analyses various AI techniques currently employed globally, which can be adapted for use in India. The paper analyses various AI techniques currently employed globally, which can be adapted for use in India. It also highlights the associated limitations that need to be addressed and suggests areas for future research. This review follows a structured and transparent approach to select, evaluate, and present the role of AI in malaria elimination.
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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.