Translational applications of artificial intelligence and machine learning for mosquito-borne disease control in low- and middle-income countries.

Artificial intelligence for precision malaria control: transforming surveillance, prediction, and intervention strategies

Malaria elimination has stalled globally despite decades of investment, with traditional surveillance constrained by retrospective reporting and limited capacity to integrate high-dimensional, non-linear data. In this perspective, we argue that artificial intelligence (AI) encompassing machine learning and deep learning can help shift malaria control from reactive reporting toward predictive, precision public health, while cautioning that it is one enabler among many rather than a stand-alone solution. We examine AI across three interconnected domains: surveillance (understood broadly as case detection, entomological and intervention-coverage monitoring, and the data-to-response loop), prediction (outbreak forecasting and spatial risk mapping), and control (resource stratification and intervention optimisation). Reported AI diagnostics can exceed 90% accuracy, and forecasting models offer useful lead times by integrating climatic, entomological and epidemiological data. However, realising this potential depends on resolving data-quality limitations, algorithmic bias, weak digital infrastructure, and absent governance frameworks, and on locally adapted rather than uniformly generalised models. We contend that demand-driven integration into national strategies, local capacity building, and prospective trials not algorithmic novelty will determine whether AI meaningfully accelerates progress toward elimination.

Data-driven decision making in malaria control: the role of AI in the public health policy

Malaria endures a significant part in public health concern, especially in tropical and subtropical regions. Traditional malaria control methods often face limitations with surveillance, diagnosis and efficient resource allocation. This review explores the role of Artificial Intelligence (AI) in augmenting data-driven decision-making for malaria control and elimination efforts, focusing on surveillance systems, enhancing the effectiveness of intervention strategies and optimizing the resource allocations. AI technologies, mainly machine learning algorithms and computer vision systems, demonstrate significant potential in improving malaria control outcomes. Key findings include increased accuracy in outbreak prediction, improved diagnostic precision through automated microscopy and optimized resource allocation reducing response times. Additionally, deep learning models are emerging as promising tools in identifying drug resistance patterns and personalizing treatment protocols. AI integration in malaria control programs offers substantial benefits for public health decision-making. In this article, we conducted a comprehensive review of peer-reviewed literature, analyzing AI applications in malaria control across key domains such as surveillance, diagnosis, treatment and resource management. However, effective implementation requires robust data infrastructure, ethical frameworks addressing algorithmic bias and sustained international collaboration. Future directions prioritize equitable access, capacity building and development of standardized evaluation metrics for evaluating AI-driven interventions.  

Application and Prospects of Artificial Intelligence in Schistosomiasis Prevention and Control

Schistosomiasis is a serious zoonotic parasitic disease affecting approximately 250 million people worldwide. Despite significant progress in China, challenges remain in snail surveillance, the low sensitivity of traditional diagnostics, and imprecise resource allocation. Recent advances in artificial intelligence offer new solutions. In snail monitoring, CNNs achieve over 90% accuracy in image recognition, and deep learning combined with remote sensing accurately identifies snail habitats. For diagnosis, mobile microscopy integrated with deep learning enables automated on-site egg detection. In strategy optimization, machine learning models (e.g., XGBoost) quantitatively evaluate intervention cost-effectiveness, and decision support systems simulate integrated control effects. Challenges include data quality, accessibility, and ethical privacy concerns. Future directions encompass multimodal diagnostic integration, transfer learning with foundation models, large language model (LLM)-assisted decision-making decision-making, and a "human-animal-environment" intelligent prevention and control system.

Framework for developing explainable artificial intelligence models for neglected tropical disease diagnosis in low-resource settings

Background Neglected tropical diseases (NTDs) continue to affect more than one billion people globally, disproportionately impacting populations living in low-resource settings characterized by limited diagnostic infrastructure, shortages of trained healthcare personnel, and restricted access to specialist services. Recent advances in artificial intelligence (AI), particularly deep learning and computer vision, have demonstrated significant potential for improving disease detection through the analysis of clinical images and microscopy data. However, despite encouraging diagnostic performance, many AI systems remain difficult to interpret, creating barriers to clinical trust, adoption, regulatory acceptance, and sustainable implementation in endemic regions. Main body This narrative review examines the current landscape of AI applications in NTD diagnosis and critically evaluates the role of explainable artificial intelligence (XAI) in addressing challenges associated with transparency and trustworthiness. Evidence from studies involving malaria, schistosomiasis, soil-transmitted helminth infections, leishmaniasis, and skin-related NTDs demonstrates the growing capacity of AI to support diagnostic decision-making in resource-constrained environments. Nevertheless, persistent challenges related to limited datasets, poor data quality, algorithmic bias, model drift, infrastructure constraints, and ethical governance continue to impede translation into routine healthcare practice. Existing explainability approaches, including Gradient-weighted Class Activation Mapping (Grad-CAM), heatmaps, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and attention mechanisms, were reviewed to assess their relevance for NTD diagnostic systems. Framework development Drawing upon current evidence in explainable AI, digital health implementation, and global health systems research, a seven-stage framework is proposed comprising: (1) problem definition, (2) data acquisition, (3) model development, (4) explainability layer integration, (5) clinical validation, (6) deployment in low-resource settings, and (7) continuous learning and monitoring. The framework embeds explainability throughout the AI development lifecycle to enhance transparency, accountability, clinical relevance, and equity. Conclusions Artificial intelligence has considerable potential to improve NTD diagnosis in low-resource settings, but successful adoption depends on trust, transparency, and usability. The proposed framework provides a structured pathway for developing explainable AI systems that are technically robust, clinically meaningful, ethically responsible, and implementable within resource-constrained health systems, thereby supporting future NTD control and elimination efforts.

Machine Learning Strategies for Malaria Risk Prediction based on Text-based Clinical Information

Explainable AI for Malaria Diagnosis: Comparative Analysis of ML Models Using Random Forest Feature Selection and SHAP Interpretability

Malaria remains one of the most significant causes of morbidity and mortality in tropical and subtropical regions, and timely diagnosis is essential for effective case management. Microscopy is the traditional parasitological reference standard, while rapid diagnostic tests (RDTs) are widely used, field-deployable alternatives with product- and antigen-dependent sensitivity and specificity; both approaches are constrained by requirements for trained personnel, reagents, or equipment in resource-limited settings. This study develops and evaluates, on a simulated clinical dataset calibrated to published aggregate statistics, an explainable artificial intelligence pipeline for malaria diagnosis prediction from routinely collectable symptoms, vital signs, and haematological indices, using six machine-learning (ML) models: Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Classifier (SVC), and Decision Tree (DT). The Synthetic Minority Oversampling Technique (SMOTE) and Random Forest feature selection are embedded within a single leakage-safe pipeline that is refitted in every cross-validation fold. The reported best model is selected on the basis of the cross-validated F1-score rather than held-out test performance, and the test set is used exactly once for confirmatory reporting. Under this design, SVC with RF-selected features was selected (mean cross-validated F1 = 0.740), achieving a test-set accuracy of 0.906 [95% CI 0.852, 0.953], recall of 0.867 [0.667, 1.000], and ROC AUC of 0.959 [0.919, 0.988]. A paired bootstrap test found no statistically significant difference in AUC compared with the runner-up, Logistic Regression (AUC 0.956, p = 0.81). Permutation importance corroborated 8 of the top 10 impurity-based features. Parasite density, the quantity used to determine the parasitological diagnosis, was excluded from the predictor set as a precautionary safeguard against near-total label leakage. Calibration, subgroup recall by age and sex, and a class-weighting comparison are also reported. This framework illustrates, without any claim of clinical validity, how a leakage-safe ML pipeline and SHAP interpretability can be combined and rigorously self-audited; real patient-level data and external validation are required before any clinical inference is drawn.