Home›Neurology› Explainable artificial intelligence offers a structured framework to improve neglected tropical disease diagnosis
Explainable artificial intelligence offers a structured framework to improve neglected tropical disease diagnosisExplainable artificial intelligence could improve diagnosis of neglected tropical diseases
Frontiers in MedicinePublished August 11, 2026Study authors: David Chinaecherem Innocent, Rejoicing Chijindum Innocent, Increase Praise InnocentDOI ↗Editorial oversight: Dr. Ji-eun Park, MD · Brain, Mind & Pain
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Key Takeaway
Note that XAI offers a structured framework to improve NTD diagnosis while addressing issues of transparency and trust.
This narrative review explores the integration of explainable artificial intelligence (XAI) for diagnosing neglected tropical diseases (NTDs), including malaria, schistosomiasis, leishmaniasis, and soil-transmitted helminth infections. The authors synthesize the potential of AI to improve disease detection through the analysis of clinical images and microscopy data.
The review identifies significant barriers to widespread adoption, including limited datasets, poor data quality, algorithmic bias, model drift, infrastructure constraints, and ethical governance concerns. To address these challenges, the authors propose a seven-stage framework: (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 proposed framework aims to ensure that AI systems are technically robust, clinically meaningful, and ethically responsible. However, the review notes several limitations, including the lack of high-quality data and the complexities of infrastructure in resource-constrained environments. The findings provide a conceptual roadmap for developers rather than evidence from clinical trials.
How this fits prior evidence
This narrative review addresses gaps in the technological infrastructure for managing neglected tropical diseases (NTDs). While previous coverage focused on diagnostic tools like environmental DNA and next-generation sequencing for post-elimination surveillance, this review focuses on the role of explainable artificial intelligence (XAI) to improve detection. It complements existing evidence regarding malaria and schistosomiasis by proposing a structured framework to overcome barriers such as algorithmic bias and infrastructure constraints in low-resource settings.
Many people living in low-resource areas suffer from neglected tropical diseases, including malaria and certain parasitic infections. These conditions are often hard to diagnose quickly because of a lack of specialized equipment or experts. Researchers are looking at how artificial intelligence can help identify these illnesses by analyzing clinical images and microscopy data.
To make this technology useful for doctors, the focus is on "explainable" AI. This means creating systems that don't just give an answer, but provide a clear reason for their decision. This transparency helps build trust between the technology and the healthcare workers who use it every day to save lives.
While there are hurdles like poor data quality and limited infrastructure, a new seven-stage framework offers a roadmap for development. This plan covers everything from gathering data to ensuring the tools are ethically responsible and easy to use in clinics with few resources.
What this means for you:
Explainable AI could help doctors in low-resource areas more accurately diagnose neglected tropical diseases.
Common questions
What are neglected tropical diseases?
Neglected tropical diseases include conditions like malaria, schistosomiasis, leishmaniasis, and soil-transmitted helminth infections. These diseases often affect people living in low-resource settings where medical infrastructure is limited.
How does explainable AI help doctors?
Explainable artificial intelligence (XAI) provides transparency and trustworthiness. Instead of being a "black box," it helps clinicians understand why the AI reached a specific diagnosis, making it more useful for clinical practice.
What are the challenges in using AI for these diseases?
Several hurdles exist, including limited datasets, poor data quality, and algorithmic bias. Other issues include model drift, infrastructure constraints, and the need for strong ethical governance to ensure the technology is safe and fair.
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.
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.
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.
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.
