MalariaCare+ is a mobile-compatible, AI-powered diagnostic assistant developed by Universiti Malaya to address the realities of malaria diagnosis in rural and remote Malaysia. Diagnosis of Plasmodium knowlesi—now prevalent in Sabah and Sarawak—remains labour-intensive and error-prone due to variable slide quality and limited access to trained parasitologists, while many existing AI tools are trained on non-local species.

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The project employs a deep learning model capable of identifying malaria-infected red blood cells even in overlapping or low-quality blood smears, effectively providing clinicians with a second set of expert eyes. The model has been integrated into a working mobile application and is currently being tested using real samples from Kapit Hospital, in collaboration with Universiti Malaysia Sarawak, ensuring relevance to real field conditions.

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MalariaCare+ is designed as a diagnostic assistant, not a replacement for healthcare professionals, prioritising usability, explainability, and local context. Planned enhancements include stage-specific classification, real-time inference, secure patient history tracking, and compatibility with smartphone-linked microscopes—supporting deployment in low-connectivity environments

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Importantly, the platform demonstrates how AI can accelerate and improve diagnostic accuracy for infectious disease transmission in underserved settings. By supporting earlier detection and faster response, MalariaCare+ contributes to Malaysia’s malaria elimination goals and serves as a scalable model for equitable, AI-enabled diagnostics*in rural healthcare systems.