Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis
arXiv:2605.30734v1 Announce Type: new Abstract: Malaria remains a leading cause of mortality in sub-Saharan Africa, where scarce diagnostic infrastructure makes timely, accurate diagnosis particularly challenging. While deep learning offers a compelling path toward automated malaria screening, clinical adoption is hindered by computational cost and opacity in decision-making. This work benchmarks four deep learning models spanning a wide range of designed design architectures and model capacities on the NLM-Malaria dataset, jointly evaluating predictive performance, robustness, and post-hoc ex
The proliferation of deep learning models has led to a critical juncture where the practical application in sensitive areas like medical diagnosis necessitates moving beyond mere accuracy to address efficiency, robustness, and interpretability, especially in resource-constrained environments.
This work demonstrates how the operational challenges of deploying AI in critical healthcare settings are being systematically addressed, which is crucial for real-world adoption and impact on public health.
The focus in AI development for medical diagnosis is shifting from purely predictive performance to a multi-faceted evaluation that includes the practical considerations of computational cost, reliability, and explainability.
- · AI healthcare startups
- · Sub-Saharan African healthcare systems
- · Deep learning researchers focusing on efficiency/explainability
- · Patients in resource-limited settings
- · AI models that are computationally expensive and black-box
- · Traditional diagnostic methods in malaria-endemic regions
More deployable and transparent AI diagnostic tools will emerge, improving access to healthcare in underserved regions.
Reduced mortality and morbidity from diseases like malaria as AI-driven diagnostics become more widespread and trustworthy.
Increased global equity in healthcare access, potentially spurring investment and innovation in digital health infrastructure in developing nations.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG