LAVA: Granular Neuron-Level Explainable AI for Alzheimer's Disease Assessment from Fundus Images

Explainability is essential for clinical AI: The paper addresses a critical gap where previous AI models for Alzheimer's detection "have yet to provide a rational explanation about the decision," highlighting that healthcare applications require transparent reasoning, not just accurate predictions.

Retinal imaging offers a non-invasive diagnostic pathway: The research validates the retina as a potential biomarker for Alzheimer's Disease, enabling detection directly from fundus (eye) images without requiring longitudinal studies or invasive clinical procedures, which could democratize early screening.

Granular AI interpretation enables disease staging: LAVA's neuron-level explainability allows the model to "infer the stage of disease's progression" across the AD continuum, moving beyond binary disease/no-disease classifications to provide clinically actionable information about disease severity.

Model-agnostic frameworks increase responsible AI adoption: By designing LAVA as "model-agnostic," the framework can be applied across different CNN architectures, making explainability a generalizable tool rather than tied to specific models—important for widespread responsible deployment in healthcare settings.

Validation against established clinical standards matters: The framework's validation against "UK Biobank cognitive tests and vascular morphological features" demonstrates the importance of grounding AI explanations in recognized clinical evidence rather than relying solely on model performance metrics.