aceExperimental Protocols for Developing and Validating Explainable AI Models in Secure Healthcare Diagnostics
Artificial Intelligence (AI) has drastically enhanced healthcare diagnostics with automated disease detection, predictive analytics, and intelligent systems for clinical decision support. Nevertheless, the black-box nature of deep learning models hampers interpretability, clinical trust, and regulatory acceptance and raises concerns about data privacy and cybersecurity. This paper presents comprehensive experimental frameworks for developing and evaluating explainable AI models for secure healthcare diagnostics.…
A combined pipeline of explainability methods, federated learning and differential privacy reached 96% accuracy, 94% sensitivity and 95% specificity while limiting privacy leakage and highlighting diagnosis-relevant regions.
Evidence
- Peer-reviewedJournal of Visualized Experiments2026-10-16
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Truvace Impact Record TRV-2026-1961, v1: “Experimental Protocols for Developing and Validating Explainable AI Models in Secure Healthcare Diagnostics.” Truvace, 2026-10-20. /record/TRV-2026-1961 (accessed at citation time). sha256 07731111b808483c…
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