Medical imaging applications are increasingly deployed on mobile devices, yet large‑scale learning is constrained by privacy regulation, data silos, and class imbalance in real‑world collections. In parallel, patient‑centric digital twin architectures aim to enable personalized “what‑if” simulations but are rarely integrated with image‑based screening pipelines and explainability mechanisms. This paper introduces FedTwin‑XAI, a unified patient‑owned framework that integrates: (i) mobile imaging‑based screening with post‑hoc explainability, (ii) decentralized personal data pods for data sovereignty, (iii) federated learning for collaborative training without centralizing raw data, (iv) differential privacy‑aware synthetic augmentation to mitigate scarcity and imbalance, and (v) per‑patient digital twins for longitudinal monitoring and scenario simulation. The framework is positioned for privacy-by-design deployment and aligns with federated and explainable machine vision. We instantiate the imaging component using a VGG16‑based scalp disease classifier with SHAP explanations and report component-level quantitative performance on a 10‑class dataset (13,196 images), achieving 99.84\% accuracy on a labeled validation set on the aggregator node. We then provide a protocol to evaluate the end‑to‑end federated twin pipeline under non‑IID client partitions, including communication–accuracy trade‑offs, calibration, and privacy accounting. The result is an actionable blueprint for privacy‑preserving, explainable medical imaging systems that can be embedded into patient‑centric digital twins for intelligent healthcare.