Patient-Centric Blockchain-Governed EHR Sharing Framework for Secure Digital Radiography Sensor Data and AI-Assisted Chest X-Ray Screening

Secure exchange of electronic health records (EHRs) requires clinical confidentiality, verifiable provenance, patient-controlled authorization, and practical handling of high-resolution medical imaging payloads generated by IoMT digital radiography sensors. This study presents an end-to-end patient-centric framework linking digital radiography flat-panel detector streams and acquisition metadata to a blockchain-governed EHR management system. Clinical payloads are secured via authenticated symmetric bulk encryption (AES-256-GCM), stored off-chain on the InterPlanetary File System (IPFS), and referenced on-chain through cryptographic record hashes, ECDSA signatures, wrapped-key references, and stateful access control predicates. A smart contract enforces dynamic, patient-controlled view, edit, and prospective revocation rights. The framework incorporates a DenseNet121 deep learning module that computes clinician-reviewable probability vectors for 14 thoracic findings, cryptographically binding each inference output to the source-image hash, model version, execution timestamp, and reviewing clinician. To prevent data leakage, the NIH ChestX-ray dataset was split at the patient level (80/10/10). System evaluation across 50 benchmark trials demonstrates sub-second execution overhead: encryption and decryption of a 100 MB radiograph payload require 0.957 ± 0.032 s and 0.898 ± 0.028 s, respectively, while IPFS upload and download of a 500 MB imaging payload take 11.470 ± 0.450 s and 13.260 ± 0.520 s. Smart contract state-changing operations consume between 29,150 and 168,010 gas units. The proposed architecture establishes a cryptographically verifiable, high-throughput pipeline for digital radiography sensor data management and AI-assisted EHR decision support.