Objective:

To develop and implement a blockchain-based dynamic consent framework integrated with artificial intelligence (AI) to support Low-Dose Computed Tomography (LDCT) lung cancer screening and biobank data utilization in Taoyuan, Taiwan.

Approach:

Key Findings:

• The proposed architecture enables real-time consent modification.
• Strengthens data traceability through blockchain hash registration.
• Improves transparency in data use.
• Currently being piloted within the Taoyuan Expanded Lung Cancer Screening Program targeting 15,000 participants.

Interpretation:

A blockchain-enabled dynamic consent system can address legal, ethical, and governance challenges in LDCT-based lung cancer screening programs.

Conclusion:

This model supports precision health initiatives and provides a scalable pathway for integrating AI and biobank data into public health programs.

Attribution Notice

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