Global Policies on Radiological Image Use for Teaching, Research, and AI

Radiology Data Governance

Introduction Regulations Best Practices Case Study References

Introduction Regulations Best Practices Case Study References

Beyond Diagnosis: Global Policies on Radiological Image Use for Teaching, Research, and AI

A comparative analysis of legal frameworks, institutional best practices, and ethical considerations for the secondary use of imaging data.

Created by Dr. Sharad Maheshwari, imagingsimplified@gmail.com

The Global Regulatory Landscape

While the goal of protecting patient privacy is universal, legal mechanisms differ significantly across major jurisdictions. Understanding these differences is key to international collaboration and establishing robust local policies.

HIPAA (USA)

The US approach is primarily focused on the **de-identification** of Protected Health Information (PHI). If an image and its report are successfully stripped of 18 specific identifiers, they are no longer considered PHI and can be used more freely for research and AI training [1]. An Institutional Review Board (IRB) can also waive the need for consent under strict conditions [2].

GDPR (Europe)

GDPR takes a more **rights-based, consent-centric approach**. Health data is a "special category," and the default basis for its use in research is **explicit consent** for a specific purpose [3, 4]. It crucially distinguishes between truly anonymized data (outside GDPR) and pseudonymized data (still protected) [5].

DPDP Act (India)

India's DPDP Act aligns with the consent-based model of GDPR, emphasizing **specific, informed, and unambiguous consent**. It enforces "purpose limitation," meaning data collected for diagnosis requires separate consent for research [6]. The concept of "Deemed Consent" has limited application for research.

Institutional Policies: Learning from the Leaders

Top academic medical centers are not just complying with regulations; they are creating sophisticated ecosystems for data governance that serve as global benchmarks.

• S

  Stanford Medicine

  Pioneered the secure **data enclave** (STARR), allowing researchers to analyze data without ever downloading it, minimizing breach risks [7].

• M

  Mayo Clinic

  A leader in patient-centric governance, utilizing a clear "opt-out" model for research and implementing **federated learning**, where AI models are trained locally so patient data never leaves its source [8, 9].

• H

  Harvard University (MGB)

  Renowned for developing **automated de-identification pipelines** using advanced NLP to scrub identifiers from unstructured text in reports, a major challenge in anonymization [10].

• T

  University of Toronto (UHN)

  Focuses on strong governance through **Research Ethics Boards (REBs)** and the creation of curated **research data repositories** (or "data trusts") [11].

Case Study: The Legacy Archive Dilemma

This common scenario highlights the friction between historical data collection practices and modern privacy laws.

The Scenario:

A senior radiologist has collected tens of thousands of "anonymized" images over a 20-year career by removing patient names from DICOM headers. A startup offers to commercialize this dataset to train a new diagnostic AI tool.

The Verdict:

Using this legacy archive for a new commercial purpose is **legally and ethically indefensible** under modern laws. The original consent is invalid for the new purpose [4, 6], and the level of anonymization is almost certainly insufficient to meet HIPAA or GDPR standards [1, 5]. The only path forward would be to re-contact patients for specific consent or undergo a rigorous re-anonymization and ethics board review process.

References

1. U.S. Department of Health & Human Services (HHS). (2012). Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule.
2. U.S. Department of Health & Human Services (HHS). (2003). Code of Federal Regulations, Title 45, §164.512(i).
3. European Parliament and Council of the European Union. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation).
4. European Data Protection Board (EDPB). (2020). Guidelines 05/2020 on consent under Regulation 2016/679.
5. Article 29 Working Party. (2014). Opinion 05/2014 on Anonymisation Techniques.
6. Government of India, Ministry of Law and Justice. (2023). The Digital Personal Data Protection Act, 2023.
7. Stanford Medicine Research IT. (n.d.). The Nero Data Enclave.
8. Mayo Clinic. (n.d.). Use of Health Information for Research.
9. Pfohl, S. R., et al. (2019). Federated learning for healthcare informatics. Journal of the American Medical Informatics Association, 26(10).
10. Henry, S., et al. (2020). A de-identification pipeline for unstructured clinical notes in the Mass General Brigham healthcare system. JAMIA Open, 3(4).
11. University Health Network (UHN). (n.d.). Research Ethics Board (REB).
12. National Medical Commission. (2002). Indian Medical Council (Professional conduct, Etiquette and Ethics) Regulations, 2002.

© 2025. This article is intended for informational purposes and does not constitute legal advice.