Can I Audit How AI Uses My Medical Data? The Quest for Algorithmic Transparency in Healthcare

Introduction

The integration of Artificial Intelligence (AI) into healthcare is rapidly transforming diagnostics, treatment planning, and drug discovery. From predicting patient outcomes to optimizing hospital workflows, AI promises a future of personalized and efficient medicine. However, this revolution is built on a foundation of highly sensitive personal information: your medical data. As AI systems—often described as "black boxes"—become central to clinical decisions, a critical question arises for patients and professionals alike: Can I audit how AI uses my medical data?

The answer is complex, residing at the intersection of technological capability, existing data privacy laws, and emerging ethical demands for algorithmic transparency. For the general public and digital health professionals, understanding the current rights and the regulatory gaps is paramount to ensuring trust in this new era of medicine.

The "Black Box" Challenge and Existing Rights

AI models, particularly deep learning networks, are notoriously opaque. Their decision-making processes are often too complex for humans to fully trace, leading to the "black box" problem. This opacity directly challenges a patient's ability to understand and contest an AI-driven medical decision.

Current regulatory frameworks offer a partial, but incomplete, answer to the audit question:

• HIPAA (Health Insurance Portability and Accountability Act): In the United States, HIPAA governs the use of Protected Health Information (PHI). It grants individuals the right to an accounting of disclosures and the right to dispute the accuracy of their information [1]. While this applies to the PHI used by AI systems, it does not grant a direct right to inspect the algorithm's internal logic or its training data for bias.
• GDPR (General Data Protection Regulation): In the European Union, the GDPR is more explicit, granting a "right to an explanation" for decisions based solely on automated processing [2]. However, legal scholars note that this right often translates to "meaningful information about the logic involved," which may not provide the technical depth required for a true audit of the AI's data usage or performance [3].

Crucially, both HIPAA and GDPR mandate audit controls for systems handling sensitive data, but these are typically internal organizational requirements focused on security and access, not patient-initiated audits of the AI's decision-making process itself [4].

The Emerging Right to Algorithmic Transparency

The current limitations have spurred a global movement toward establishing a new set of patient rights specifically tailored for the AI age. The concept of a patient-initiated audit is evolving into a demand for algorithmic transparency and a right to contest AI-driven outcomes.

For a patient to effectively audit or contest an AI decision, they require access to four key pieces of information, which go beyond simple data access:

1. Data Use: How the AI system specifically utilized the patient's data in its training and inference phases.
2. Bias Identification: Information on the system's potential biases and how they were mitigated.
3. Performance Metrics: The system's accuracy and error rates in a real-world clinical setting.
4. Division of Labor: A clear understanding of where the AI's influence ends and the human clinician's judgment begins [3].

This shift from a passive right to data access to an active right to algorithmic accountability is the next frontier in digital health policy. It requires developers to move beyond simply explaining what an AI did, to explaining why it did it, and how the patient's data contributed to the outcome.

The Future of Patient Auditing and Accountability

Achieving a true patient audit right requires a multi-pronged approach involving technology, regulation, and professional oversight. Technologically, this means developing more inherently interpretable AI models (Explainable AI or XAI) that can provide clear, human-readable rationales for their outputs.

Regulatorily, it means new laws that explicitly define the scope of a patient's right to contest an AI decision, including mandatory disclosure of the AI's performance and bias assessments. The EU's AI Act, for instance, is a landmark effort to classify AI systems by risk and impose corresponding transparency requirements.

Ultimately, the goal is to foster a healthcare environment where AI is a trusted partner, not an inscrutable oracle. The ability to audit, or at least gain meaningful insight into, how AI uses one's medical data is fundamental to maintaining patient autonomy and trust.

For more in-depth analysis on this topic, including the ethical implications of AI in clinical settings and the future of regulatory compliance, the resources at www.rasitdinc.com provide expert commentary and professional insight into the evolving landscape of digital health and AI innovation.

References

[1] HIPAA Journal. (2025). When AI Technology and HIPAA Collide. URL: https://www.hipaajournal.com/when-ai-technology-and-hipaa-collide/ [2] European Union. (2016). General Data Protection Regulation (GDPR). Article 22. [3] Ploug, T., et al. (2025). The need for patient rights in AI-driven healthcare – risk-based regulation is not enough. Journal of the Royal Society of Medicine. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC12198154/ [4] Inquira Health. (2025). GDPR and HIPAA Compliance in Healthcare AI. URL: https://www.inquira.health/blog/gdpr-and-hipaa-compliance-in-healthcare-ai-what-it-leaders-must-know [5] Morgan Lewis. (2025). AI in Healthcare: Opportunities, Enforcement Risks and False Claims and the Need for AI-Specific Compliance. URL: https://www.morganlewis.com/pubs/2025/07/ai-in-healthcare-opportunities-enforcement-risks-and-false-claims-and-the-need-for-ai-specific-compliance