Advancing Precision Medicine with Ethical AI and Standardized Data
Contributed Commentary by Dr. Rakesh Nagarajan, Chief Medical Officer, Velsera
April 25, 2025 | The integration of artificial intelligence (AI) into precision medicine is transforming healthcare by enabling more personalized treatment strategies. However, along with these advancements come significant ethical considerations. AI must be implemented such that it enhances, rather than replaces, the physician-patient relationship while maintaining patient autonomy, transparency, and explainability in clinical decision-making.
A critical factor in the ethical adoption of AI is data standardization. With vast amounts of genomic and clinical data generated annually, AI’s ability to deliver reliable and unbiased insights depends on the use of high-quality, structured data. Without standardization, AI-driven healthcare solutions risk perpetuating disparities rather than advancing equitable patient care.
AI as a Decision-Support Tool, Not a Replacement
While concerns about AI replacing human physicians exist, its true role is to support clinical decision-making by analyzing complex datasets and identifying patterns. By functioning as a component of a clinical decision support system (CDSS), AI enhances physician expertise, empowering clinicians to make more informed decisions while ensuring patients remain at the center of care.
Rather than dictating treatment paths, AI serves as an adjunct to physician expertise, equipping healthcare providers with tools to enhance diagnostic accuracy and improve patient outcomes. Ensuring that final decision-making remains a collaborative process between patients and physicians is fundamental to ethical AI implementation.
Transparency and Explainability in AI-Driven Healthcare
For AI to be trusted in clinical settings, it must be transparent and explainable. AI-generated recommendations should be traceable to authoritative sources, allowing clinicians to evaluate their validity. Much like peer-reviewed medical research, AI models should link conclusions to supporting data, ensuring that healthcare providers can confidently integrate AI insights into patient care.
Data standardization is integral to AI’s reliability. Standardizing genomic and clinical data enhances model accuracy, reduces ambiguity in AI-generated outputs, and ensures interpretability across diverse patient populations. Without standardization, discrepancies in AI training datasets can lead to inconsistencies in patient diagnoses and treatment plans.
Addressing Bias Through Data Standardization
One of the greatest risks in AI-driven precision medicine is bias in training data. AI models trained on incomplete or non-representative datasets can inadvertently reinforce disparities in healthcare. For example, AI systems trained primarily on genomic data from European populations may be less effective in diagnosing and treating individuals from underrepresented groups.
Global data standardization is crucial in mitigating bias. By ensuring that AI algorithms interpret genomic and clinical data consistently across institutions, standardization promotes fairness and accuracy. AI models that incorporate diverse datasets provide more equitable healthcare outcomes, preventing misclassifications of genetic variants and improving diagnostic precision and recall.
Ensuring Fairness in AI-Powered Clinical Workflows
Without standardized data structures, AI-powered healthcare solutions risk favoring certain populations based on how and where data were collected. Regulatory bodies and advocacy groups must establish universal standards and minimum requirements for AI model training data to ensure equitable healthcare outcomes. By fostering consistent data-sharing frameworks, policymakers can help develop AI-driven precision medicine tools that serve all patients, rather than reinforcing existing healthcare disparities.
Informed Consent and Privacy Considerations
While patients do not explicitly consent to physicians using AI-based clinical decision support tools—just as they do not for medical calculators—determining the type of consent needed becomes essential when AI is used for human subjects research versus when it is used for clinical operations or quality improvement. In each setting, patients must be informed about how their data are used and what measures are in place to protect their privacy.
Standardized privacy frameworks ensure that patient data are anonymized or de-identified as appropriate and ethically utilized in AI research. Transparency around data usage fosters trust in AI-driven medicine and reassures patients that their information is handled responsibly.
Strengthening Data Privacy: Anonymization and Synthetic Data
The ethical use of AI in healthcare requires robust privacy protections, including:
- Anonymizing patient data before incorporating it into AI models.
- Generating synthetic datasets to prevent patient re-identification.
- Implementing strict data storage protocols to mitigate privacy risks.
One promising approach is federated learning, which allows AI models to be trained across multiple institutions without centralizing raw patient data. This approach maintains privacy while enabling AI to learn from large-scale, diverse datasets, ultimately improving healthcare applications without compromising patient confidentiality.
Overcoming the Interoperability Challenge
For AI to reach its full potential in precision medicine, harmonized data formats are essential for interoperability across healthcare institutions and research organizations. Without rigorous data standardization, AI models will not be trained well and may thus struggle to deliver consistent and reliable recommendations, limiting their effectiveness in clinical settings.
By adopting standardized genomic and clinical data frameworks, AI-driven insights can remain applicable across healthcare systems, enhancing diagnostic accuracy and treatment personalization. Standardization is the foundation for scaling AI-driven precision medicine globally.
The Need for Global Collaboration in AI Development
To ensure AI-driven precision medicine is equitable and effective, AI models must incorporate data from diverse genetic backgrounds. Standardized data-sharing protocols improve AI model generalizability, mitigating biases that could disproportionately impact specific populations.
Through international collaboration and harmonized data governance, AI developers, healthcare providers, and policymakers can establish ethical frameworks that ensure AI-driven medicine benefits all patients, regardless of nationality or genetic background.
Ethical AI as an Enabler, Not a Replacement
AI should empower, not replace, clinical decision-making. Physicians remain at the heart of patient care, with AI functioning as a tool to support and enhance medical expertise. Ethical AI adoption relies on transparency, bias mitigation, and data standardization, ensuring AI-driven medicine remains a trusted and effective resource.
As AI continues to reshape precision medicine, collaboration between industry leaders, regulatory bodies, and advocacy groups is essential in setting ethical standards and best practices. AI’s role in healthcare should not be about replacing human judgment but about enhancing clinical decision-making through data-driven insights.
By fostering open dialogue, global cooperation, and standardized best practices, we can harness AI’s full potential while upholding trust, integrity, and equity in precision medicine.
Dr. Rakesh Nagarajan is the Chief Medical Officer at Velsera, focusing on democratizing clinical genomics and advancing precision research through 'omics technologies. With nearly thirty years of experience at the intersection of computer science, informatics, and medicine, he is a trained physician scientist committed to clinical and translational research. He can be reached at rakesh.nagarajan@velsera.com.