Diversity Gap in Genomic Medicine: Ethical Frameworks for Inclusive AI
Key Questions
What diversity gap exists in genomic data used for AI and precision medicine?
Genome-wide association studies (GWAS) show an 85% European ancestry bias, which skews polygenic risk scores and pharmacogenomics applications. This limits fairness and accuracy for non-European populations.
Why are inclusive datasets and ethical frameworks needed for AI in health?
Without broader ancestry representation, AI models risk perpetuating health inequities in clinical trials and precision medicine. Reviews on AI and health equity stress the need for diverse training data and governance.
What sources highlight the need for more inclusive genomic data in AI?
A Nuffield Council bioethics event and a top journal review on AI and health equity both address ancestry bias and data gaps. Related discussions emphasize expanding patient data diversity before AI can reliably transform trials.
A Nuffield Council bioethics event highlights the 85% European ancestry bias in GWAS, impacting polygenic risk scores and pharmacogenomics. A new review from a top journal on AI and health equity reinforces the need for inclusive training data and ethical frameworks. This is critical for the fairness and accuracy of AI-driven precision medicine and a must-read context for students and self-learners.