Toward Precision Oculoplastics: Federated Data, Artificial Intelligence, and the Path to Equitable Therapeutic Discovery

Abstract

Oculoplastic and orbital disorders are rapidly entering a new era of precision medicine, driven by advances in genomics, multi-omic profiling, and artificial intelligence (AI). However, the evidence base supporting these innovations remains overwhelmingly Eurocentric: more than 80% of all genome-wide association study (GWAS) participants globally are of European ancestry, and pivotal trials of teprotumumab, the first targeted biologic for thyroid eye disease (TED), enrolled over 85% European-descended participants. This imbalance constrains understanding of ancestry-specific therapeutic responses, limits the transferability of polygenic and pharmacogenomic models, and risks perpetuating structural bias in emerging AI systems used for diagnostic and surgical decision-making. The high cost of teprotumumab (> USD 200,000 per treatment course) further underscores the need for equitable, predictive stratification. This article outlines how federated learning (FL) and harmonized multi-omic data infrastructures can close these gaps. We highlight the EXAM study as a real-world demonstration of FL’s ability to integrate multi-institutional data without transferring sensitive patient information, achieving superior predictive performance while preserving privacy. Building on these principles, we introduce GeneVault Harmony, a federated harmonization and benchmarking framework that integrates genomic, transcriptomic, imaging, and clinical data while maintaining data sovereignty and aligning with GA4GH and WHO standards. By enabling bias quantification, diversity simulation, and cross-institutional interoperability, Harmony provides a scalable foundation for equitable oculoplastic genomics. We conclude by proposing a pathway toward inclusive precision therapeutics in oculoplastics, including multi-regional genomic consortia and federated, multi-omic AI pipelines spanning the Middle East, Africa, South Asia, and Latin America. Integrating globally representative data into future genomic and pharmacogenomic discovery is essential to ensure that precision ophthalmology evolves in a manner that is scientifically robust, clinically equitable, and globally relevant.

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