Enhancing Personal Healthcare through AI-Driven DNA Analysis and Public Health Data Integration

Abstract

This article presents a privacy-preserving framework that integrates AI-driven DNA analysis with publicly available health data to deliver personalized healthcare recommendations. The model synthesizes private genomic profiles with research repositories such as PubMed and the NIH Genome Database, ensuring that insights are both individualized and grounded in current medical evidence. A dual-enclave architecture separates private DNA information from public research inputs, while large language models (LLMs) serve as verification agents to cross-check outcomes against peer-reviewed literature. Key genetic markers, including MTHFR, COMT, MTR, and AHCY, are analyzed to provide recommendations on supplementation, stress management, and lifestyle modifications. By employing a zeroknowledge design and encrypted processing, sensitive data remains protected while benefiting from continuous recalibration against emerging evidence. Unlike existing frameworks that remain descriptive or limited to static recommendations, the proposed system enables dynamic, realtime updates that adapt to evolving genomic and clinical knowledge. This approach advances personalized medicine by offering actionable, evidence-based interventions while maintaining the confidentiality and integrity of individual health records.

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