Integrating Social Determinants of Health into Precision Medicine: A Critical Review of a Missing Link in Personalized Healthcare

Background: Precision medicine has transformed healthcare through advances in genomics, biomarkers, and artificial intelligence. However, its predominant focus on biological determinants reflects a reductionist approach that inadequately captures the broader social and environmental factors influencing health outcomes. Aim: This review aims to critically examine the limitations of current precision medicine frameworks, particularly the exclusion of social determinants of health (SDOH), and to explore the need for a more integrative, context-aware approach to personalized healthcare. Methods: A critical narrative review was conducted using literature from PubMed, Scopus, and Web of Science, covering publications between 2015 and 2025. Relevant studies addressing precision medicine, SDOH, health disparities, and artificial intelligence were selected based on predefined inclusion criteria and analyzed using thematic synthesis. Results: The findings reveal that current precision medicine models are constrained by biological reductionism, data bias, algorithmic inequities, and fragmented data systems. The exclusion of SDOH limits predictive accuracy, reduces the effectiveness of personalized interventions, and contributes to persistent health disparities. Emerging evidence highlights the potential of integrating artificial intelligence and digital health technologies with social and behavioral data to enhance context-aware care. Conclusion: Precision medicine, as currently practiced, is biologically precise but contextually incomplete. Integrating SDOH into precision medicine frameworks is essential for improving clinical effectiveness, promoting health equity, and ensuring real-world applicability. A shift toward an integrative model that combines biological, social, and behavioral determinants is necessary to achieve truly personalized and patient-centered healthcare.