Advancing Computational Models for Personalized Medicine: Enhancing Predictive Performance, Interpretability, and Practical Implementation for Equitable Healthcare

Personalized medicine (PM) represents a transformative approach in healthcare, utilizing computational models to tailor medical interventions based on individual patient data, including genomic, environmental, clinical, and lifestyle factors. While PM has demonstrated promising outcomes, significant challenges remain in enhancing these models’ prediction performance, interpretability, and clinical implementation. Key issues include prediction inaccuracies due to data heterogeneity, interpretability barriers associated with complex AI-driven models, and gaps in real-world application, which complicate clinical integration. Addressing these gaps through advanced computational techniques, robust validation frameworks, interdisciplinary collaboration, explainable AI (XAI), and culturally adaptive practices is essential to realizing PM’s full potential. This review highlights the importance of socio-cultural and ethical considerations, particularly in promoting equitable access and culturally sensitive healthcare, as well as ensuring data privacy and informed consent. Future research should improve model generalizability across populations, develop culturally responsive models, and advance XAI techniques for greater clinical usability. These efforts are critical for advancing personalized medicine toward more precise, effective, and equitable healthcare.