Medicina de precisión e IA: futuras estrategias de tratamiento y diagnóstico de MASLD

Artificial intelligence (AI) has transformed the diagnosis and management of metabolically dysfunction-associated fatty liver disease (MASLD). Machine learning models integrate clinical data, biochemical biomarkers, and imaging to non-invasively detect and stratify the disease, with algorithms achieving AUCs as high as 0.91 for advanced fibrosis3. Precision medicine incorporates multi-omics data—genomics, lipidomics, transcriptomics, proteomics—to identify biomarkers of early mechanistic risk and design personalized interventions1,2. AI also enables mapping the systemic interconnection between MASLD, insulin resistance, and type 2 diabetes, improving cardiovascular and renal risk stratification4. The use of explainable methods such as SHpley Additive exPlanations (SHAP) increases clinical confidence in these models1. Key challenges include high data dimensionality, cohort heterogeneity, and the need for external validation in diverse populations to ensure generalizability2,3. The transition to causal inference models and the integration of dynamic monitoring into clinical decision support algorithms represent the most promising prospects1,2.