Multimodal AI and Machine Learning for Predictive Risk Stratification, Early Detection, And Clinical Management of Pica: Integrating Etiological and Pathophysiological Biomarkers

Pica is a clinically significant and often overlooked feeding and eating disorder in which individuals have a persistent urge to eat or chew non-nutritious or non-food items and is linked to nutritional, hematological, behavioral, and environmental issues. Human studies have regularly found links between pica and iron deficiency and anemia, as well as between changes in hematological parameters, decreased ferritin, and decreased zinc concentrations; toxic-element exposure may also be a part of certain behaviors (like geophagia). The review critically summarises the potential use of multimodal artificial intelligence (AI) and machine learning (ML) to support predictive risk stratification, early detection, and clinical management of pica, highlighting the importance of combining etiological and pathophysiological biomarkers, clinical, behavioural, toxicological and electronic health-record data. Biomarker evidence also indicates human exposure to multiple biomarkers together may be a more complete risk profile than single biomarkers, since there are human data from which to draw conclusions. AI and natural language processing could be used to identify undetected pica-related behaviors and track changes over time to clinical and laboratory data. However, there are still limitations in the development and validation of pica-specific AI models, and existing evidence from related conditions mainly methodologically supports the use of AI. Further studies are needed on large, prospective, diverse human cohorts, standardized pica phenotyping, longitudinal assessment of biomarkers, explainable AI, external validation, and prospective clinical assessment. Finally, multimodal AI should be used as a clinically interpretable decision support system, not as an independent diagnostic system.