From fragmented innovation to an integrated plant-to-product framework: integrated digital twins and artificial...

Even in today's scientifically advanced era, phytomedicine still faces challenges regarding raw material authentication, chemical variability, process control, and batch variations. Although robust innovations in artificial intelligence (AI) and digital twins (DTs) are being applied to several medicinal fields, their practical implications in phytomedicine remain fragmented. Researchers are employing AI for the identification of plants, drug likeness, quality predictions, and chemometric analysis, while DTs are used in pharmaceutical manufacturing and controlled environmental agriculture. Therefore, this perspective contends for an integrated AI-assisted plant-to-product-based DT framework, connecting genotypes, medicinal plant cultivation, metabolomic, and chemometric profiling to extraction and final product design. A consistent execution of a planned and well-organized framework warrants authentic raw material alongside a validated analytical procedure, standardized metadata, and robust model validation.