Artificial Intelligence for Precision Phytomedicine: A Critical Review and the Patient–Product–Mechanism–Response ( PPMR ) Framework

Plant‐derived preparations remain widely used, yet their clinical application is constrained by multicomponent composition, batch‐to‐batch chemical variability, and complex pharmacokinetics, poorly characterized interactions, and marked interindividual variability in response. Artificial intelligence (AI) and computational pharmacology offer analytical tools suited to the high‐dimensional data generated by phytomedicine, but enthusiasm has outpaced the supporting evidence. This critical narrative review evaluates AI applications across the phytomedicine pipeline, grading them by the strength and directness of evidence from computational prediction through in vitro, animal, observational, and randomized clinical studies. We organize the field around a Patient–Product–Mechanism–Response (PPMR) framework, arguing that meaningful personalization requires joint characterization of both the patient and the chemically variable botanical product. Current applications are most mature for botanical authentication, spectral annotation, quality assurance, and mechanism‐informed hypothesis generation, whereas validated tools for patient stratification, individualized product and dose selection, and improved clinical outcomes remain limited. Rather than claiming efficacy gains, we present AI as a means to advance standardized, mechanism‐informed, and testable phytomedicine, contingent on higher‐quality data, prospective external validation, and regulatory alignment. The central finding is that none of the studies examined demonstrated a complete AI‐guided personalized‐phytomedicine pathway from joint patient‐and‐product characterization through a prospectively validated individualized response; precision phytomedicine should therefore be described as an emerging and hypothesis‐generating research programme rather than an established clinical capability.