Human-in-the-Loop Computational Phytopharmacology: Expert Curation, Model Oversight, Mechanistic Reasoning, and Translational Decision Confidence

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Computational phytopharmacology increasingly uses artificial intelligence, natural product informatics, target prediction, pathway analysis, safety modeling, and evidence integration to support discovery-stage decisions. However, phytopharmacological data are often chemically complex, biologically heterogeneous, and dependent on uncertain botanical identity, chemical annotation, assay context, target evidence, pathway inference, and safety interpretation. This article proposes an original human-in-the-loop framework for computational phytopharmacology in which expert curation, model oversight, mechanistic reasoning, uncertainty interpretation, translational review, and decision-confidence assessment are embedded across the discovery workflow. The framework distinguishes expert curation from data annotation, model oversight from model selection, explainability review from causal interpretation, decision confidence from clinical readiness, and translational review from regulatory endorsement. It positions human expertise before, during, and after computational inference through checkpoints for botanical identity, chemical annotation, bioactivity evidence, assay relevance, training-data suitability, model-output interpretation, uncertainty communication, bias detection, biological plausibility, exposure plausibility, safety review, herb–drug interaction review, evidence grading, validation planning, and decision-boundary control. The main contribution is a structured framework that treats computational outputs as research-prioritisation evidence requiring transparent documentation, expert disagreement recording, validation planning, audit trails, and feedback loops for data and model refinement. The framework is conceptual and does not represent an implemented, clinically validated, or regulatory-approved decision-support system.