Responsible AI for Phytopharmaceutical Innovation: Scientific Validity, Ethical Sourcing, Explainability, Reproducibility, and Clinical Trust

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Artificial intelligence is increasingly used to support phytopharmaceutical innovation through natural product discovery, compound prioritization, mechanism interpretation, safety assessment, and translational decision-making. Yet responsible use of AI in this field requires more than technical model performance, because phytopharmaceutical research involves complex biological materials, variable phytochemical composition, heterogeneous evidence, traditional knowledge contexts, biodiversity concerns, and clinically sensitive interpretations. This article develops an original responsible innovation framework for AI-enabled phytopharmaceutical research. The framework connects scientific validity, botanical and phytochemical integrity, data provenance, ethical sourcing, traditional knowledge sensitivity, biodiversity stewardship, explainability, reproducibility, uncertainty communication, evidence grading, human expert review, clinical trust, translational safeguards, and governance accountability. It argues that AI outputs in phytopharmaceutical innovation should be treated as decision-support evidence rather than proof of efficacy, safety, mechanism, ownership, or clinical readiness. Scientific validity is positioned as the foundation of responsible AI, requiring clear research questions, reliable botanical identity, chemical characterization, data quality, and validation planning. Ethical sourcing is treated as inseparable from scientific trust because provenance, cultural context, benefit-sharing awareness, and ecological stewardship shape the legitimacy of data reuse. Explainability and reproducibility are presented as necessary but insufficient safeguards that must be combined with uncertainty communication and expert review. The framework offers a structured governance logic for cautious, transparent, and evidence-aware AI-enabled phytopharmaceutical innovation.