Validation, provenance, and governance bottleneck persists in AI-driven healthcare

New educational and research examples emphasize that AI value depends on closed-loop experimentation, evidence appraisal, provenance, and independent validation rather than model output alone. Proteome-guided degrader optimization shows experimentally grounded benefit in cells and xenografts, while synthesis-aware design and TechBio closed-loop frameworks highlight unresolved chemical feasibility, reproducibility, safety, and clinical-translation requirements.