When clinical teams deploy general-purpose AI using prompt engineering, they encounter a dangerous pattern: outputs look credible but contain errors only domain experts can catch. A protocol cites outdated ICH terminology, eligibility criteria conflict with endpoint definitions, or regulatory references are subtly wrong. These aren't bad prompts, they're architectural limitations.
ICH E6(R3) and FDA's January 2025 AI guidance introduced risk-based credibility frameworks requiring sponsors to document how AI models were trained, validated, and quality-controlled. Prompt-engineered workflows can't provide that documentation because performance varies by user and session. Research in BMC Medical Informatics found optimized prompting achieved only marginal improvements, with 11.7% residual bias patterns under safety-first conditions.
Structured clinical intelligence takes a different approach: domain-grounded knowledge from verified repositories, provenance tracking to source documents, cross-document consistency enforcement, and constrained generation within regulatory templates. This aligns with CDISC standards and FDA submission requirements, where controlled terminology and traceability reduce review errors. The difference isn't sophistication, it's system-level properties that prompt engineering alone cannot produce.