人工智慧在製藥領域的應用

Evidence must match the context of use and the consequences of error. A promising retrospective model is not automatically credible for a clinical or regulatory decision.

重點摘要

• State context of use and endpoint.
• Use risk-based validation and multidisciplinary review.
• Manage the model across its lifecycle.

Define the intended use, population, endpoint, and decision boundary. A model prioritizing compounds for laboratory study differs from one used to inform a clinical submission. Preserve the distinction between exploratory hypotheses and evidence used to support safety or effectiveness. Use documented data provenance, quality controls, and appropriate validation. Check batch effects, missing measurements, site differences, and whether the outcome label is a meaningful proxy. For time-dependent or prospective decisions, use evaluation data that respects when information becomes available. FDA and EMA guiding principles emphasize human-centered design, risk-based approaches, context of use, multidisciplinary expertise, data governance, performance assessment, and lifecycle management. Treat these as a framework for evidence and accountability, not as a blanket approval of a model. Retain versioned protocols, model outputs, and review decisions. Monitor performance after deployment and define how a change in data, assay, or model triggers reassessment.

1. Imagine a model ranking ten compounds for laboratory testing with a strong retrospective score.
2. Before using it for a patient-safety decision, define the prospective endpoint and evaluate on data collected under that protocol.
3. Record the uncertainty and require domain review at the new decision boundary.

The constructed example separates exploratory prioritization from regulated evidence.