Pharma AI glossary

Machine learning (ML)

Building models whose behavior is learned from data rather than explicitly programmed.

Building models whose behavior is learned from data rather than explicitly programmed. The umbrella over everything here except pure rule-based automation; the regulatory texts (Annex 22, GAMP's ML appendix, FDA's guidance) are really about ML's defining property, statistical behavior learned from data, and its consequences: bias, drift, opacity, and the need for evidence-based rather than specification-based assurance.

Learned behavior vs programmed rules

Traditional software executes rules a human wrote; ML models learn patterns from examples encoded in millions of parameters. Training builds the model; inference applies it to new data. Outputs are probabilistic (usually right, sometimes confidently wrong), which is why GxP teams validate differently than for deterministic code.

In pharma practice

Vision inspection, environmental and equipment monitoring, predictive maintenance, and sample triage are common ML entry points on the manufacturing floor. Literacy means understanding input space limits, drift, independent test data, and when a confidence score is meaningful versus off-label input, the same theme FDA credibility and Annex 22 stress from different angles.

Frequently asked questions

Do we validate ML models like CSV?

Lifecycle and intent overlap, but evidence emphasizes data representativeness, performance subgroups, monitoring, and change control, not only script execution. CSA and GAMP 5 AI guide frame risk-based depth.

What is the difference between ML and AI?

In practice, AI is the umbrella (including LLMs and agents); ML is models trained from data. Regulators care about the specific system, its context of use, and consequences, not the marketing label.

Where does ML most often appear in GMP?

Automated visual inspection, process and environmental anomaly detection, predictive maintenance, and low-consequence triage before human follow-up. These paths often carry lower decision consequence than release or batch disposition, which shapes validation depth under FDA credibility and Annex 22 draft.

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Educational content only, not legal or regulatory advice. Regulatory guidance cited here includes drafts (FDA AI credibility guidance; EU GMP Annex 22) as of August 2026; verify against final texts before relying on them in submissions. Company-reported figures (Merck CSR timings, Sanofi results) are labeled where used. MIT's ~95% pilot figure carries its own caveat (~150 interviews, contested definitions, not peer-reviewed). Re-check sources on module finalization.

Definition