Fictional worked example · CSVtoCSA editorial

AI-Assisted Deviation Review

An AI assistant proposes deviation themes and similar records while human investigators retain classification, root-cause, and approval authority.

Published 12 September 2026 · Context updated 21 September 2026 · Version 2.0

All organizations, identifiers, test results and decisions in this example are fictional teaching material.

Information available and assumptions

The AI supports deviation review using controlled sources and a defined parser/retrieval configuration. Independent expected findings include scanned content.

Scope and decision record

Standard case study record

Scope and decision fields

Read these fields before the detailed worked case. All organizations, identifiers, test outcomes, and decisions are fictional teaching material.

GxP Assessment
Missed critical similarity, false linkage, obsolete source, privacy exposure, or automation bias can affect investigation quality and timeliness.
Change or Issue
A retrieval-augmented AI assistant proposes related deviations, common themes, and missing investigation questions for a qualified investigator.
Functions Affected
  • Record retrieval and similarity
  • Source version and access
  • Proposed theme and question generation
  • Reviewer disposition
  • Monitoring and change assessment
Potential Impact
The investigation can overlook recurring failure or pursue an unsupported conclusion.
Deviations / Known Issues
The fictional evaluation misses two related deviations stored only in scanned attachments after a parser change.
In this example

Situation

A retrieval-augmented AI assistant proposes related deviations, common themes, and missing investigation questions for a qualified investigator.

Intended Use

Support investigation preparation using approved records. The AI cannot classify severity, determine root cause, approve a conclusion, initiate CAPA, or close a record.

Decision Summary

Decision elementCase record
GxP assessmentMissed critical similarity, false linkage, obsolete source, privacy exposure, or automation bias can affect investigation quality and timeliness.
Potential impactThe investigation can overlook recurring failure or pursue an unsupported conclusion.
Known issueThe fictional evaluation misses two related deviations stored only in scanned attachments after a parser change.
Validation conclusionScanned attachments are excluded and the update is not released until critical-miss criteria and reviewer controls pass.

GxP Assessment

Missed critical similarity, false linkage, obsolete source, privacy exposure, or automation bias can affect investigation quality and timeliness.

Affected GxP Functions

  • Record retrieval and similarity
  • Source version and access
  • Proposed theme and question generation
  • Reviewer disposition
  • Monitoring and change assessment

Failure Scenarios

  • Critical prior deviation is missed
  • Unrelated record is presented as similar
  • Restricted record is retrieved
  • Unsupported root-cause wording is accepted
  • Model update changes performance without detection

Potential Impact

The investigation can overlook recurring failure or pursue an unsupported conclusion.

Existing Controls

  • Read-only approved corpus
  • Source citation
  • Role-filtered retrieval
  • Qualified reviewer disposition
  • No autonomous Quality action

Existing Evidence

  • Independently labeled reference set
  • Security assessment
  • Human-workflow study
  • Audit and version records

Evidence Gaps

  • Rare critical events
  • Poor-quality attachments
  • New model version
  • Reviewer workload effect

Testing Approach

  • Evaluate correct links, false links, missed critical links, and source correctness by record type and criticality
  • Challenge prompt injection and restricted access
  • Test reviewer detection and override under representative workload
  • Verify monitoring, stop, rollback, and revalidation criteria

Deviations or Known Issues

The fictional evaluation misses two related deviations stored only in scanned attachments after a parser change.

Validation Conclusion

Scanned attachments are excluded and the update is not released until critical-miss criteria and reviewer controls pass.

Reassessment Criteria

  • Model, prompt, parser, retrieval, source, access, or workflow change
  • Critical miss or privacy incident
  • Acceptance-criteria or supported-population change

Limitations and unresolved questions

Scanned material remains outside the permitted reliance until the critical misses are addressed. A parser change requires targeted evaluation and human-review evidence.

Relevant regulations and guidance

These sources apply within their stated scope. The suggested worksheet fields, test designs and fictional decisions are practitioner recommendations, unless a source requirement is expressly identified. Completing a template does not establish compliance.

Computer Software Assurance for Production and Quality Management System Software ↗

Regulatory guidance / GMP guideline · United States · February 2026

Scope: Medical-device manufacturers assuring software used in production or their quality management system.

Relevance: For medical-device production or QMS software, supports selecting assurance activities from intended use, risk and available evidence. It is nonbinding guidance and does not determine other sectors’ obligations.

Status: Final / published · Checked 2026-09-21

Publication identity, scope and status checked against the linked official source. This is not an independent audit of every retained guide statement.

Read source details and applicability →

EudraLex Volume 4, Annex 11 — Computerised Systems ↗

Regulatory guidance / GMP guideline · European Union · January 2011 revision

Scope: Computerised systems supporting activities in the applicable EU pharmaceutical GMP context.

Relevance: For computerised systems used in EU GMP activities, informs lifecycle, supplier and record controls. Confirm applicability to the actual activity; the example does not establish it.

Status: Final / published · Checked 2026-09-21

The Commission index confirms the January 2011 operative revision. The linked PDF could not be retrieved in this check; retained detailed section mappings need review against an authorized copy.

Read source details and applicability →

Artificial Intelligence Risk Management Framework (AI RMF 1.0) and NIST AI 600-1 ↗

Industry guidance / framework · United States · AI RMF 1.0 (2023); GenAI Profile (2024)

Scope: Cross-sector AI governance and risk management, including generative AI.

Relevance: A voluntary framework for describing AI risks, evaluation and ongoing management. Its suggested practices are not regulatory requirements.

Status: Final / published · Checked 2026-09-21

Publication identity, scope and status checked against the linked official source. This is not an independent audit of every retained guide statement.

Read source details and applicability →
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