Log: AI-Based Automated Visual Inspection Performance and Drift Monitoring

This is a ready-to-use ongoing monitoring log for an AI-based automated visual inspection (AVI) system. It is a continuous, multi-period record, distinct from a single point-in-time periodic monitoring review: it captures the physical and operational signals specific to an inspection line, the reject rate and per-class reject mix, imaging-condition readings, the independent AQL sampling feedback signal, and human pre-sort override rate, so a drift is visible in the trend before it is visible in any single review. Use it alongside your model monitoring SOP, which sets the cadence and escalation this log’s entries feed. Replace every <<FILL: ...>> placeholder, set your own document numbers, and route it through your normal document control. A worked specimen with real arithmetic follows.

Document control header

Field	Entry
Document title	AI-Based Automated Visual Inspection Performance and Drift Monitoring Log
Document number	`<<FILL: DOC-ID>>`
Version	`<<FILL: version>>`
AVI machine, model, and version	`<<FILL>>`
Governing SOP	`<<FILL: SOP-ID for model monitoring>>`
Logging cadence	`<<FILL: e.g. weekly automated pull, reviewed monthly, with an event-driven check on any trigger>>`
Data source	`<<FILL: AVI machine data historian / MES report reference>>`

1. What is logged, and why it is not the same as a generic AI monitoring review

A generic AI monitoring review trends input-distribution drift, an override rate, an output-class mix, and a confidence distribution. Those signals matter here too, but an AVI line produces additional signals that a generic template has no field for, because they are physical, not purely statistical: an imaging-condition reading, a reject count broken out by defect class rather than a single blended mix, and the independent AQL manual-sampling result as a feedback signal on the automated step itself. Log all of the following every period.

Field	Format	Required	Who	When
Units inspected	count	Yes	System extract	Each period
Total rejects and reject rate	count, percent	Yes	System extract	Each period
Reject count by defect class	count per class	Yes	System extract	Each period
Imaging-condition readings (lamp intensity, focus, exposure)	value vs qualified range	Yes	Equipment / automated log	Each period
AQL feedback signal (defect found at AQL that AVI should have caught)	count, description	Yes	QC / AQL inspection record	Each lot released
Human pre-sort override rate (where a pre-sort step exists)	percent	Conditional	Reviewer log	Each period
Periodic re-challenge result	pass/fail per class, with numbers	Yes	Validation / Inspection SME	Per defined cadence and on trigger

2. Trigger thresholds

Signal	Baseline	Action limit	Basis
Aggregate reject rate	`<<FILL: e.g. 1.78% established at qualification>>`	`<<FILL: e.g. +/- 0.25 percentage points>>`	`<<FILL: derived from observed period-to-period variation during qualification>>`
Per-class reject share (any single class)	`<<FILL: baseline share of total rejects>>`	`<<FILL: e.g. a fall of more than one-third from baseline share with no corresponding process change>>`	`<<FILL: a per-class fall inside a stable or falling aggregate is the signature of a capability loss, not a genuine improvement>>`
Imaging-condition reading	Qualified range	Out of qualified range, or `<<FILL: e.g. within 10% of the qualified limit for 2 consecutive periods>>`	Physical drift driver, acts before detection visibly breaks
AQL feedback signal	Zero AVI-missed defects expected	Any confirmed AVI-missed defect at AQL	A direct signal the qualified capability may have degraded
Human pre-sort override rate	`<<FILL: baseline>>`	`<<FILL: e.g. rate rising above baseline for 2 consecutive periods, or a near-zero override rate suggesting rubber-stamping>>`	Both a rising and a vanishing override rate are signals

Do not read the aggregate reject rate alone. A stable or falling aggregate rate can conceal a single defect class losing detection while other classes hold steady; the per-class row exists specifically to catch that pattern, which the specimen below demonstrates.

3. Periodic entry (repeat one row set per period)

Period	Units inspected	Total rejects	Reject rate	Class A rejects	Class B rejects	Imaging reading vs range	AQL feedback	Override rate	Action limit crossed
`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	Yes / No

4. Trigger response record (complete on any crossing)

Field	Entry
Trigger that fired	`<<FILL>>`
Verification performed (data error ruled out)	`<<FILL>>`
Re-challenge executed, class and result	`<<FILL>>`
Root-cause line pursued (imaging, product envelope, training data, other)	`<<FILL>>`
Action taken (continue / tighten monitoring / hold and revert to qualified manual fallback / retrain)	`<<FILL>>`
Change control or investigation reference	`<<FILL: number or N/A>>`
Disposition	`<<FILL>>`
Reviewer (name, signature, date)	`<<FILL>>`
QA review (name, signature, date)	`<<FILL>>`

5. Acceptance criteria

Every period has a complete row; a missed logging period is itself recorded, not silently absent.
The per-class reject mix is logged every period, not only the aggregate rate.
Every action-limit crossing has a completed trigger response record before the next period closes.
Any confirmed AQL-feedback signal opens an investigation, not just a lot disposition.
The log is reviewed on its stated cadence and retained per <<FILL: retention period>>.

References

USP General Chapter <1790>, Visual Inspection of Injections, on ongoing capability and periodic challenge. USP General Chapter <790>, Visible Particulates in Injections, on the AQL sampling step this log treats as a feedback signal. ICH Q9, Quality Risk Management, for the risk basis of the action limits. 21 CFR Part 11 and EU GMP Annex 11, for the log as a controlled GxP record.

Confirm the current version of each reference before issue.

Revision history

Version	Date	Author	Summary of change
`<<FILL: 1.0>>`	`<<FILL: date>>`	`<<FILL: author>>`	Initial issue.

Approvals

Role	Name	Signature	Date
Author	`<<FILL>>`
QA	`<<FILL>>`

Filled specimen

Six weekly entries for a vial-line AVI system, model clf-v3.2, baseline reject rate 1.78 percent established at qualification, with roughly two-fifths of rejects normally from the particulate class. Numbers are illustrative.

Week	Units inspected	Total rejects	Reject rate	Particulate-class rejects	Cosmetic/fill-level rejects	Imaging reading	AQL feedback	Override rate	Action limit crossed
1	82,400	1,470	1.78%	610	860	Within range	None	6%	No
2	81,900	1,455	1.78%	605	850	Within range	None	6%	No
3	83,100	1,440	1.73%	615	825	Within range	None	7%	No
4	82,700	1,300	1.57%	420	880	Lamp intensity at 92% of qualified minimum	None	6%	No, watched
5	81,500	1,180	1.45%	330	850	Lamp intensity at 87% of qualified minimum	One confirmed particulate found at AQL	6%	Yes
6	82,900	1,050	1.27%	250	800	Lamp replaced mid-week; post-replacement reading within range	None since replacement	6%	Yes, carried from week 5

Trigger response record, week 5: the aggregate reject rate had fallen inside what could be read as normal improvement, but the per-class row showed particulate-class rejects down from a 610-unit baseline to 330, more than a third, with cosmetic and fill-level rejects flat. The same week, AQL sampling on a released lot found a particulate the automated step should have caught. Verification ruled out a data or reporting error. Root cause traced to lamp intensity drifting to 87 percent of the qualified minimum, reducing contrast on small particulates specifically, consistent with the class-specific pattern. Action: line held for particulate-class inspection, reverted to the qualified manual fallback for that shift, lamp replaced, imaging re-qualified against the qualified conditions, and a re-challenge of the particulate class confirmed detection restored before the automated step resumed. Investigation reference <<FILL>> opened for the AQL-feedback finding, with the released lot’s disposition handled separately per the batch-release procedure.

Common inspection findings this log prevents

Only the aggregate reject rate is trended, so a falling rate is read as good news and a per-class capability loss is missed entirely.
Imaging-condition readings are collected by equipment engineering but never cross-referenced against the reject-rate trend, so the causal link is found late or not at all.
An AQL-feedback finding (a defect found manually that the automated step should have caught) is dispositioned as a single lot event with no link back to the AVI monitoring log or an investigation into capability.
A rising or vanishing human pre-sort override rate is not tracked, so automation bias in the pre-sort step is invisible until an unrelated audit surfaces it.
Logging gaps are silent: a missed period is indistinguishable from a period with nothing to report.

How to adapt this log

Replace the two illustrative defect-class columns in sections 3 and 6 with your actual defect catalog; log every class with a materially different baseline share, not just the highest-volume one.
Set your own baseline reject rate, per-class shares, and action limits from your qualification data, not the specimen’s numbers.
Point the AQL-feedback field at your real batch-release and complaint-investigation procedures so a confirmed miss is never dispositioned as a standalone lot event.
Set the logging cadence and the review cadence separately if your system supports automated weekly extraction with a monthly formal review; state both in the header.
Confirm every reference against its current published version before issue.