# AI and quality in life sciences
Regulatory updates, how AI is being adopted in GxP quality systems, and what it takes to validate it. Written by the QLogix team for quality, validation and regulatory affairs leaders.
- [Manufacturing science15 October 202610 min read Signal detection in GMP: how control charts with run rules catch a shift or a drift before it becomes a deviationA result inside specification can still be the first point of a shift that ends in an out-of-specification batch three lots later. Control charts with alert limits at two sigma, action limits at three and Nelson's run rules see the shift while every result still passes. When the chart, the AI's reading of each signal and QA's disposition sit on one audited record, trending stops being an annual review exercise and becomes continued process verification.The alert limit: a single result between two and three standard deviations from the centre line is a warning, not a deviation.2 σNelson's run rules: shifts, trends, alternation, stratification and mixtures that no single-point limit sees.8Signature per signal: QA's disposition, investigate, monitor or not a signal, with the reason on record.1Read the article](https://qlogix.ai/blog/spc-signal-detection-gmp-trending)
- [AI in quality15 October 20269 min read Backlog triage: ranking open deviations, CAPAs and change controls by risk instead of by ageMost quality backlogs are worked in the order they arrived, which means the deviation on a sterile batch waits behind a labelling query from March. Triage by risk ranks every open item on patient and product impact, regulatory exposure, recurrence, due dates and age, says why in one line each, and puts QA's decision on each priority on record. The queue is then worked in the order that matters, and the order is defensible.Read the article](https://qlogix.ai/blog/backlog-triage-risk-based-prioritization)
- [AI in quality15 October 202612 min read Predictive maintenance in GMP: how CMMS, calibration and reliability data decide when equipment is serviced, calibrated and replacedA preventive interval copied from a manual is a guess. The work orders, downtime and calibration results a site already records contain the answer: mean time between failures, availability, whether failures are rising with age, and the interval that catches most of them before they happen. When the CMMS, the calibration module and the quality system share one record graph, the model reads itself, the engineer confirms, and the change goes through change control.Read the article](https://qlogix.ai/blog/predictive-maintenance-cmms-calibration-gmp)
- [AI in quality14 October 202612 min read Connecting LIMS to investigations and CAPA: how AI speeds up OOS investigations and strengthens complianceMost OOS investigations lose their first days to gathering: the result, the raw data, the instrument's calibration, the standard's expiry, the analyst's history, the trend. When the LIMS, the investigation and the CAPA are one connected record graph, that evidence arrives with the result, AI reads it and drafts the hypotheses, and the effectiveness check is proven by the results that follow. The analyst, the investigator and QA still decide and sign.Read the article](https://qlogix.ai/blog/lims-investigation-capa-integration-ai)
- [AI in quality14 October 20267 min read AI in deviation investigations: faster, more consistent root cause analysisAI is most useful in an investigation at the start, where time is lost: it drafts the problem statement, the containment check, the 5 Whys and a fishbone whose causes are testable hypotheses, each quoting the write-up or an attached document. The investigator then tests, decides and signs. Nothing the AI writes becomes the root cause until a qualified person says so.Read the article](https://qlogix.ai/blog/ai-deviation-investigation-root-cause-analysis)
- [AI in quality14 October 20267 min read AI-assisted CAPA: from root cause to a verified effectiveness checkA CAPA fails for structural reasons: actions that restate the symptom, no owner or date, an effectiveness check invented at closure, and no view across records to see the same cause coming back. AI helps most before the plan is approved: proposing actions that address the confirmed cause, drafting the effectiveness criterion up front, and matching the CAPA against earlier records. People approve, implement, verify and close.Read the article](https://qlogix.ai/blog/ai-capa-management-effectiveness)
- [Validation14 October 20267 min read AI in document management and validation workflows for life sciencesDocument control under EU GMP Chapter 4, 21 CFR Part 11 and Annex 11 is about controlled versions, approvals, effective dates, distribution and periodic review. AI changes who writes the first draft and who finds the gaps, not what must be controlled. The same holds for validation: plans, protocols and reports drafted from templates and requirements, with assurance effort set by risk under GAMP 5 and FDA's CSA guidance, and a person approving every version with an electronic signature.Read the article](https://qlogix.ai/blog/ai-document-management-validation-workflow)
- [AI in quality12 October 20264 min read FDA's seven-step credibility framework for AI, in plain termsFDA's draft guidance asks sponsors to show that an AI model is credible for a specific context of use. The effort scales with model risk: how much the model's output drives the decision, and how serious a wrong decision would be.Read the article](https://qlogix.ai/blog/fda-ai-credibility-framework)
- [Manufacturing science12 October 20264 min read AI in drug manufacturing: where FDA stands and what to build forFDA has no final guidance written only for AI in drug manufacturing. It has a 2023 discussion paper naming five open areas and a 2025 draft framework that covers manufacturing models. Together they show what a process model has to prove.Read the article](https://qlogix.ai/blog/ai-in-drug-manufacturing-fda)
- [Medical devices12 October 20264 min read AI-enabled medical devices: what FDA's list and guidance showFDA's public list counted 1,614 AI-enabled medical devices through June 2026, about three in four of them in radiology. Two documents shape how they are built and changed: the final guidance on predetermined change control plans and the draft guidance on lifecycle management.Read the article](https://qlogix.ai/blog/ai-enabled-medical-devices-fda)
- [Manufacturing science12 October 20263 min read Robotics in aseptic manufacturing: what Annex 1 expectsThe revised EU GMP Annex 1 names robotic systems, with isolators and RABS, as technologies to consider for protecting sterile products. Taking the operator out of the critical zone removes the largest contamination source and moves the quality work to design, software and data.Read the article](https://qlogix.ai/blog/robotics-aseptic-manufacturing-annex-1)
- [Regulatory updates12 October 20264 min read EU GMP Annex 22: what the draft AI annex asks of GMP manufacturersDraft Annex 22 is the first GMP annex written for AI. It allows static, deterministic models in critical GMP applications, keeps generative AI out of them, and asks for a defined intended use, independent test data and ongoing monitoring.Read the article](https://qlogix.ai/blog/eu-gmp-annex-22-ai-draft)
- [Validation12 October 20264 min read Computer software assurance: what FDA's final guidance changes for validationFDA's final guidance on computer software assurance replaces test-everything validation with effort matched to risk. Features that could compromise safety get scripted testing; the rest can be covered by unscripted testing and supplier evidence.Read the article](https://qlogix.ai/blog/computer-software-assurance-final-guidance)

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Source: https://qlogix.ai/blog
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