Who Is Accountable When AI Gets It Wrong at a Trial Site?
Accountability for an AI-assisted error sits with people. The investigator answers for the delegated activity, the sponsor for the trial, and the vendor for the system. This guide covers the chain link by link, the delegation record, the route an error travels and what an inspector asks for.
What AI May and May Not File in an eTMF or eISF
AI can classify a document, propose its destination and populate the index fields. The filing is an act a named person confirms. This guide covers the four steps, which document types a reading check can place, the eTMF and eISF split, and what the audit trail holds.
Validating an AI Feature in a Regulated Clinical Trial System
You specify its intended use, set an acceptance criterion as a rate rather than a single correct answer, qualify the installation, the configuration and the performance against a scripted set, and bring every model version under change control. This guide covers each step, what IQ, OQ and PQ leave open, and model change control.
How Is AI Used in Clinical Trials? A Guide for UK Research Sites
AI reads records that already exist and proposes something a person then confirms. This guide maps AI use across the five stages of a UK trial, separates the site’s list from the sponsor’s, defines the five terms vendors use interchangeably, and names the UK rules that apply.
What AI a UK Research Site Actually Uses, Role by Role
A UK research site buys four AI features in practice: drafting, document classification, cross-record flagging and natural-language retrieval. This guide sets out what each one does for a research nurse, a principal investigator, a trial pharmacist and a quality assurance lead, and which capabilities belong to the sponsor.
AI-Assisted Quality Risk Detection for UK Research Sites
Quality risk detection at a UK research site reads patterns across the deviation log, the CAPA record, the training file and the document register. This guide covers the four signals that appear only across records, what each indicates, the record that confirms it and who decides.
Automation vs AI in a Trial Platform: What a Site Should Expect From Each
Automation fires a predetermined rule and returns the same output every time, so it needs testing. AI returns a proposal that varies with its input, so it needs a named person to confirm it. This guide classes the features a trial platform ships and the evidence each class attracts.
AI for Compliance and Inspection Readiness: What a UK Site Should Look For in a Platform
A platform qualifies on AI for compliance and inspection readiness when its AI reads across the connected record, routes every flag to a named owner, and leaves the proposal, the model version and the human confirmation in one audit trail. This guide covers the claim taxonomy, a specification, a weighted scorecard and the demo proof script.
How to Complete the MHRA Computerised Systems Inspection Dossier for Your Site’s Systems
You complete the MHRA computerised systems inspection dossier by listing every system used in your trials with its version, validation status, owner and hosting. This guide covers the five places systems appear in template v7.0, the new Computerised Systems dossier, worked inventory rows and what to ask a vendor.
UK GDPR for Clinical Trial Sites: What Your Systems Must Do
UK GDPR compliance in trials rests on an Article 6 lawful basis of public task or legitimate interests, the Article 9(2)(j) research condition and the Data Protection Act 2018 safeguards. This guide covers the six legal layers, the controller split, what a platform must do, where trial data may be hosted and what the Data (Use and Access) Act 2025 changes.
