Clinical Trials AI Buyer Checklist
A buyer should not evaluate clinical-trials AI from a feature list or a single accuracy number. Start with the protocol-bound task, name the person who keeps decision authority, and ask for evidence that matches the proposed population, sites, data sources, system version, and consequence of error. A demo becomes decision evidence only when reviewers can trace what the system did, reproduce the test conditions, and explain what stops a pilot.
This guide is for healthcare technology research and procurement planning. It is not medical, clinical, legal, privacy, security, regulatory, or compliance advice. It does not approve a vendor or replace sponsor, investigator, IRB, quality, privacy, security, legal, or regulatory review. Use the broader healthcare AI vendor evaluation checklist for common procurement controls; use this page to examine trial-specific workflow, records, participant protections, and change control.
1. Map the trial workflow and authority
Define one intended workflow before requesting a demo. It might be protocol feasibility, cohort identification, eligibility prescreening, participant matching, remote data acquisition, data review, monitoring support, or document quality control. Do not combine these into a single use case: they have different inputs, users, failure modes, and downstream decisions.
Create a workflow-and-authority map that names the trigger, protocol version, source systems, minimum data, AI output, user who reviews it, action that follows, and person who can pause processing. Separate a suggestion from a final trial decision. The ICH E6(R3) Good Clinical Practice guideline supports proportionate quality management, fit-for-purpose computerized systems, clear responsibilities, and protection of participants; it does not certify any AI vendor or assign identical controls to every system.
Write exclusions beside the intended use. State which protocols, sites, countries, populations, languages, devices, and data sources are outside the review. If an AI output affects eligibility, safety follow-up, consent communication, or trial data used for decisions, the map should show the qualified human and governed process that retain authority.
2. Match evidence to the intended context of use
Ask the vendor for a context-of-use evidence matrix, not a general claim that the model is validated. For each evidence item, compare the task, protocol, population, setting, data source, label or reference standard, model and software version, reviewer workflow, outcome measure, limitations, and date with the proposed deployment.
The FDA and EMA guiding principles for good AI practice in drug development frame AI credibility around a clearly defined context of use, risk-based evidence, human-centric design, data governance, performance assessment, and lifecycle management. They are guiding principles, not product approval or a substitute for application-specific regulatory advice. For a voluntary cross-sector risk vocabulary, the NIST AI Risk Management Framework can help teams organize governance, measurement, monitoring, incident response, and change management without turning the framework into a compliance claim.
Request results for relevant exclusions and difficult cases, not only average performance. Evidence from a different population, site environment, protocol task, data source, or version should be labeled as indirect. The buyer must record what local verification is still required and who decides whether the residual evidence gap is acceptable.
3. Trace data, records, and participant protections
Build a data-and-records map from collection to deletion. Identify protocol fields, participant identifiers, consent or authorization status, source records, derived variables, eCRF data, digital measurements, queries, safety records, and audit trails only when they are actually in scope. For every element, record the source of truth, purpose, recipients, transformations, correction path, retention, secondary use, and deletion or archival process.
FDA's final guidance on electronic systems, electronic records, and electronic signatures in clinical investigations supports asking whether relevant systems are fit for intended use and whether records preserve integrity, metadata, authorized access, audit trails, backup, recovery, and inspection-ready access. It does not mean every record is subject to 21 CFR part 11 or that a generic security certificate proves trial-record integrity.
Participant-facing workflows need a separate protection review. The HHS OHRP informed consent FAQs describe disclosure, understanding, voluntariness, questions, and prospective consent where required. An AI interface or signed form does not by itself establish comprehension or authorize automated consent decisions. Confirm the applicable IRB, investigator, consent, privacy, and authorization requirements with qualified owners. For PHI-related research pathways, use the HHS OCR research guidance to structure questions, while avoiding the assumption that HIPAA applies to every dataset or organization.
4. Test exceptions and implementation burden
Design an exception test log before the pilot. Include at least one clear inclusion, clear exclusion, incomplete record, conflicting source, changed protocol criterion, disputed output, unavailable integration, and attempted out-of-scope request. For each case, record expected behavior, observed behavior, reviewer, correction, downstream effect, detectability, escalation, and whether processing can be paused safely.
If the proposed workflow uses a digital health technology to collect participant data remotely, FDA's DHT guidance for remote data acquisition in clinical investigations supports examining fitness for purpose, verification, validation, usability, the intended population and environment, training, missing data, transmission, and risk management. That guidance does not automatically apply to every AI workflow, and an authorized commercial device is not automatically fit for a particular endpoint or trial.
Measure implementation work as part of the evaluation: interface build, identity and access setup, data mapping, protocol configuration, site training, support coverage, monitoring, reconciliation, incident handling, rollback, and retirement. A product that performs well in a controlled sample can still fail the buying decision if the organization cannot operate its controls under normal workload.
5. Set pilot measures and change-control gates
Create a pilot decision record before go-live. Select measures that match the workflow, such as reviewer time, eligibility agreement, missing-data rate, unresolved discrepancy count, protocol-deviation signal review, participant support load, site support load, correction volume, and system availability. Define the numerator, denominator, exclusions, baseline window, pilot window, source system, owner, and action threshold for each chosen measure. These are candidate measures, not universal benchmarks.
Predefine pass, fail, pause, and investigate conditions. Do not offset a failed participant-protection, privacy, record-integrity, safety, or auditability gate with time savings. The decision record should show open evidence, accountable owner, due date, and the condition required before any scope expansion.
Treat changes to the protocol, population, site, endpoint, device, source data, model, software, interface, or reviewer workflow as review events. The vendor should state how it versions the system, notifies customers, assesses impact, tests regressions, supports rollback, and reconstructs historical outputs. The buyer should decide in advance which changes require documentation, renewed verification, or a reset of approval.
Procurement questions to ask
Use role-owned questions so missing evidence remains visible instead of becoming a vague group approval.
Sponsor or program owner
- What exact protocol task and version is in scope, and which use cases are explicitly excluded?
- Which decision remains with the sponsor or delegated qualified reviewer, and who can pause the workflow?
- What evidence must be complete before a site, country, population, or protocol expansion is considered?
Site and investigator team
- Where does the output appear in daily work, and what source evidence can the reviewer inspect before acting?
- How are incomplete, conflicting, excluded, or disputed cases routed and documented?
- What training, support, downtime procedure, and participant communication burden will the site absorb?
Data management and biostatistics
- Which fields are source data, derived data, model inputs, outputs, and corrections, and can lineage be reconstructed?
- Do validation results match the proposed population, data source, protocol task, endpoint role, and deployed version?
- How will missingness, disagreement, drift, version changes, and downstream reconciliation be measured?
Privacy, security, quality, and regulatory reviewers
- What data crosses each system boundary, for what purpose, for how long, and with which subprocessors or secondary uses?
- Which access, audit-trail, retention, backup, recovery, incident, and inspection-support evidence applies to this configuration?
- Which claims depend on a particular jurisdiction, regulated record, consent or authorization pathway, and who confirms applicability?
Trial AI evidence matrix
This matrix is an editorial planning framework, not a validated instrument, regulatory checklist, or vendor score. Adapt it to the protocol and ask qualified owners to determine applicable obligations.
Gate 1: intended use and authority
Evidence to request: workflow-and-authority map, protocol version, exclusions, reviewer, and pause path. Stop condition: the output or downstream action has no named qualified owner. Accountable owner: sponsor or delegated workflow owner.
Gate 2: evidence fit
Evidence to request: context-of-use matrix, validation report, limitations, dataset description, and deployed version. Stop condition: evidence cannot be matched to the proposed task, population, setting, data, or version. Accountable owner: clinical, data science, and quality reviewers.
Gate 3: data and records
Evidence to request: data flow, lineage, access model, audit trail, correction, retention, backup, and recovery evidence. Stop condition: required records cannot be reconstructed or corrected. Accountable owner: data management, quality, privacy, and security owners.
Gate 4: participant protection
Evidence to request: IRB-approved process where applicable, consent or authorization workflow, accessible communication, and escalation path. Stop condition: the workflow bypasses required review or cannot protect voluntariness and questions. Accountable owner: investigator, IRB, privacy, and participant-support owners.
Gate 5: exception handling
Evidence to request: exception test log, downtime plan, escalation, pause, and reconciliation evidence. Stop condition: a high-impact failure is silent, unowned, or not recoverable. Accountable owner: site operations and sponsor quality owners.
Gate 6: pilot and change control
Evidence to request: baseline, measures, thresholds, version notices, regression plan, rollback, and review-reset rules. Stop condition: expansion can occur without measured evidence or renewed review. Accountable owner: program owner and governance group.
FAQs
What should buyers verify first for clinical trials AI?
Verify the intended protocol task, excluded uses, accountable human decision-maker, and stop path. Then ask whether the available evidence matches the proposed population, site environment, data source, and deployed version.
Can an AI system make the final eligibility decision?
This guide does not authorize that use. The buyer should map how protocol criteria are applied, what evidence the investigator or qualified reviewer sees, how disagreements are recorded, and which human-governed process retains decision authority.
What evidence should a vendor provide before a pilot?
Request a context-of-use statement, validation and limitation documentation, data-flow and lineage map, record-integrity controls, exception results, implementation plan, monitoring plan, version/change policy, incident path, and references from comparable settings. A document being present is not proof that it is adequate for the proposed trial.
Does a security certification prove the tool is ready for clinical trials?
No. A certification may be one input to security review, but it does not establish protocol fit, record integrity, participant protection, regulatory applicability, local workflow safety, or vendor approval.
Which pilot metric matters most?
There is no universal metric. Choose measures that match the task and pair benefit with harm, rework, missingness, disagreement, support burden, and unresolved risk. Define thresholds and owners before the pilot so the vendor cannot redefine success after results are available.
Evidence boundary
The cited FDA, HHS/OHRP, ICH, and NIST materials define evidence questions and boundaries for their stated scopes. They do not establish that a named product is safe, accurate, validated, compliant, approved, or appropriate for a local trial. This article reports no product tests, sponsor or site interviews, customer results, legal analysis, or local performance measurements. Document each unanswered or inapplicable evidence request as an open gate, not a favorable score.