Turning AI Governance Frameworks into Product Decisions in Life Sciences
An AI governance policy becomes useful when it changes a product decision. It may limit the population in which a tool is used, require better source controls, prevent an unsupported claim, or allow a well-evaluated application to proceed. A completed checklist cannot demonstrate any of those outcomes on its own.
Life sciences organizations already have experience making consequential decisions under uncertainty. Clinical development, diagnostic validation, quality systems, and regulatory strategy require evidence that fits a particular use. AI governance should connect to that discipline rather than operate as a separate vocabulary of principles.
My work across oncology, diagnostics, devices, and partnerships has taught me to look for the operational detail behind a policy. Who owns the output? Can the source and version be reconstructed? Can a qualified person challenge it before action? Who can pause the function when the input fails? These questions identify whether the controls are usable.
The method below treats governance as a sequence of product decisions with accountable owners. A framework organizes the review; the application’s data, users, evidence, and consequences determine the answer.
Begin with the intended decision
The team should write a short intended-use statement in plain language. Who uses the tool? What input does it accept? What output does it produce? What action may follow? In which population, setting, and time frame? What is outside scope? This exercise is more useful than classifying the tool as simply “AI in healthcare.”
Compare three systems. One summarizes publicly available research for an internal scientist. Another ranks possible trial sites for a study team. A third recommends a clinical intervention for a patient. They may share a model provider, but their data, error costs, users, and regulatory questions differ. A single review checklist cannot substitute for an analysis of each intended use.
The intended-use statement should distinguish advice from automation. “Human in the loop” is too vague. Does the user review every output before action? Can the system prefill a document that is later approved? Does an alert trigger a clinical protocol before anyone checks its basis? The meaning of oversight depends on timing and authority.
NIST’s AI Risk Management Framework uses functions such as govern, map, measure, and manage. I find it helpful as a structure for these conversations. It is voluntary and broad. The team still needs to translate each function into product-specific evidence and control. [1]
Microsoft’s Dragon Copilot documentation describes draft generation for clinician review. This published boundary is useful when discussing governance: assessment of a draft assistant should not be represented as evidence for an autonomous clinical decision system. The permitted output, reviewer action, and point of transfer into the clinical record all belong in the use statement. An organization that expands the workflow has to reassess the new reliance it is asking users to place on the system. [4]
Map claims to evidence
The product team should list every consequential claim it intends to make. “Finds relevant studies” differs from “identifies eligible patients.” “Drafts a summary” differs from “produces an approved medical response.” Each claim needs a population, performance measure, evaluation dataset, comparator, and known limitations.
For a clinical trial operations model, define a decision baseline and test whether the tool improves a relevant outcome prospectively or in a credible retrospective simulation. For a medical content assistant, evaluate source fidelity, omissions, unsupported claims, review burden, and version traceability. For a patient-facing device function, the applicable clinical, quality, and regulatory evidence may be substantially more demanding.
The test set should reflect the actual inputs, including incomplete and difficult cases. An evaluation on polished examples may miss the errors that matter in routine work. The team should state which groups and contexts are underrepresented. It should avoid claiming broad reliability from a narrow validation sample.
Evidence can be staged. An exploratory prototype may justify a controlled pilot. A pilot may justify a broader deployment after performance and workflow are assessed. The gate between stages should be written in advance. This reduces the temptation to redefine success after seeing favorable results.
Assign owners to data and outputs
Data governance and AI governance intersect at every consequential input. Document the source, permission, provenance, quality expectations, retention, and permitted use of each dataset. If the system retrieves regulated or confidential documents, access controls must apply to both the source and generated answer. A model should not make a restricted document available to a broader user through a friendly interface.
The output needs ownership too. Who checks it before use? Where is it recorded? Can a user correct it, and does that correction alter the system? Who decides whether an output is an incident? A vendor may own the model service while the life sciences company owns the regulated decision. The boundary must be explicit in contracts and operating procedures.
Keep a trace linking a consequential output to source data, prompt or configuration where relevant, model version, retrieval index, and human action. The detail varies by use case, but the ability to reconstruct what happened is essential to investigation and learning.
Privacy and security review should be tied to actual data flows. Is personal information sent to a third-party service? Is it retained? Can it be used to train a general model? Where does it reside, and who can access logs? Generic assurances are less useful than a diagram and an enforceable agreement describing those flows.
Decide what a human must do
Human oversight is not a safety control merely because a person can see the output. The person needs time, expertise, context, and authority to challenge it. If reviewers face hundreds of fluent drafts under a tight deadline, they may miss an unsupported claim. If clinicians receive an alert with no source context, they may ignore it or accept it uncritically.
Test the review process itself. Measure errors detected, errors missed, time spent, disagreement between reviewers, and whether the tool changes the user’s decision. The evaluation should include cases where the AI is wrong but persuasive. It should ask whether the user can access the underlying evidence easily enough to make an independent judgment.
For some uses, a human approval step may be essential. For others, an automated integrity check and periodic sampling may be appropriate. The choice should be proportional to the consequence of error and the reversibility of the action. A blanket rule that every AI output requires sign-off can create ceremonial review; a blanket rule that experts remain accountable without giving them workable controls is equally weak.
The product should make uncertainty usable. It may abstain when inputs are outside scope, flag missing data, show source passages, or route a case to a specialist. A confident answer when the system has no relevant evidence is a design failure, not simply a user training issue.
Govern change over the lifecycle
Models, data feeds, clinical practice, and source documents change. Define which changes require a new evaluation, who approves them, and how affected users are told. A minor interface adjustment and a new model version that changes outputs should not pass through identical review, but neither should occur invisibly.
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For a medical device software function, applicable FDA guidance and quality system requirements shape the change process. FDA’s guidance on predetermined change control plans for AI-enabled device software functions offers a product-specific route for planned modifications under its scope. For AI used to support regulatory decision making in drug or biologic development, FDA’s draft framework discusses a risk-based credibility assessment tied to context of use. These documents address different domains; the team should not treat them as one universal approval route. [2, 3]
Maintain a change log with the reason, affected population and workflow, test evidence, approval, deployment date, and rollback plan. Predictions or outputs should remain linked to the version that generated them. Monitoring should cover data integrity, performance where outcomes are observable, user behavior, and incidents.
The team needs a pause mechanism. If a source feed breaks or a serious error pattern emerges, who can stop the function, how is work handled meanwhile, and how are affected users informed? A rollback plan written only for a technical outage does not answer a clinical or regulatory incident.
Make fairness specific
Fairness is often treated as a mandatory metric selected before the use case is known. Start by identifying who might be excluded or harmed by the tool’s data and workflow. In a trial planning system, historical site data may disadvantage newer sites or communities underrepresented in prior studies. In a clinical model, performance and access to follow-up action may differ across patient groups.
The analysis should include data coverage, error rates where estimation is credible, and the consequences of errors. Small subgroup samples need uncertainty and careful interpretation. A statistically similar score distribution does not prove equitable outcomes if some patients cannot access the intervention the score triggers.
There may be tradeoffs. Expanding a tool to a population with limited validation could improve access or expose people to poorly understood error. Governance should make the tradeoff explicit and define an evidence plan, rather than claim that one aggregate fairness number settles the matter.
Patient and clinician input can reveal harms a dataset cannot show: confusing explanations, burdensome follow-up, or a pathway that works only at well-resourced centers. These perspectives should influence product design before broad deployment.
Create a decision record not a ceremony
For each use case, maintain a compact decision record: intended use and exclusions; data and permissions; key harms and benefits; evidence and uncertainty; human controls; monitoring and change plan; owners; and the decision to pilot, deploy, restrict, or stop. The record should link to detailed technical documentation without forcing executives to read a hundred-page packet for every choice.
A cross-functional group should challenge the record in proportion to risk. Clinical, regulatory, quality, privacy, security, data, and product specialists may all be needed for a consequential use. A low-risk internal research aid can move through a lighter path. This is risk-based governance in practice: more scrutiny where a wrong output can do more harm.
Reviewers should identify the weakest assumption, not merely check that each box contains text. Is the evaluation population representative? Is the user actually able to override the recommendation? Can the vendor explain a change? Does the data agreement permit the proposed use? The unresolved assumptions should determine conditions for a pilot or deployment.
The decision record should be updated when the use changes. A tool introduced to summarize documents may later be used to recommend an action. That is a new context of use even if the underlying model is unchanged. Governance must follow the product’s actual behavior, not its original procurement label.
An illustrative product review
Imagine a biotechnology company proposes an assistant that drafts responses to medical information questions using approved source documents. The intended user is a trained medical information specialist. The output is a draft, never a direct message to a clinician or patient. The assistant retrieves versioned sources, shows citations, and logs edits before approval.
The evaluation would include common and difficult questions, outdated sources, ambiguous requests, and cases requiring escalation. The measures would include factual accuracy, source support, omissions, reviewer time, and errors that survive review. Access controls would prevent retrieval of materials outside a user’s role. A change to the model or approved document corpus would trigger defined checks.
Suppose the pilot saves drafting time but reviewers spend more time verifying citations and a few unsupported statements survive. The governance decision should not be “the model passed because it is accurate on average.” The team might narrow scope, improve retrieval, require abstention for certain questions, and run another evaluation. That is a product decision grounded in evidence.
If the organization later wants to send answers automatically, the intended use changes dramatically. The earlier pilot does not validate the new workflow. The oversight, evidence, and regulatory or legal assessment must be revisited. This is the practical value of documenting the boundary at the start.
What leaders should ask each quarter
The quarterly review should establish how many systems are operating, what decisions they influence, who owns them, and whether their current use matches the approved scope. Review incidents, data or model changes, performance signals, user feedback, and overdue mitigations. Identify deployments that were stopped or narrowed because evidence failed to support expansion.
The last question matters. If governance never changes a product decision, it may be a reporting exercise. A healthy program can approve useful tools, improve weak ones, and say no when risk or evidence warrants it. It can explain those decisions without hiding behind generic principles.
A governance framework earns its place when the company can show how it influenced a consequential use. That account should connect the evidence to the permitted action, the remaining uncertainty, and the person responsible for reassessment. It allows management to advance a useful application without claiming that the framework itself proves the application safe or effective.
A useful test of governance maturity is whether a team can explain a proposal it declined or narrowed. A record of a promising system that was narrowed because the evidence did not support a broader population demonstrates that the review has influence. It also gives the team a path to revisit the decision when better data become available. Governance should help useful products advance under the right conditions and keep unsupported claims from becoming routine practice.
Sources
[1] National Institute of Standards and Technology. AI Risk Management Framework. Source
[2] U.S. Food and Drug Administration. Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (draft guidance, 2025). Source
[3] U.S. Food and Drug Administration. Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions (final guidance, 2025). Source
[4] Microsoft. What is Microsoft Dragon Copilot (physicians)? Source
