Verifiability in AI-Assisted Medical Content Review: What Can Be Checked and What Still Requires Judgment

An AI review demo can look convincing long before its output is ready to support a real MLR workflow.

The model highlights a claim, produces an explanation, and assigns a risk label. But the evaluator still needs to ask: What makes this result correct? Can another reviewer reproduce the check? Which source supports it? What does an error cost? And where does a qualified reviewer need to make the decision?

These questions matter because medical content review is often described as if it were one task. It is not.

MLR is not one verifiable task; it is a workflow of checks with different evidence, error costs, and human judgment requirements.

For Digital and IT evaluators, verifiability provides a better way to separate a plausible model output from an enterprise review capability.

What the verifiability thesis means for MLR

At Sequoia's AI Ascent 2026, Andrej Karpathy summarized a useful distinction: "Traditional computers automate what you can specify in code. This latest round of LLMs can automate what you can verify."

His broader point includes an important qualification. Capability does not depend on verifiability alone. It also reflects training attention, data coverage, and economic incentives. Models can be exceptionally capable in one setting and unexpectedly weak in another.

That pattern applies to medical content review, but not in the simplest way.

Some review checks have explicit rules and observable answers. A required disclaimer is present or absent. A reference identifier matches a controlled source or it does not. A material uses the current approved claim version or an older one.

Other checks require evidence comparison. Does the cited passage support the exact population, endpoint, comparator, and degree of certainty expressed in the claim? The result can be evaluated, but only when the evidence boundary and reviewer standard are defined.

Still other questions depend on scientific interpretation, overall impression, market rules, intended audience, and risk tolerance. They may be structured and supported by evidence without becoming fully deterministic.

Verifiability is therefore not a label that applies to "MLR AI" as a whole. It is a property of a defined review task.

A three-level verifiability ladder

The most useful question is not "Can AI review this material?" It is "What kind of verifier can test this particular output?"

Level 1: Deterministic checks

These tasks offer the clearest pass/fail structure. They are good candidates for repeatable automated checks because the expected relationship can be specified in advance.

But even a deterministic check needs context. A disclaimer may be required for one channel but not another. A phrase may match an approved version while being used beside a visual that changes its meaning. The system must know when the rule applies, not only whether the string appears.

Performance can be measured through task-level precision, recall, location accuracy, exception handling, and routing accuracy. A global model score is less useful than evidence that the defined check works across the material types and markets in scope.

Level 2: Evidence-bounded comparisons

Many high-value review tasks sit in the middle.

A system can retrieve the source behind a claim, identify the relevant passage, compare entities and qualifiers, and explain a possible mismatch. That creates a reviewable question. It does not automatically settle whether the evidence is adequate.

Evaluation requires more than a correct risk label. Teams should ask whether the system found the right claim, source, page, object, population, endpoint, comparator, and limitation. They should also examine false positives, false negatives, abstentions, and disagreements between qualified reviewers.

The best output at this level is not a confident verdict. It is a compact evidence package that makes the next human decision easier to inspect.

Level 3: Contextual judgments

Some decisions cannot be reduced to one stable answer without losing what matters.

Whether a material creates a misleading overall impression may depend on language, visual hierarchy, surrounding claims, audience knowledge, market expectations, product status, and precedent. Two qualified reviewers may interpret the same evidence differently and still raise legitimate points.

AI can support these tasks by locating relevant content, assembling evidence, surfacing applicable rules, identifying similar decisions, and routing the issue. Evaluation can test whether that preparation is complete, relevant, and traceable.

The final judgment remains with the accountable reviewer. The system is evaluated on decision support, not on pretending that ambiguity has disappeared.

Define the verifier before evaluating the model

An enterprise evaluation should specify five elements for every review task.

1. Context of use

Define the material type, audience, market, product stage, review point, workflow stage, and intended system action. "Checks claims" is not specific enough.

2. Controlled evidence

Identify the sources that can support the check: approved claims, product information, source passages, company-specific SOPs, market rules, or annotated examples. Record their version, effective date, provenance, and access conditions.

3. Error model

Define what counts as a true positive, false positive, false negative, acceptable abstention, and routing error. The operational cost of missing a high-risk issue is different from the cost of sending an extra low-risk flag to a reviewer.

4. Human reference standard

State who creates the expected answer, how disagreements are adjudicated, and which decisions remain examples rather than policy. Historical review records can contain valuable context, but they may also contain outdated rules or inconsistent reasoning.

5. Change control

Specify what happens when the model, prompt, parser, rule, source library, threshold, or workflow changes. A previously measured result cannot be assumed to hold after a material component changes.

Together, these elements turn "the AI seems accurate" into a claim that a buyer can examine.

From a data flywheel to a governed learning loop

The source material describes a moat built from real data, workflows, and evaluation criteria. The stronger enterprise interpretation is a governed learning loop.

Each review event can connect:

1. the material and exact review object;
2. the applicable evidence and rule version;
3. the system output and explanation;
4. the reviewer's action and rationale;
5. the final disposition and any later correction.

An override should not flow directly into future behavior as if every human action were correct. It first needs classification. Did the model fail? Was the evidence missing? Was the SOP ambiguous? Did the reviewer apply a valid exception? Has the policy changed?

Those distinctions determine whether the organization should update a rule, add evidence, revise an evaluation example, train users, or leave the system unchanged.

The defensible capability is not simply having more historical data. It is being able to turn reviewed decisions into controlled, testable improvement.

Questions buyers should ask

When evaluating an AI-assisted medical content review system, ask:

1. Which individual review tasks are supported, and what is excluded?
2. What makes the answer verifiable for each task?
3. Can every important flag be traced to a page, object, source, and rule?
4. Are results reported by review point and material type rather than as one overall score?
5. How are reviewer disagreements and uncertain cases handled?
6. Does the system abstain when evidence is insufficient?
7. How are overrides classified before they influence rules or evaluation data?
8. What changes trigger retesting, approval, or rollback?

These questions expose the difference between a horizontal model with a domain prompt and a review system with defined evidence, evaluation, and accountability.

Where ZENO fits

ZENO is designed as an MLR pre-review layer for medical content materials before formal MLR approval.

It helps teams identify and locate potential review risks, connect findings to source evidence and company-specific review logic, explain why an issue was flagged, and route uncertain or material questions to the appropriate human reviewer.

ZENO's role is not to declare every MLR question objectively solved. It is to make more of the review process inspectable: define the task, show the evidence, preserve the decision boundary, and measure performance at the level where correctness can be meaningfully assessed.