Clinical AI evaluation & advisory
Evaluation of clinical usefulness and failure modes against a defined task, rather than a broad claim of accuracy.
What this involves
- Context of useThe user, the inputs, the decision, the operating environment and the consequences of error, and what the evaluation will and will not demonstrate.
- Clinical test casesRepresentative and difficult cases, including missing data, conflicting timelines and clinically consequential edge cases.
- Reference reviewFindings from qualified clinicians against which the tool is compared.
- Error analysisMissed findings, false positives, their severity, and the reviewer effort needed to verify results.
- Human oversightWhat review is needed, by whom, and where the tool should not be relied on.
Why it matters in oncology
A tool can perform well on polished examples and fail on missing data or on a clinically important edge case. A single accuracy figure says little unless you know what the errors were, how serious they were, what the reference standard was and how much verification work remained for the reviewer.
When to involve us
- Assessing a vendor’s performance claimsfor a clinical-review tool.
- Defining acceptance criteriafor a pilot or a new release.
- Investigating failure modesand the human review they require.
- Deciding whether a tool is readyfor a defined use.
How we approach it
- Define the context of use and the decision the tool supports.
- Build the test set and the reference reviews.
- Run the tool and adjudicate differences, classifying errors by clinical consequence.
- Report the limits as clearly as the results.
What you receive
- A context-of-use and evaluation specification
- A clinical test-case and reference-review framework
- A findings report with error analysis and limitations
- Recommendations for human oversight and further evidence
Working together
- StructureA scoped advisory assessment, or a collaborative evaluation with statistical, engineering and validation expertise as needed.
- What we need from youThe intended workflow, access to a testable tool, permitted evaluation data and the decision the assessment should inform. Synthetic or de-identified cases can be used.
- IndependenceAssignments are subject to an independence and conflict review that includes DrugDevX’s own product-development interests. An evaluation is not regulatory certification and does not guarantee safety, compliance or generalizability.
Questions
Can one accuracy percentage establish clinical usefulness?
Rarely. The seriousness of errors, the reference standard, the case mix and the verification effort all affect whether a result is useful in practice.
How is patient data handled in an evaluation?
Evaluation can use synthetic or de-identified cases. Any use of real data follows an agreed information-handling process defined before work begins.
Discuss this service.
Tell us about the program, the question and the timing. We will respond about fit, expertise and next steps.
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