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Veterinary AI software should be evaluated for a defined task. Evidence that a tool drafts notes or performs well on one imaging dataset does not establish that it can diagnose every patient, operate independently or deliver the same result in another clinic.
This guide helps clinical and operational teams read a claim critically and turn it into questions for a vendor. It is not a clinical validation of Vetigen or any other product.
Identify exactly what the system does#
Separate transcription, summarization, image classification and clinical decision support. Ask what input is required, what output is produced and which decisions remain with a veterinarian. Write down the intended species, setting and users.
A useful starting document is the provider’s current product specification. If a capability is only planned, exclude it from the evaluation of today’s workflow.
Read a study beyond its headline#
Record the model and version, study design, population, reference standard and outcome. Check whether the evaluation used data independent of development and whether the patients resemble those in your practice. Ask about errors as well as successful examples.
Evidence question | Why it changes interpretation |
|---|---|
Which task was tested? | Success on one task does not establish another capability |
Which species and setting? | The evaluation may not represent your case mix |
What was the reference standard? | Results depend on what counted as correct |
Which metric was reported? | Sensitivity, specificity and accuracy answer different questions |
Which version was evaluated? | A changed system may need a new assessment |
For example, Pomerantz and colleagues’ 2023 thoracic-radiography study evaluated a particular AI application and task. Its results should be interpreted within that study’s scope; they are not Vetigen performance results.
Keep generated text under clinical review#
Chu’s 2024 practical review discusses potential veterinary uses of generative AI alongside hallucination and ethical limitations. It provides context for evaluating a workflow, not a guarantee that a product’s output is safe or accurate.
Before saving a draft, the responsible clinician should compare it with the source information. Check whether findings were omitted, uncertain statements became definitive, units changed or information was invented. Keep patient observations separate from the system’s suggestions.
Run a controlled local evaluation#
Use authorized, appropriately prepared examples. Define the reviewer and the acceptance criteria before testing. Record both accepted outputs and corrections so unsuccessful cases do not disappear from the assessment.
Include the work of review, correction, training and handling failures when evaluating the operational benefit. Do not translate a short demonstration into a guaranteed consultation-time reduction.
Document the boundaries at launch#
Create a brief use policy: approved tasks, prohibited uses, review responsibility, data-handling requirements and the route for reporting errors. Reassess it when the tool or workflow changes. The clinic should know how to continue without the AI output when needed.
For related evaluation questions, see multilingual AI testing and cloud security. Bring your intended task and evidence questions to a Vetigen discussion.
