A recent lawsuit involving an Oregon veterinary hospital and an alleged misdiagnosis by an AI diagnostic tool raises a question practices haven't answered.
The repeated thing across AI adoption
Brakke Consulting flagged the Oregon case and the question it raises: when AI contributes to a recommendation, who is responsible for validating it?
The pattern Brakke sees: AI is becoming ubiquitous in animal health, diagnostics, market analysis, due diligence, fundraising. Experts rarely accept the first AI-generated answer. They challenge assumptions, test conclusions, iterate. Without that expertise, Brakke notes, a polished output can too easily be mistaken for a reliable answer.
The professional remains responsible for the recommendation, whether interpreting diagnostics or advising on strategy. That principle does not change because the tool is new.
What the pattern predicts
Owner-DVMs buying AI tools to increase throughput and reduce associate burnout need to ask whether they have updated protocols and coverage to match the new risk. Practice managers fielding vendor pitches now have a concrete question to ask before signing: what does the contract say about liability indemnification, and what protocol requirements does the vendor expect the practice to follow?
Brakke is applying the same principle to a program helping animal health startups and growth companies prepare for fundraising. AI can organize diligence materials, build checklists, identify documentation gaps, and manage much of the preparation process. That can significantly reduce the number of expensive consulting hours required. But it does not eliminate the need for expertise. It allows experienced advisors to spend their time where judgment creates the most value: positioning, priorities, investor expectations, and identifying the questions management may not know to ask.
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What would prove us wrong
The principle Brakke states holds until the Oregon case or others like it establish clearer precedent: AI may reduce the cost of expertise, but it should not reduce the standards of expertise.
The thing to watch next
How courts allocate liability between the veterinarian, the practice, and the AI vendor will shape how practices evaluate and deploy these tools. The Oregon case is a forcing function for a conversation: who validates the output, and what happens when the algorithm is wrong?
The answer applies whether you are interpreting a cytology slide or advising on a fundraising strategy. The professional remains responsible for the recommendation. The tool does not take your liability with it.