Demystifying artificial intelligence for veterinary professionals: practical applications and future potential
Bibliographic record
Abstract
The field of veterinary medicine, like many others, is expected to undergo a significant transformation due to artificial intelligence (AI), although the full extent remains unclear. Artificial intelligence is already becoming prominent throughout daily life (eg, recommending movies, completing text messages, predicting traffic), yet many people do not realize they interact with it regularly. Despite its prevalence, opinions on AI in veterinary medicine range from skepticism to optimism to indifference. However, we are living through a key moment that calls for a balanced perspective, as the way we choose to address AI now will shape the future of the field. Future generations may view us as either overly optimistic, blinded by AI's allure, or overly pessimistic, failing to recognize its potential. By understanding how algorithms function and predictions are made, we can begin to demystify AI, seeing it not as an all-knowing entity but as a powerful tool that will assist veterinary professionals in providing high-level care and progressing in the field. Building awareness allows us to appreciate its strengths and limitations and recognize the ethical dilemmas that may arise. This review aims to provide an accessible overview of the status of AI in veterinary medicine. This review is not intended to be an exhaustive account of AI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".