AI’s role in care is growing, but clinicians still lead

Artificial intelligence is moving deeper into healthcare and veterinary medicine, but the clearest message from recent reviews is that it still works best as support, not substitution. A 2026 narrative review in Healthcare found AI can match clinician-level performance in selected, tightly defined tasks, especially in imaging, lab medicine, rehabilitation, and conversational tools, while warning that most evidence still comes from retrospective or otherwise controlled settings rather than day-to-day clinical practice. That caution is echoed outside veterinary medicine too: a 2025 systematic review and meta-analysis in BMC Oral Health found that while some AI-based diagnostic estimates looked promising in impacted canine cases, the evidence was too limited and inconsistently reported to support clinical decision-making, with the authors concluding AI should be treated as an adjunctive, hypothesis-generating tool rather than a decisive one. In companion animal medicine, a recent systematic review in Animals likewise described fast-growing use cases across diagnostics, behavior, monitoring, and welfare, but said adoption remains fragmented and not yet well integrated into routine care. On the practice side, vendors such as Digitail are framing AI less as a single-note transcription tool and more as workflow infrastructure for records, scheduling, communication, and error checking. (pmc.ncbi.nlm.nih.gov)

Why it matters: For veterinary professionals, the opportunity is real, but so is the governance gap. The American Association of Veterinary State Boards said in its March 2025 AI white paper that veterinarians remain responsible for the standard of care when using AI, even where the tools themselves are not subject to premarket approval for veterinary use. The paper calls for transparency, data privacy safeguards, and informed consent when appropriate, and warns against assuming that AI automatically improves care or that tools cleared for human use are suitable for animal patients. The broader evidence base points in the same direction: even in adjacent clinical fields, stronger conventional approaches such as CBCT imaging and validated spatial measures still outperform the promise of poorly validated AI, reinforcing the need for external validation, calibration, and cautious interpretation before tools are used in practice. That puts the practical focus on validation, documentation, oversight, and choosing tools that reduce workload without outsourcing clinical judgment. (aavsb.org)

What to watch: Expect the next phase to center on benchmarking, real-world validation, and clearer state-level expectations as veterinary regulators, academic centers, and software companies push AI from pilot projects into everyday workflows. Just as recent non-veterinary evidence has called for larger prospective studies, longer follow-up, standardized reporting, and rigorous external validation, veterinary AI will likely face growing pressure to show not just technical performance, but reliable clinical value in real-world settings. (aavsb.org)