AI’s role in care is growing, but clinicians remain central
CURRENT BRIEF VERSION: Artificial intelligence is gaining ground across healthcare and veterinary settings, but the clearest message from recent reviews is that it works best as decision support, not as a stand-in for clinicians. A Vet Candy summary published March 9 highlighted a narrative review spanning diagnostic imaging, laboratory medicine, rehabilitation, and conversational agents, concluding that AI performs well in controlled settings but still faces major limits around generalizability, bias, ethics, and oversight. In parallel, a recent systematic review in Animals found AI use in companion animal care is expanding beyond diagnostics into behavior, welfare, and monitoring, while Digitail’s March 2026 practice-management piece pointed to growing commercial use in workflows such as documentation, intake, follow-up, and triage. That caution is also showing up outside veterinary medicine: a new systematic review and meta-analysis in BMC Oral Health found that although some small studies reported stronger AI-related diagnostic estimates, the evidence was too limited and inconsistently reported to support clinical decision-making, reinforcing the view that AI is best treated as an adjunct rather than a decisive tool. Across sources, the common thread is that adoption is broadening, but human review remains central. (myvetcandy.com)
Why it matters: For veterinary professionals, that distinction matters. AI may help reduce documentation burden, flag abnormalities, support imaging review, and improve continuity of care, but the evidence base still shows a gap between strong research performance and reliable real-world deployment. Reviews in both human and veterinary medicine point to the same pressure points: limited external validation, dataset bias, uneven transparency, and the need for governance before these tools are trusted at scale. The BMC Oral Health review underscored the same issue from another angle, finding that even when AI appears promising in imaging-heavy applications, small samples, heterogeneity, and weak external validation can keep it from being clinically decisive. For clinics, that means AI can be useful now in narrow, supervised tasks, but not as a replacement for clinical judgment, communication with pet parents, or accountability for care decisions. (myvetcandy.com)
What to watch: Expect more scrutiny around validation, calibration, labeling, and real-world monitoring as regulators and veterinary organizations push for safer, more transparent AI deployment. (fda.gov)