AI gains ground in care, but clinicians remain central

CURRENT FULL VERSION: Artificial intelligence is gaining ground across healthcare, but the latest literature lands on a measured conclusion: it can strengthen care delivery, yet it isn’t ready to replace clinicians. A recent narrative review of AI across diagnostic imaging, laboratory diagnostics, rehabilitation, and conversational agents found strong performance in narrow, controlled applications, while stressing that most systems still need better prospective validation, bias mitigation, transparency, and regulatory oversight before routine clinical deployment. That same caution shows up in newer specialty evidence. A 2025 systematic review and meta-analysis in BMC Oral Health examined diagnostic, predictive, and therapeutic approaches for impacted canines and found that although a small subset of studies reported higher AI-related diagnostic estimates, the evidence was limited by small samples, heterogeneity, scarce external validation, and inconsistent reporting. The authors concluded that current evidence does not support AI-based methods as clinically decisive and that, at most, they should be treated as adjunctive, hypothesis-generating tools rather than replacements for established imaging and clinical assessment. (pmc.ncbi.nlm.nih.gov)

That broader context matters because veterinary medicine is entering a phase that human healthcare has already been navigating: enthusiasm is outpacing standardization. A 2025 systematic review in Animals concluded that AI has an expanding role in companion animal care, but that use beyond clinical diagnostics, including behavior and personality assessment, remains fragmented and not yet well integrated into routine practice. Meanwhile, commercial vendors are promoting a much wider set of use cases, from automated SOAP notes and intake to follow-up communication, inventory support, and workflow automation. The gap between research maturity and commercial availability is becoming one of the defining features of the veterinary AI market. (mdpi.com)

The regulatory conversation is also becoming more concrete. In January 2025, the FDA issued draft guidance for developers of AI-enabled medical devices, recommending a lifecycle approach that includes early planning, testing, postmarket performance monitoring, and explicit attention to transparency and bias. While that announcement focused on medical devices, it signals the direction of travel for AI oversight more broadly. In veterinary medicine specifically, a 2025 American Journal of Veterinary Research paper from FDA Center for Veterinary Medicine authors described CVM efforts to integrate AI and machine learning into regulatory science, including postmarketing safety surveillance, antimicrobial resistance research, and information modernization, while emphasizing data quality, validation, and human-led governance. (fda.gov)

Professional regulators are sending a similar message. In March 2025, the American Association of Veterinary State Boards released its white paper on AI in veterinary medicine, stating that licensees must understand AI’s risks and limitations, maintain transparency around its use, protect client data privacy, and obtain informed consent when appropriate. The paper also warned against erosion of the standard of patient care and unlicensed practice. That’s a notable signal for clinics and vendors alike: even if adoption accelerates, accountability is expected to remain with the licensed veterinary team. (aavsb.org)

Early workforce data suggest the profession is interested, but not fully prepared. A 2026 JAVMA survey of 673 veterinary workers found that 90.5% reported no or minimal formal AI training, 25% said AI was already being used at their workplace, and most respondents believed AI will alter veterinary medicine. Importantly, 85.2% did not believe AI would completely replace radiologists. A separate 2026 survey of ACVIM and ECVIM-CA members reported that 39% of respondents were already using AI tools in clinical practice, even though many of those users still described their own knowledge as moderate, low, or nonexistent. Taken together, those findings support a consistent theme: adoption is happening, but education and governance are lagging. (pubmed.ncbi.nlm.nih.gov)

For veterinary professionals, that’s where the practical implications come into focus. AI may help reduce documentation burden, improve triage consistency, support imaging interpretation, surface relevant record history, and streamline communication with pet parents. But those gains depend on whether tools are validated in real clinical settings, integrated into workflows without adding friction, and used by teams that understand their limits. The current evidence base does not support handing off diagnosis, prognosis, or client communication to AI without meaningful clinician oversight. That point is reinforced by the broader healthcare literature: in the impacted-canine review, established imaging and reproducible spatial indicators remained the practical framework for care, with AI not yet supported as a decisive clinical tool. In practice, the safest framing is augmentation: AI can help teams work faster and more consistently, but it doesn’t remove the need for examination, context, ethics, or accountability. (pmc.ncbi.nlm.nih.gov)

That distinction also matters commercially. Many veterinary AI products are being marketed first on efficiency, especially around records and administrative work, because those use cases are easier to deploy than high-stakes clinical decision-making. That may be a rational entry point for practices facing staffing shortages, burnout, and documentation overload. Still, the profession will need to separate genuine utility from vendor promise. Claims about time savings, accuracy, or improved outcomes will increasingly need independent validation, not just case studies or product marketing. The same lesson appears in adjacent medical literature: promising AI signals in imaging-heavy domains can look stronger than the underlying evidence really is when studies are small, short-term, and inconsistently reported. (digitail.com)

Why it matters: The near-term question for veterinary medicine isn’t whether AI is coming. It’s whether clinics, regulators, educators, and vendors can put the right safeguards around it before it becomes embedded in daily care. For practices, that means asking harder questions about training, privacy, bias, auditability, informed consent, and who is accountable when AI gets something wrong. For the profession, it likely means AI literacy will become less of a niche skill and more of a baseline expectation. And as evidence from other clinical fields suggests, external validation, calibration, and prospective follow-up matter just as much as headline performance claims. (aavsb.org)

What to watch: Watch for more prospective studies in veterinary settings, more state-board and association guidance, and growing pressure on vendors to prove that AI tools improve care quality or reduce workload without compromising clinical standards or pet parent trust. Also watch for stronger study design expectations, including standardized reporting, external validation, and longer follow-up before AI tools are treated as reliable decision aids in practice. (aavsb.org)