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

Artificial intelligence is gaining ground across healthcare, but the current evidence still points to a supporting role rather than a replacement for clinicians. A 2026 narrative review, Artificial Intelligence in Healthcare: From Diagnosis to Rehabilitation, found strong performance in selected use cases, particularly diagnostic imaging, digital pathology, laboratory medicine, rehabilitation technologies, and patient-facing conversational tools. At the same time, the authors stressed that these gains are most often documented in retrospective or controlled environments, not in the messier conditions of routine care, and concluded that AI should be treated as clinical decision support rather than a stand-in for healthcare professionals. (pmc.ncbi.nlm.nih.gov)

That caution also shows up in narrower clinical literature. A 2025 systematic review and meta-analysis in BMC Oral Health examined diagnostic, predictive, and therapeutic approaches for impacted canines and found that while some AI-related diagnostic estimates appeared higher in a small subset of studies, the evidence was too limited, heterogeneous, and poorly externally validated to support clinical inference. More established tools performed better: cone-beam CT was associated with higher diagnostic accuracy than two-dimensional panoramic radiography, and reproducible spatial indicators such as inter-tooth and inter-root contact distance were highlighted as more practical predictors. The review concluded that AI-based methods are not currently clinically decisive and, at best, should be viewed as adjunctive or hypothesis-generating until larger prospective studies with standardized reporting, calibration, and external validation are available.

That conclusion lines up with what’s happening in veterinary medicine, where enthusiasm is growing faster than the evidence base or the regulatory framework. A systematic review in Animals described AI’s expanding role in companion animal care, including diagnostics, health monitoring, behavior assessment, and welfare applications, but noted that adoption beyond narrow clinical tasks remains fragmented. An earlier systematic review of AI feasibility in veterinary medicine similarly found wide interest across diagnostics, pathology, microbiology, epidemiology, education, and animal welfare, underscoring how broad the field has become even as validation remains uneven. (mdpi.com)

The commercial market is already moving quickly. Digitail’s recent practice-management content argues that clinics should think beyond AI scribes and consider a wider set of workflow uses, including appointment scheduling, medical record support, communication, billing, inventory functions, and prescription or lab-related checks. That framing matters because it reflects where many practices may first feel AI’s impact: not in replacing veterinarians, but in reducing administrative friction and helping teams recover time. Still, vendor claims should be read as directional rather than definitive evidence, especially when independent outcomes data are limited. (digitail.com)

Regulators are starting to catch up. In March 2025, the American Association of Veterinary State Boards released a white paper saying licensees remain fully responsible for appropriate AI use under existing veterinary practice acts, even if the AI-enabled tool itself is not subject to veterinary premarket approval. The paper highlights risks around bias, fabricated or inaccurate outputs, recordkeeping, confidentiality, unlicensed practice concerns, and the absence of standardized benchmarking in veterinary AI. It also explicitly says veterinary professionals should not assume that FDA-approved human AI devices are appropriate for animal care. (aavsb.org)

Academic leaders are making similar points. Cornell has emerged as a visible hub for veterinary AI work, with its Symposium on Artificial Intelligence in Veterinary Medicine and a 2025 AJVR special issue focused on the field. Cornell researchers have also warned about the rapid rise of nonregulated AI systems in veterinary medicine and the need for trustworthy, safe deployment. Separately, a recent survey of ACVIM and ECVIM-CA members found specialists were generally optimistic that AI tools will improve veterinary medicine and become part of their careers, a sign that clinician sentiment may be shifting from skepticism to conditional adoption. (news.cornell.edu)

Why it matters: For veterinary professionals, the practical takeaway is that AI is becoming harder to ignore, but easier to misuse. The near-term value is likely to come from narrow, supervised applications: summarizing records, supporting communication, surfacing patterns in data, and assisting interpretation in areas such as imaging or pathology. But the standard of care still rests with the veterinarian, and the burden is on practices to understand where a tool has been validated, what data it was trained on, how errors are detected, and whether client data are protected. Evidence from adjacent clinical fields reinforces the point: promising AI signals do not necessarily translate into clinically actionable tools when studies are small, short-term, inconsistently reported, or lack external validation. In other words, AI may help teams do more with less, but it doesn’t reduce the need for professional judgment, informed consent decisions, or defensible documentation. (pmc.ncbi.nlm.nih.gov)

There’s also a business and workforce angle. Administrative AI could help address burnout by shifting time away from repetitive tasks and back toward patient care, a benefit the AAVSB white paper explicitly acknowledges. But if clinics adopt tools faster than they build policies around them, they risk inconsistent use, overreliance on opaque outputs, and new liability questions. That makes implementation strategy as important as software selection. (aavsb.org)

What to watch: The next stage will likely be shaped by three things: better real-world benchmarking, more formal guidance from regulators and professional bodies, and stronger evidence on where AI actually improves outcomes in veterinary settings. Watch for additional state-board guidance, more peer-reviewed validation studies, and continued efforts from institutions such as Cornell to build shared datasets and benchmarks that can move veterinary AI from promising demos to reliable clinical tools. Recent adjacent evidence is also a reminder that the bar is rising: larger prospective studies, longer follow-up, standardized reporting, and rigorous external validation are increasingly becoming the minimum expectations for AI claims that aim to influence care. (aavsb.org)