AI’s role in care is growing, but clinicians remain central

CURRENT FULL VERSION: Artificial intelligence is moving from pilot projects into everyday healthcare workflows, but the latest message from the literature is a cautious one: AI is becoming more useful, not more autonomous. A March 9 Vet Candy report on a new narrative review framed the issue clearly, describing strong AI performance in imaging, lab medicine, rehabilitation, and conversational tools, while emphasizing that these systems still haven’t earned unchecked trust in clinical care. That same assistive-not-replacement theme is now showing up across veterinary research, practice software, and policy discussions. It is also echoed in adjacent medical fields: a recent systematic review and meta-analysis in BMC Oral Health found that AI-based methods for impacted canine assessment may look promising in small studies, but current evidence does not support them as clinically decisive and instead positions them as adjunctive, hypothesis-generating tools pending stronger validation.

The backdrop is familiar to anyone in practice. AI first gained traction in medicine through image-heavy tasks, where algorithms could be trained to detect narrow patterns in radiology, pathology, dermatology, and ophthalmology. That strength is now carrying into veterinary medicine, where recent reviews describe promise in areas such as respiratory disease detection, pathology, and oncology. But those same reviews also describe a persistent translation problem: models often look strong in retrospective or tightly controlled studies, then face tougher conditions in real clinics with different populations, equipment, record quality, and workflows. The BMC Oral Health review illustrates the same pattern well. Across 28 studies published from 2015 to 2024, diagnostic performance overall was moderate to substantial, and cone-beam CT outperformed two-dimensional panoramic radiography for diagnosis, but AI-related estimates came from only a small subset of studies and were limited by heterogeneity, small samples, scarce external validation, and inconsistent reporting.

That’s why the current conversation has shifted from whether AI can do impressive things to whether it can do them safely, consistently, and transparently in practice. The Vet Candy piece said the underlying review identified four recurring concerns: generalizability, algorithmic bias, ethical implementation, and regulatory oversight. Veterinary-focused literature is landing in a similar place. A recent systematic review of AI for respiratory disease diagnosis in dogs and cats concluded that AI is promising as a complementary tool, but that practical integration still depends on overcoming methodological, data, and validation barriers. A scoping review in veterinary oncology likewise found rapid growth in the field, but said clinical translation is being held back by data bottlenecks and validation gaps. The dental review reached a nearly identical conclusion: despite encouraging signals, current evidence does not justify treating AI outputs as decisive, and larger prospective studies with standardized reporting, calibration, and rigorous external validation are still needed.

At the same time, commercial adoption is moving faster than the evidence base. Digitail’s March 6 article argued that AI use in clinics now extends well beyond SOAP-note dictation into intake, follow-up, triage, record audit, risk assessment, compliance coaching, and operational support. On its product pages, the company says clinics can use AI across more than 20 workflows, while also stressing that outputs remain reviewable and that critical decisions stay with licensed professionals. That framing is notable because it mirrors the language emerging from researchers and regulators: AI may speed work and surface signals, but it still requires human oversight. (digitail.com)

Regulators are also signaling that oversight will tighten as adoption grows. FDA announced in 2025 that it had completed its first AI-assisted scientific review pilot and planned broader internal rollout, while also proposing a framework to assess the credibility of AI models used in drug and biologic submissions. The agency has separately called for public comment on how to measure real-world performance of AI-enabled medical devices after deployment. Those actions are centered on human healthcare, but they matter to veterinary medicine because they show where expectations are heading: better documentation, clearer validation, and ongoing monitoring rather than one-time claims of accuracy. The AVMA, for its part, supports responsible, ethical, science-based use of technology in veterinary medicine and emphasizes regulatory review grounded in risk assessment. The same themes surfaced in the BMC Oral Health review, which called not just for external validation but also for calibration and standardized reporting before AI-based approaches can be relied on clinically. (fda.gov)

Why it matters: For veterinary professionals, the practical takeaway is less about futuristic replacement and more about workflow design, risk management, and trust. AI can likely deliver value first in bounded uses: summarizing records, supporting imaging review, identifying missing documentation, helping standardize communication, or surfacing follow-up needs. But the literature doesn’t support handing off diagnosis, treatment planning, or client communication without clinician oversight. That matters not just for patient safety, but for the veterinarian’s role in interpreting uncertainty, recognizing context, and maintaining trust with pet parents. In other words, the near-term opportunity is augmentation, not substitution. The broader lesson from adjacent imaging-heavy fields is the same: even when AI appears to outperform older approaches in early studies, that does not automatically make it ready for frontline clinical use without stronger real-world evidence. (digitail.com)

There’s also a business and operational angle. If clinics adopt AI too narrowly, they may miss efficiency gains in intake, follow-up, and records. If they adopt it too broadly, without governance, they risk overreliance on tools that haven’t been adequately validated in veterinary populations. That tension is especially important as more vendors market AI-enabled features directly to practices. The strongest near-term adopters may be the clinics that treat AI like any other clinical support technology: useful when its strengths, limitations, documentation, and accountability are clearly defined.

What to watch: The next phase will likely center on external validation studies, clearer regulatory expectations, and more pressure on vendors to show how models perform in real-world veterinary settings, not just demos or internal datasets. Expect more attention, too, to calibration and reporting quality, as newer reviews make clear that apparent accuracy alone is not enough for clinical trust. (fda.gov)