AI in care delivery is advancing, but clinicians remain central

Artificial intelligence is gaining ground across healthcare, but the most credible message from the latest literature is a restrained one: it’s powerful, but it isn’t ready to replace clinicians. A recent narrative review of AI in healthcare concluded that AI can match or approach professional performance in selected tasks, especially in imaging-heavy settings, yet most evidence still comes from retrospective or otherwise controlled environments rather than messy real-world care. The authors argue that AI should be treated as clinical decision support, with broader adoption dependent on prospective validation, bias mitigation, ethical safeguards, and stronger oversight. (pmc.ncbi.nlm.nih.gov)

That framing resonates in veterinary medicine, where AI adoption appears to be accelerating faster than the profession’s guardrails. A 2024 review in Research in Veterinary Science described AI as increasingly integrated into veterinary diagnostic imaging, but emphasized that these systems should support, not replace, human judgment. The paper highlighted both opportunity and risk: faster image review, workflow support, and pattern detection on one hand, and concerns about transparency, training data quality, ethics, and market readiness on the other. (sciencedirect.com)

The broader companion animal picture is also widening. A recent systematic review in Animals found AI’s role in companion animal care is no longer confined to diagnostics, with growing applications in behavior assessment, health monitoring, welfare, and human-animal interaction. But the review also suggested the field is still fragmented, meaning many tools remain siloed, unevenly validated, or not fully embedded in day-to-day clinical workflows. That gap between promising pilots and routine use is a familiar pattern in human healthcare AI as well. (mdpi.com)

That same caution shows up in adjacent clinical literature outside veterinary medicine. A recent systematic review and meta-analysis in BMC Oral Health examined diagnostic, predictive, and therapeutic approaches for impacted maxillary canines and found that while AI-related diagnostic estimates appeared higher in a small subset of studies, the evidence base was too limited and inconsistent for clinical inference. More established approaches performed better on firmer footing: CBCT showed higher diagnostic accuracy than two-dimensional panoramic radiography, and reproducible spatial indicators were useful in prediction, with imaging decisions still expected to follow radiation protection principles such as ALARA. The authors concluded that current evidence does not support AI-based methods as clinically decisive and that, for now, they are better viewed as adjunctive or hypothesis-generating tools pending larger prospective studies, external validation, calibration, and standardized reporting.

On the commercial side, veterinary software companies are already positioning AI as a workflow layer rather than a one-off feature. Digitail’s recent materials describe use cases extending from note generation to client communication, record review, and operational support, underscoring how quickly AI is being packaged into practice management systems. That doesn’t amount to independent validation, but it does show where the market is heading: embedded tools that touch documentation, triage, communication, and scheduling before they take on higher-stakes clinical roles. (digitail.com)

Early professional sentiment suggests veterinarians are interested, but not uniformly prepared. In a 2026 Journal of Veterinary Internal Medicine survey of ACVIM and ECVIM-CA members, respondents were broadly optimistic that AI would improve veterinary medicine and become part of their careers. At the same time, 39% said they were already using AI in clinical practice, even though more than half reported only slight or no knowledge of AI. A companion commentary in the same journal called for improved awareness and literacy, and noted that veterinary medicine still lacks the kind of formal regulatory oversight seen in parts of human healthcare. (academic.oup.com)

Why it matters: For veterinary professionals, this is less a story about replacement than about responsibility. AI may be most useful in the near term where workload is heavy and risk is manageable, such as imaging support, record summarization, draft client materials, and administrative workflows. But those gains only hold if practices understand what a tool was trained on, how it performs outside ideal datasets, how it handles confidential information, and where clinician review is mandatory. The broader evidence base also suggests a practical hierarchy: validated imaging methods and established clinical indicators should remain primary, while AI is layered in cautiously as decision support rather than treated as decisive on its own. AVMA policy supports the responsible and ethical use of technology and also backs standardized health information systems, both of which matter if AI outputs are going to be reliable, interoperable, and defensible in practice. (avma.org)

There’s also a practical workforce angle. AVMA has already pointed to technology, including AI, as one factor that could improve efficiency in companion animal practice, particularly as demand pressures continue. If that plays out, the biggest gains may come not from autonomous diagnosis, but from reducing documentation burden, improving information flow, and helping teams focus more time on patient care and communication with pet parents. Still, the profession’s own literature suggests that optimism without literacy is a risk factor in itself, especially when automation bias can make flawed outputs look more trustworthy than they are. That last point is an inference drawn from the survey findings and broader regulatory commentary, not a direct claim from a single veterinary study. (avma.org)

What to watch: The next milestones will likely be prospective validation studies in veterinary settings, more continuing education on AI literacy, clearer privacy and governance expectations, and stronger evidence on which use cases actually improve outcomes, efficiency, or both. Just as importantly, expect more scrutiny around external validation, calibration, and standardized reporting before AI tools are trusted in higher-stakes diagnostic or therapeutic decisions. (academic.oup.com)