AI in healthcare review says tools can assist, not replace clinicians: full analysis

Artificial intelligence is moving deeper into healthcare, but the latest message from a narrative review featured by Vet Candy is notably restrained: AI looks strongest as a clinical support tool, not as a replacement for the professional in the room. The review examined evidence across diagnostic imaging, laboratory medicine, rehabilitation technologies, and AI-powered conversational agents, and found a familiar pattern: strong results in narrow, controlled settings, paired with unresolved questions about real-world performance, bias, transparency, and accountability. (myvetcandy.com)

That framing lands at a moment when AI adoption is accelerating across medicine. FDA maintains a public list of AI-enabled medical devices authorized for the U.S. market and says the list is intended to improve transparency for clinicians and patients, while also noting that it is not a comprehensive catalog of every AI-enabled device. In late 2024, the agency also issued draft guidance for developers of AI-enabled devices covering the product lifecycle, including performance monitoring and information that should be conveyed to users. (fda.gov)

According to the Vet Candy summary, the review drew from PubMed/MEDLINE, Scopus, Web of Science, and Embase, with studies published primarily over the past decade. The strongest evidence base appears to be in imaging-heavy specialties, where AI can match trained clinicians on specific tasks such as identifying suspicious findings in scans or digital pathology images. But the review also stresses that many of those results come from retrospective studies or curated institutional datasets, raising the question of whether performance will hold up across different patient populations, equipment, workflows, and care settings. Similar caution applies in laboratory medicine, where AI may improve workflow efficiency and decision support, but evidence is still weighted toward controlled environments. (myvetcandy.com)

That caution is echoed in broader literature on real-world clinician use. A 2024 systematic review with narrative synthesis in the Journal of Medical Internet Research found that health professionals’ experiences with AI-based clinical decision support vary widely. Common themes included limited understanding of how tools generate outputs, mixed confidence in accuracy, questions about whether AI adds value beyond confirming existing judgment, and concerns about governance and implementation. In other words, technical performance alone doesn’t guarantee clinical trust or adoption. (pubmed.ncbi.nlm.nih.gov)

Stakeholder research in radiology points in the same direction. A scoping review in European Radiology found that most stakeholders expect AI to significantly affect practice, but not replace radiologists in the near term. The review also identified recurring concerns around trust, education, economics, and medicolegal responsibility, with radiologists emphasizing that humans should remain “in the loop” when errors occur. (pubmed.ncbi.nlm.nih.gov)

Why it matters: For veterinary professionals, this is less a distant human-health debate than a preview of questions already arriving in animal health. AI tools aimed at imaging, pathology, client communication, record summarization, and decision support may improve efficiency and consistency, especially in high-volume practices or specialty settings. But the same limitations apply: a model trained on narrow datasets may not translate cleanly across species, breeds, clinics, equipment, or case mix. Veterinary teams will likely need to evaluate not just whether a tool performs well in a demo, but whether it performs reliably in their own workflow, with their own patients, and with clinicians who understand its limits. That makes governance, validation, staff training, and documentation as important as the algorithm itself. This is an inference from the human-health evidence base and regulatory direction, but it is a reasonable one. (myvetcandy.com)

Global health authorities are reinforcing that point. WHO’s guidance on AI for health says these systems may improve diagnosis, treatment, and research, but should be designed and deployed with ethics, human rights, and accountability at the center. WHO has continued to build out its governance work, including naming a collaborating centre on AI for health governance in 2025, underscoring that the field is moving from experimentation toward more formal oversight. (who.int)

What to watch: The next meaningful shift won’t be AI claiming it can think like a clinician. It will be whether developers can show prospective, real-world benefit; whether regulators sharpen expectations around monitoring and transparency; and whether healthcare teams, including veterinary teams, build practical rules for when AI supports care and when human judgment must clearly override it. (myvetcandy.com)