AI changes diagnosis and management in veterinary medicine

Artificial intelligence (AI) tools are already impacting veterinary practice, especially in imaging diagnosis and cytology for small animals. Computer vision models and natural language-based systems are being integrated into clinical workflows, bringing gains in efficiency and accuracy, as well as new ethical and educational demands for professionals in the field.

The use of computer vision, a branch of AI, is growing in veterinary cytology, enabling automated analysis of slides and blood tests. According to Chu and colleagues, the application of these systems already allows the identification of relevant cytological patterns in small animals, with research demonstrating advances in scientific validation and industrial adoption of these tools. The article highlights that, although technology is advancing, there are still gaps in transparency and standardization, requiring rigorous validation and ongoing professional oversight. The review also warns of the risk of skill loss if professionals become overly dependent on technology without maintaining mastery of traditional methods.

In veterinary radiology, AI already acts as a clinical decision support tool, but does not replace the veterinarian's judgment. Basran and colleagues emphasize that diagnostic responsibility remains entirely with the professional, regardless of the use of automated systems. Transparency is considered essential: "If you cannot explain to the client, in understandable terms, how the AI system works and its limitations, it should not be used in practice," the authors state. For safe implementation, recommended practices include team training, maintenance of traditional skills, and quality assurance protocols.

In addition to image analysis, systems based on natural language models are being used to optimize client communication, medical record keeping, and clinical decision support. According to Bollig and collaborators, incorporating these tools can increase efficiency and clinical accuracy, but requires attention to inherent risks such as the generation of inaccurate or biased information. The article recommends that professionals carefully evaluate the integration of these systems into workflows, maintaining human oversight and critical review of generated results.

Predictive models based on AI and machine learning are being used to estimate disease risks in small animals, drawing on data from medical records, insurance, wearable devices, and environmental information. Ruple and Reid highlight that these tools enable advances in precision veterinary medicine, but should be seen as complementary to clinical expertise. The article emphasizes practical limitations, potential biases, and ethical issues, reinforcing that decision-making should remain centered on professional judgment.

Despite advances, the literature points to important challenges for the safe and effective adoption of AI in veterinary practice. Chu and colleagues highlight the need for standardization and robust system validation, while Basran and collaborators warn of the importance of transparency and maintaining traditional competencies. Bollig points out risks inherent to language automation, such as possible biases and interpretation errors. Ruple and Reid stress that predictive models should not be used in isolation, to avoid inappropriate decisions. All authors agree that AI should be seen as a support tool, never as a substitute for clinical reasoning and professional responsibility.

The integration of AI into veterinary routines requires constant professional development, both in technical skills and digital ethics. According to Pinard, understanding the fundamental concepts of AI and machine learning is essential for evaluating, adopting, and contributing to the development of these tools. Successful implementation depends on team training, critical review of results, and transparent communication with animal owners. Responsible use of AI can bring gains in efficiency, diagnostic accuracy, and personalized care, provided it is accompanied by human oversight and quality protocols.

Artificial intelligence is already a reality in veterinary practice, with growing impact in areas such as cytology, radiology, and clinical management. The advancement of these technologies requires professionals to maintain continuous training, critical thinking, and attention to ethical implications, so that AI becomes a safe and effective ally in animal care.