Digital Veterinary Care in Companion Animal Practices: Integrating Telemedicine, Artificial Intelligence, and Remote Patient Monitoring

Digital technologies are reshaping companion-animal healthcare by expanding access to veterinary services and enabling more connected, data-driven models of care. This narrative review synthesizes current evidence on digital veterinary care, with a focus on the integration of telemedicine, artificial intelligence (AI), and remote patient monitoring (RPM) in companion-animal practice. A targeted narrative literature search of PubMed, Scopus, CAB Abstracts, Google Scholar, and relevant professional guidelines published between 2010 and 2026 was conducted to identify evidence on digital veterinary healthcare; the synthesis was narrative, and no formal risk-of-bias or study-quality assessment was undertaken. Current evidence suggests that telemedicine has shown the greatest value for teletriage, follow-up consultations, chronic disease management, specialist referral, and postoperative care, while RPM extends longitudinal monitoring beyond clinic visits and AI has been evaluated for applications in diagnostic imaging, predictive analytics, clinical decision support, and workflow efficiency, largely in retrospective, pilot-scale, or single-center veterinary studies. However, widespread implementation remains constrained by limited veterinary-specific validation of AI algorithms and commercial biosensors, evolving veterinarian–client–patient relationship (VCPR) regulations, cybersecurity and data privacy concerns, interoperability challenges, and the need for robust clinical evidence. Emerging concepts—including AI-integrated RPM, digital phenotyping, digital twins, federated learning, and One Health interoperability—remain largely prospective or extrapolated from human healthcare but have the potential to advance precision companion-animal medicine, strengthen disease surveillance, and improve preventive healthcare. Overall, digital veterinary care should complement rather than replace conventional veterinary practice. Future progress will depend on rigorous clinical validation, interoperable digital infrastructure, transparent AI governance, harmonized regulatory frameworks, and evidence-based implementation to improve animal welfare, veterinary service delivery, and One Health outcomes.