From Reactive to Predictive Care: A Conversation on AI and Animal Health

Four experts reveal how AI is transforming disease surveillance and veterinary practice

In a remote region of California, a wildlife rehabilitation centre uploads routine clinical notes about an injured eagle showing signs of neurological symptoms. Within hours, an AI system flags this case alongside similar reports from across the State, detecting an unusual pattern that might signal an emerging disease outbreak—weeks before traditional surveillance methods would notice. This scenario, now a reality through platforms like WildAlert, an online surveillance platform, exemplifies how artificial intelligence is revolutionising animal health from a reactive to proactive discipline.

The transformation extends far beyond individual cases. From cattle farms in rural communities to national Veterinary Services grappling with climate-driven disease emergence, AI technologies are reshaping how we detect, prevent and respond to animal health threats. Yet this technological revolution brings both unprecedented opportunities and significant challenges that demand careful navigation.

The Current AI Landscape in Animal Health

The application of AI in animal health spans a remarkably diverse range of technologies and use cases. At the forefront of disease surveillance, researchers have developed sophisticated systems that convert the chaos of real-world veterinary data into actionable intelligence.

‘We’re leveraging artificial intelligence to strengthen wildlife disease surveillance by improving our ability to detect early signs of health threats in wildlife populations’, explains Terra Kelly, a wildlife epidemiologist involved in developing WildAlert. The system uses natural language processing to extract clinical information from free-text medical records, converting informal veterinary notes into standardised data that can be analysed across time, geography and species.

This approach addresses a fundamental challenge in veterinary medicine: much of the valuable information about animal health exists in unstructured formats. Traditional surveillance relies heavily on definitive diagnoses, but AI facilitates what researchers call ‘pre-diagnostic’ surveillance—identifying patterns before diseases are formally confirmed.

Meanwhile, Pranav Pandit, an Assistant Professor of veterinary epidemiology at the School of Veterinary Medicine, University of California, Davis, explains how researchers are pushing the boundaries of AI applications in epidemiological and clinical settings. At UC Davis, machine learning algorithms are used to model disease risk using climatic predictors, helping forecast everything from bovine respiratory disease in cattle to the future distribution of zoonotic disease reservoirs, he says. Computer vision models are being trained to detect early signs of disease through radiographic imaging, though he points out that these approaches remain under validation.

The technology’s reach extends beyond direct clinical applications. Foresight platform developers are using AI agents to scan and synthesise global signals of change. Jens Hansen, Strategy and Communication Director of global foresight intelligence platform Shaping Tomorrow explains how maintaining databases of over 150,000 recent statements about the future help Veterinary Services anticipate zoonotic risks and prepare for climate-related disruptions.

Even in operational settings, AI tools are becoming indispensable. At the Wildlife Conservation Society, Executive Director of Health Chris Waltzer explains how teams routinely use platforms like ChatGPT for strategic writing and data extraction, while research teams rely on AI-powered literature review tools to rapidly identify knowledge gaps and refine research proposals.

Overcoming Implementation Challenges

Despite these advances, the path to AI adoption in animal health is fraught with technical and institutional obstacles. The most significant challenge lies in data quality and availability—a problem that affects everything from model training to real-world implementation.

‘Wildlife rehabilitation medical records often include unstructured, non-standardised data’, notes Kelly. ‘We conducted extensive training of our natural language processing model to recognise unique domain-specific terms, abbreviations, and the diverse ways clinical presentations are recorded’.

This data challenge is particularly acute in resource-limited settings. ‘A key challenge lies in the availability of high-quality, harmonised datasets, particularly from underfunded regions where capacity is limited’, observes Walzer. The disparity in data availability risks creating an AI divide that could exacerbate existing inequalities in animal health services.

Beyond technical challenges, AI implementation faces institutional resistance rooted in the fundamental differences between machine learning and traditional epidemiological approaches. ‘Traditional epidemiological frameworks value model interpretability, which is often limited in machine learning approaches’, explains Pandit. ‘This may hinder adoption and funding’.

The challenge extends to building the interdisciplinary teams necessary for successful AI development. Effective AI tools require collaboration between veterinarians, wildlife experts, epidemiologists, statisticians and AI specialists—a combination that can be difficult to assemble and coordinate.

Perhaps most critically, the speed of technological development often outstrips institutional capacity to adapt. ‘The pace of technological adoption surpasses the ability of regulatory and institutional frameworks to keep up, creating gaps in oversight and implementation’, warns Walzer.

Measuring Success and Impact

Despite these challenges, AI applications in animal health are delivering measurable improvements across multiple domains. The most immediately apparent benefits relate to efficiency and speed of analysis.  

AI-supported surveillance systems have ‘significantly reduced the time needed for horizon scanning, scenario building and policy briefing preparation’, says Hansen. For veterinary professionals, this translates into earlier identification of cross-sectoral risks and better-informed decision-making on disease prevention and resource allocation, he adds.  

The technology’s ability to leverage existing data sources represents another significant advancement. Rather than requiring entirely new data collection systems, AI tools can extract value from information that already exists but was previously underutilised. ‘The AI technologies have enabled us to leverage existing clinical wildlife data to complement active disease surveillance efforts,’ notes Kelly. ‘This allows us to conduct wildlife disease surveillance without solely relying on active field sampling, making surveillance more comprehensive and resource efficient.’  

In research settings, AI has accelerated fundamental processes. Walzer points out that policy reviews and grant development cycles that once took weeks can now be completed in days, helping organisations respond more quickly to emerging issues, while faster literature reviews allow researchers to identify knowledge gaps and refine proposals more rapidly.  

Perhaps most importantly, AI tools are enabling earlier detection of potential disease outbreaks. Machine learning-based anomaly detection systems can flag unusual patterns—such as increases in neurological cases within particular species or regions—even before definitive diagnoses are made. This capability shifts animal health from reactive to proactive approaches, potentially preventing small problems from becoming major crises.  

Broader Implications for Society

The societal implications of AI in animal health extend far beyond veterinary practice. At its best, AI can strengthen the interconnected systems that protect both animal and human health, supporting the One Health approach that recognises the links between wildlife, livestock and human disease.

‘AI can enhance disease surveillance, animal welfare, and early warning systems, strengthening food security and animal health in general’, observes Pandit. The technology supports a shift toward more sustainable and preventive veterinary practices, potentially reducing reliance on treatments like antimicrobials that can contribute to resistance.

Operating within what Walzer describes as ‘the complex reality of global polycrisis’, AI tools help integrate knowledge across sectors and disciplines. This capability is particularly valuable for addressing challenges like climate change, which affects animal health through complex, interconnected pathways that are difficult for humans to track without technological assistance.

However, the risks are equally significant. ‘Risks include opaque algorithms, digital inequality and potential misuse of data’, warns Hansen. The concern is not merely technical but fundamentally about power and equity. As Walzer puts it: ‘Who controls and defines the goals of AI matters profoundly’.

The risk of entrenching existing biases looms particularly large. If AI systems are trained primarily on data from well-resourced settings, they may perform poorly in the very contexts where improved animal health services are most needed. ‘Overreliance on AI, especially when models are trained on biased datasets, can lead to flawed predictions and misguided decisions’, cautions Pandit.  

Shaping the Future of Veterinary Services

Looking ahead, experts envision AI fundamentally transforming how Veterinary Services operate, promising to enhance both the efficiency and effectiveness of animal health systems.  

‘AI will transform Veterinary Services into more anticipatory and data-driven institutions’, predicts Hansen. ‘Real-time analytics and predictive models can enable earlier interventions and smarter resource allocation’.

The vision extends beyond simple automation to encompass more sophisticated integration of AI with human expertise. ‘The greatest value will come not from automation alone, but from how AI augments human judgment and helps embed long-term foresight into animal health systems’, Hansen adds.

For national Veterinary Services, particularly those dealing with wildlife health surveillance, AI represents an opportunity to do more with limited resources. ‘AI has the potential to become an important tool for modern Veterinary Services, especially in national wildlife health programmes where surveillance is often under-resourced’, says Kelly. ‘It can automate tedious data processing, prioritise alerts, and support decision-making with real-time situational awareness’.  

The technology also promises to enable more integrated approaches to health surveillance. Future systems could link wildlife, livestock and environmental data to track emerging threats in real time, supporting the kind of cross-sectoral collaboration that One Health approaches require but that institutional boundaries often prevent.  

However, realising this potential requires careful attention to how AI development and implementation proceeds. ‘Unless AI is co-developed with veterinary medical professionals embedded in transdisciplinary teams, it risks reinforcing narrow, corporate profit-driven approaches disconnected from true societal needs and realities’, warns Walzer. 

Ethical Governance and Regulatory Approaches

As AI applications in animal health mature, questions of governance and regulation become increasingly critical. The experts interviewed demonstrate varied approaches to ensuring ethical and effective AI implementation, reflecting both the diversity of applications and the evolving nature of regulatory frameworks.

Some organisations emphasise transparency and explainability in their AI systems. ‘We adhere to principles of transparency, traceability and explainability’, notes Hansen. ‘Our clients are encouraged to subject AI outputs to human validation and scenario stress-testing’.

Others focus on rigorous validation and expert oversight. Academic researchers emphasise ‘rigorous cross-validation, expert review of model outputs, and real-world validation’, says Pandit, including field investigations of AI-generated alerts to ensure they correspond to actual health events.

In collaborative research settings, governance approaches often emphasise equity and inclusion. ‘We follow open science principles and prioritise FAIR data principles and ethical review’, explains Walzer. ‘Collaborative governance with Indigenous and local partners and CARE data stewardship is a core tenet’.  

The challenge lies in balancing innovation with responsibility. Walzer adds, ‘there is a need to temper the momentum surrounding AI with thoughtful investment in ethical governance, staff training and cross-sectoral coordination’.  

Advice for Future Adopters

For organisations considering AI adoption in animal health, the experts offer practical guidance based on their experiences. The overarching theme is to start small but start now, while maintaining focus on ethical considerations and human expertise.  

‘Start with small, targeted use cases where AI can clearly augment—not replace—human expertise’, advises Hansen. ‘Prioritise partnerships that combine data science, veterinary knowledge and strategic foresight’.  

The importance of collaboration emerges as a consistent theme. ‘It’s important to collaborate closely with domain experts to ensure the AI models are grounded in real-world context’, notes Kelly. This collaboration should extend beyond technical development to include those most affected by the problems AI seeks to address.  

‘Ensure those closest to the problem help shape the solution’, urges Walzer. ‘Don’t wait to be invited to the AI table—pull up a chair’.  

The advice also emphasises the iterative nature of AI development. ‘Be prepared for iterative development, especially when working with complex data’, counsels Kelly. This iterative approach requires patience and sustained investment, but it’s essential for developing tools that work reliably in real-world settings.  

Perhaps most importantly, experts stress the need for educational efforts that help users understand and trust AI tools. ‘Advancing this field requires interdisciplinary collaborations, standardised protocols, and educational efforts that help users intuitively understand and trust AI as part of their broader diagnostic and surveillance toolkit’, observes Pandit.

A Future Built on Collaboration

The revolution in AI-powered animal health is not a distant promise but a present reality, as evidenced by platforms already detecting disease patterns across continents and AI tools accelerating research and policy development. Yet the experts’ experiences reveal that the technology’s ultimate impact will depend not on the sophistication of algorithms alone, but on how thoughtfully we integrate these tools with human expertise and ethical governance.

The path forward requires what Walzer calls ‘clear ethical commitments’ combined with ’transdisciplinary collaboration.’ It demands that we address fundamental challenges of data inequality while building AI systems that augment rather than replace human judgment. Most critically, it requires ensuring that those closest to animal health challenges—from rural veterinarians to Indigenous communities—help shape the solutions, he says.

As climate change accelerates and global interconnectedness increases disease transmission risks, the need for predictive, integrated approaches to animal health becomes ever more urgent. AI offers unprecedented capabilities to meet these challenges, but only if we build these systems with the same care we hope they will provide to the animals under our stewardship.

The future of animal health lies not in choosing between human expertise and artificial intelligence, but in crafting partnerships between them that serve the broader goal of planetary health. In this future, the eagle with neurological signs becomes not just a patient to treat, but an early warning signal in a global system of health surveillance that protects wildlife, livestock and humans alike.

Main image copyright: Tanit Boonruen