In the soft hum of an exam room, Dr. Sarah welcomes Bella, a stoic Persian cat, and her owner, Mr. Jones. Bella walks gingerly across the tiled floor, her usual elegance replaced by a hint of stiffness. “She’s been slowing down on our walks,” Mr. Jones says. “Maybe it’s just old age.” Dr. Sarah kneels beside Bella, her practiced hands gently examining the feline. She notices subtle hesitations in Bella’s movements and a slight tension in her muscles, indications that might otherwise be overlooked. But today, Sarah doesn’t just rely on her trained eye. She pulls out her tablet and opens an AI-enabled diagnostic app, a tool she recently introduced to her practice. With a few taps, she records Bella’s movements. Within moments, the AI processes Bella’s video. Subtle abnormalities in her gait — imperceptible to the human eye — are flagged, pointing toward early signs of osteoarthritis. A detailed report appears on the screen, which Sarah reviews with Mr. Jones.
“This analysis shows Bella might be experiencing joint discomfort,” Dr. Sarah explains. “The good news is, we’ve caught it early, and we can start treatment to keep her comfortable and active.”
Mr. Jones exhales in relief, thankful for the clarity and insight the AI-powered tool has provided.
The Role of AI in Veterinary Diagnostics
While the above illustration might seem futuristic, artificial intelligence is no longer a concept for tomorrow — it’s reshaping everyday veterinary care.
I was on a team that created an AI-based osteoarthritis pain detector for cats. Designed for use by cat owners, the tool analyzed home videos of cat movements and flagged the possibility of osteoarthritis pain. Cat owners with high-likelihood results were encouraged to consult their veterinarians, strengthening diagnosis and initiating treatment. The intent wasn’t to replace veterinarians but to empower pet owners to act sooner. In this way, artificial intelligence is an ally, not a competitor.
AI’s impact extends beyond osteoarthritis detection. For instance, AI tools are used to analyze animal vocalizations, providing insights into stress or discomfort that might go unnoticed. By interpreting specific sound patterns, veterinarians can identify potential health issues earlier.
Where AI Is Making Rapid Strides
AI has introduced groundbreaking technologies such as these into veterinary diagnostics:
- AI-assisted video analysis: Tools assess an animal’s posture, gait or subtle behavioral changes, helping detect early pain symptoms. The tools are particularly valuable for animals like cats and rabbits, whose pain signals are often subtle and easily missed.
- Wearable monitors: Devices track physiological data like heart and respiratory rates, analyzing discomfort through AI algorithms. These wearables can also monitor sleep patterns, a critical indicator of an animal’s overall health.
- Facial recognition: AI systems identify pain-specific changes in an animal’s expression, such as tightening around the eyes or shifting ear positions. Advanced models are beginning to differentiate between pain-related and emotion-related facial changes.
- Behavioral analysis: Some AI tools monitor changes in an animal’s activity levels, social interactions or appetite, providing a comprehensive picture of well-being.
All technologies enhance diagnostic precision, enabling practitioners to make confident recommendations and improve patient care. For example, a device that monitors changes in a dog’s behavior and vital signs could identify chronic pain well before symptoms escalate visibly.
Why Invest in AI-Driven Tools Now?
For veterinarians relying on traditional observation, the question isn’t why use AI but why not? Here are four reasons.
1. ENHANCED OBJECTIVITY
AI removes human bias, ensuring consistent and repeatable assessments. Subtle indicators, often missed by the most experienced clinicians, are easily identified. This objectivity is crucial when diagnosing conditions in exotic or less familiar species.
2. EARLY DETECTION
Routine exams might overlook the early signs of pain. With its precise algorithms, AI can detect subtle changes, paving the way for timely intervention. For instance, the early detection of arthritis in senior pets can significantly improve their quality of life through preventive care.
3. TIME-SAVING EFFICIENCY
AI tools process large volumes of data within minutes, allowing clinicians to focus on treatment plans and client communication. This efficiency has the power to boost productivity in veterinary practices and enhance the client experience.
4. IMPROVED CLIENT TRUST
Incorporating AI into a veterinarian’s diagnostic arsenal fosters confidence among pet owners. Demonstrating advanced technology shows a commitment to staying at the forefront of veterinary medicine. Clients are more likely to trust and return to practices that invest in cutting-edge tools.
Imagine a dog presenting with unexplained restlessness. Monitoring an AI-enabled wearable device, the veterinarian documents the dog’s activity and vital signs over the next week. The AI reveals irregular heart rates during episodes of restlessness, prompting further diagnostics and treatment.
Veterinarians who avoid leveraging such tools risk missing early diagnostic opportunities.
The Challenges of AI Adoption
Despite the promise, integrating AI into veterinary practice isn’t without its challenges. They include:
- Cost: High-quality AI tools and devices often have a significant price tag, making them less accessible to smaller practices. However, as the technology advances, the costs are expected to decrease.
- Data availability: Developing effective AI systems requires large, high-quality datasets. For certain species or conditions, the datasets might be limited. Collaborations between universities, research institutions and private companies can help address this gap.
- Training: A veterinarian must invest time in learning to use AI tools effectively, which could initially slow their adoption. Providing accessible training programs and support will be critical to overcoming this barrier.
Addressing the challenges requires collaboration between AI developers, veterinarians and industry stakeholders to make the tools more accessible and practical. Transparency about how AI tools work can also help build trust among clinicians and clients.
The Road Ahead
AI technology continues to evolve, with several exciting advancements on the horizon. For example:
- Algorithms tailored to detect pain in species like reptiles, exotic birds or aquatic animals. For example, AI could monitor subtle changes in the swimming patterns of fish to detect stress or illness.
- The use of EEG (electroencephalogram) or EMG (electromyography) to measure neural and muscular responses to pain. These advancements could provide direct evidence of discomfort, bypassing subjective interpretation.
- AI systems that analyze veterinary notes and combine them with physiological and behavioral data for a comprehensive diagnostic view. These natural language processing systems could also streamline recordkeeping and improve communication within veterinary teams.
- AI-driven tools that enhance remote consultations by providing real-time analysis of videos or data submitted by pet owners, expanding access to high-quality care.
All these advancements promise to enhance diagnostic capabilities, improving outcomes for animals and their owners’ satisfaction.
AI is transforming veterinary care, bringing objectivity, efficiency and early detection to the forefront. While challenges remain, the benefits of integrating artificial intelligence into veterinary medicine far outweigh the hurdles. As technology advances, it holds the promise of delivering even more precise, species-specific and comprehensive diagnostic tools. By embracing innovations, veterinarians can provide better care, improve outcomes and strengthen client bonds.
The future is clear: AI is not just a tool but an essential companion in improving animal health and well-being.
BEHAVIOR-BASED PAIN SCALES
The Glasgow Composite Measure Pain Scale, one of the earliest behavior-based pain assessments, was developed in 2007 to evaluate acute postoperative pain in dogs. This groundbreaking scale assessed factors such as vocalizations, mobility, posture and responses to touch. Its success led to the development of similar scales for other animals, laying the foundation for modern pain detection methods. Today, these scales are being integrated into AI models, combining traditional wisdom with cutting-edge technology.