Veterinary AI Standard of Care

Veterinary medicine is experiencing an AI revolution that parallels, and in some ways outpaces, human healthcare. Diagnostic AI systems analyze radiographs and pathology samples. Telemedicine platforms connect pet owners with remote veterinarians. Treatment recommendation engines suggest protocols based on patient data. These technologies promise to expand access to veterinary care, but they also create unprecedented liability questions.

The core challenge: veterinary malpractice law is evolving to address AI, but standards remain unsettled. When an AI misdiagnoses a pet’s condition, when telemedicine AI fails to refer a critical case, when treatment algorithms recommend inappropriate care, who is liable? The emerging answer holds veterinarians accountable for the AI tools they deploy, while recognizing that AI may itself become a liability target.

The Veterinary AI Landscape

#

Diagnostic AI Applications

#

AI is transforming veterinary diagnostics across modalities:

Market Growth and Adoption

#

Veterinary AI adoption is accelerating:

• Diagnostic AI market projected to reach $1.2 billion by 2028
• 65% of veterinary practices report using at least one AI tool (2024)
• Radiograph AI most widely adopted (40%+ of practices)
• Pathology AI fastest growing segment
• Telemedicine AI expanded dramatically post-pandemic

Telemedicine and Virtual Care

#

Veterinary telemedicine AI platforms offer:

• Symptom checkers guiding pet owners through triage
• AI-assisted consultations supporting remote veterinarians
• Prescription platforms enabling medication without in-person visits
• Monitoring systems tracking chronic conditions remotely
• Chatbot triage directing urgent cases appropriately

The Veterinarian-Client-Patient Relationship (VCPR)

#

VCPR Requirements

#

The Veterinarian-Client-Patient Relationship (VCPR) is the foundational requirement for veterinary practice:

Traditional VCPR Elements:

1. Veterinarian responsibility, Vet assumes responsibility for medical judgments
2. Sufficient knowledge, Vet has examined animal or made medically appropriate visits
3. Client agreement, Client agrees to follow veterinarian instructions
4. Availability, Veterinarian is available for follow-up
5. Medical records, Veterinarian maintains patient records

VCPR and Telemedicine AI

#

AI telemedicine creates VCPR challenges:

Can AI Establish VCPR?

• Most state laws require a licensed veterinarian to establish VCPR
• AI alone cannot satisfy the relationship requirement
• But AI may support human veterinarian in establishing VCPR

State Variations:

• Some states now allow telemedicine-established VCPR
• Others require in-person examination first
• AI role varies by state interpretation

Prescription Implications:

• VCPR typically required before prescribing
• AI-only consultations may not satisfy this requirement
• Prescription violations carry licensing consequences

Diagnostic AI: Standard of Care Implications

#

When AI Assistance Becomes Standard

#

The veterinary standard of care is evolving to incorporate AI:

Emerging Standard:

• In specialties where AI achieves better-than-human accuracy, AI assistance may become expected
• Failure to use available AI tools could constitute below-standard care
• But over-reliance on AI without clinical judgment also creates liability

Current State:

• AI tools remain aids, not replacements for clinical judgment
• Veterinarians must exercise independent professional judgment
• AI recommendations should be verified before acting

AI Diagnostic Errors

#

When AI diagnostic tools fail, liability questions multiply:

False Negatives:

• AI misses significant finding on radiograph
• Veterinarian relies on AI “normal” reading
• Condition progresses, animal harmed

False Positives:

• AI identifies pathology that doesn’t exist
• Unnecessary surgery or treatment performed
• Animal harmed by inappropriate intervention

Calibration Errors:

• AI trained on different patient populations
• Accuracy varies by breed, species, age
• Systematic errors affecting certain patients

Case Study: AI Radiology Miss

#

Hypothetical based on reported incidents:

A golden retriever presents with intermittent lameness. The veterinarian obtains radiographs and runs them through the practice’s AI diagnostic system. The AI reports “no significant abnormality detected.” The veterinarian, trusting the AI assessment, diagnoses muscle strain and recommends rest.

Six weeks later, the dog returns with severe lameness. Repeat radiographs, reviewed by a specialist without AI, reveal osteosarcoma that was visible on the original films but missed by the AI system.

Liability Analysis:

• Did the veterinarian breach the standard of care by relying on AI?
• Should the veterinarian have independently reviewed the images?
• Does the AI vendor bear any liability for the missed diagnosis?
• What disclosure was owed to the client about AI use?

Telemedicine AI Liability

#

Remote Care Challenges

#

AI-assisted telemedicine creates unique liability exposures:

Examination Limitations:

• Cannot physically examine patient
• Reliance on owner-reported symptoms
• Video/photo quality affecting assessment
• AI filling gaps in examination

Triage Failures:

• AI underestimating urgency
• Delayed referral to emergency care
• Reliance on symptom checkers for serious conditions

Communication Issues:

• AI chatbots providing inaccurate advice
• Misunderstanding owner concerns
• Language and comprehension barriers

The “Black Box” Triage Problem

#

AI triage systems may make recommendations that veterinarians cannot explain:

Problematic Scenarios:

• AI recommends routine care for condition requiring urgent attention
• AI elevates routine condition to emergency, causing unnecessary expense
• AI triage logic is proprietary and unexplainable
• Veterinarian cannot review AI reasoning

Legal Implications:

• Veterinarian remains responsible for triage decisions
• Cannot delegate professional judgment to unexplainable AI
• Must be able to justify recommendations to clients and boards

Informed Consent for AI-Assisted Care

#

Clients should understand when AI is involved in their pet’s care:

Disclosure Elements:

• That AI tools are being used
• What role AI plays in diagnosis/treatment
• Limitations of AI assessment
• Human veterinarian oversight

Consent Challenges:

• Clients may not understand AI limitations
• Assumption of human review when AI primary
• Hidden AI involvement in recommendations

Treatment Recommendation AI

#

AI Clinical Decision Support

#

Treatment recommendation AI assists veterinarians with:

• Drug dosing calculations based on patient factors
• Protocol selection for common conditions
• Drug interaction checking
• Treatment planning for complex cases
• Prognosis estimation based on similar cases

Liability for AI Recommendations

#

When treatment AI recommends inappropriate care:

Veterinarian Liability:

• Remains responsible for treatment decisions
• AI recommendations do not override professional judgment
• Must recognize when AI recommendations are inappropriate

Vendor Liability:

• Potential product liability for defective AI
• Negligent design or training of algorithm
• Failure to warn of AI limitations

Comparative Fault:

• Veterinarian and vendor may share liability
• Allocation depends on circumstances
• Jury may apportion fault

Species and Breed Considerations

#

Veterinary treatment AI must account for tremendous patient variation:

Species Differences:

• Drug doses vary dramatically between species
• Cats are not small dogs
• Exotic species require specialized knowledge

Breed Considerations:

• MDR1 mutation affecting drug metabolism in herding breeds
• Brachycephalic considerations for flat-faced breeds
• Giant breed vs. toy breed dosing

AI Training Gaps:

• Most veterinary AI trained primarily on dogs and cats
• Exotic animal AI often inadequate
• Specialty conditions may be poorly represented

Veterinary Malpractice Framework

#

Standard of Care in Veterinary Medicine

#

The veterinary standard of care is defined as:

“The degree of care, skill, and treatment which, in light of all relevant surrounding circumstances, is recognized as acceptable and appropriate by reasonably competent veterinary medical professionals.”

This standard is evolving to address AI:

Pre-AI Standard:

• Based on what reasonable veterinarians would do
• Measured against professional community standards
• Expert testimony typically required

Evolving AI-Era Standard:

• Reasonable use of available technology
• Balance between AI assistance and clinical judgment
• Understanding AI limitations

Elements of Veterinary Malpractice

#

To establish veterinary malpractice, plaintiffs must prove:

Damages in Veterinary Malpractice

#

Veterinary malpractice damages differ from human medical malpractice:

Economic Damages:

• Cost of additional treatment
• Cost of replacement animal (controversial)
• Lost income (working/breeding animals)
• Lost show/competition value

Non-Economic Damages:

• Many states limit to fair market value
• Growing recognition of emotional distress damages
• Some states allow loss of companionship

Trend Toward Expanded Damages:

• Courts increasingly recognizing pet-owner bond
• Emotional distress claims in egregious cases
• Legislative efforts to expand recoverable damages

Regulatory Framework

#

State Veterinary Licensing Boards

#

State veterinary boards regulate AI use through:

Practice Act Interpretation:

• What constitutes veterinary practice?
• Can AI perform functions requiring licensure?
• Supervision requirements for AI systems

Telemedicine Rules:

• VCPR establishment via telemedicine
• AI role in telemedicine consultations
• Geographic scope of practice

Disciplinary Authority:

• Inappropriate AI reliance as unprofessional conduct
• Failure to supervise AI-assisted staff
• Licensing violations involving AI prescription

AVMA Guidance

#

The American Veterinary Medical Association (AVMA) has issued guidance on AI:

Key Principles:

• AI should augment, not replace, veterinary judgment
• VCPR requirements remain paramount
• Veterinarians responsible for AI-assisted decisions
• Informed consent should include AI disclosure

Telemedicine Position:

• VCPR can potentially be established via telemedicine
• Varies by state law
• AI tools should support, not substitute for, veterinary consultation

FDA Regulation of Veterinary AI

#

The FDA regulates veterinary medical devices, including AI:

Device Classification:

• Most diagnostic AI would be Class II devices
• Requires 510(k) premarket notification
• Substantial equivalence to predicate device

Enforcement Reality:

• Limited FDA enforcement of veterinary AI
• Focus on higher-risk human medical devices
• Gap between regulation and market reality

Emerging Liability Theories

#

Product Liability for Veterinary AI

#

AI diagnostic and treatment tools may face product liability claims:

Manufacturing Defect:

• AI performs differently than designed
• Implementation errors causing failures
• Individual system malfunction

Design Defect:

• AI architecture fundamentally flawed
• Inadequate training data
• Systematic errors affecting outcomes

Warning Defect:

• Failure to warn of AI limitations
• Inadequate instructions for use
• Missing contraindications

Corporate Practice of Veterinary Medicine

#

Some jurisdictions restrict corporate practice of veterinary medicine:

Concern:

• AI platforms controlled by non-veterinarian corporations
• Potential interference with professional judgment
• Commercial interests affecting care decisions

Implications:

• Platform structure must preserve veterinary independence
• AI cannot direct veterinary decision-making
• Business decisions cannot override clinical judgment

Unlicensed Practice Claims

#

AI systems that diagnose or recommend treatment without veterinary supervision may constitute unlicensed practice:

Risk Factors:

• Direct-to-consumer AI diagnostic apps
• Symptom checkers providing diagnoses
• Treatment recommendations without VCPR
• Prescription advice without veterinary relationship

Best Practices for Veterinary AI

#

Implementation Guidelines

#

Before deploying veterinary AI:

1. Validate performance for your patient population
2. Understand limitations of the specific AI system
3. Establish protocols for human oversight
4. Train staff on appropriate AI use
5. Develop informed consent processes

Clinical Integration

#

When using AI in clinical practice:

• Review AI findings before relying on them
• Apply clinical judgment to AI recommendations
• Document AI use in medical records
• Recognize edge cases where AI may fail
• Maintain human override capability

Telemedicine-Specific Considerations

#

For AI-assisted telemedicine:

• Verify VCPR compliance for each state
• Establish triage protocols for urgent cases
• Ensure human escalation pathways
• Document limitations of remote assessment
• Follow up on AI-triaged cases

Documentation Requirements

#

Medical records should document:

• AI tools used in diagnosis/treatment
• AI findings and recommendations
• Veterinarian’s independent assessment
• Basis for following or deviating from AI
• Client disclosure about AI use

Frequently Asked Questions

#

Related Resources

#

On This Site

#

• Healthcare AI Standard of Care, Human medical AI parallels
• Telemedicine AI, Remote care liability standards
• Diagnostic AI Liability, Diagnostic tool legal issues

Partner Sites

#

• Veterinary Malpractice Attorneys, Find attorneys handling veterinary AI cases
• Professional Liability Resources, Professional standard of care issues