Robert Sanchez, Adam Little, and Aaron Massecar explore new possibilities for client education, computer work, and clinical decision support, alongside the questions of trust and oversight that come with them.

Robert shares an AI-assisted visual explainer and what it could mean for helping pet owners understand care. The hosts debate where to start with computer-use agents, how to keep people in control, and why better clinical answers still need a connection to real patient outcomes.

In this episode:

• How the hosts are thinking about AI risk and safety.
• Computer use and visual design: what new capabilities could change for veterinary teams.
• Personalized client education and interactive learning.
• Choosing a useful first task, setting boundaries, and watching the results.
• Clinical decision support, trusted sources, and the missing outcomes loop.

Try the client-education exercise discussed in the episode:

"Help me identify six topics I repeatedly explain to pet owners. Ask me about the questions clients find hardest to understand. For the most useful topic, propose a short visual explanation with one clear takeaway, a simple storyboard, and a list of medical claims for a veterinarian to check. Use generic examples and no identifiable patient information. Draft the concept first, then wait for my feedback."

Not sure where to start with AI? Try Robert's interview approach:

Ask your AI: "Interview me, one question at a time, until you understand what matters most in my work, what repeatedly gets in the way, and what a useful result would look like. Then ask what must never happen, what information is off limits, and which actions need my approval. Summarize what you learned and suggest three small tasks I can review myself. Recommend one and explain how we'll know it helped. Start with information I provide here; ask before accessing other systems."

Choose a task you have authority to do, review its output, and keep a veterinary professional responsible for clinical content.

What is one task you would like AI to help your team with? Tell us in the episode comments, and follow Veterinary AI Brief for the next conversation.

Recorded September 14, 2026.

Chapters:
00:00 Welcome
01:13 AI risk and safety
15:33 Astra and computer use
19:05 Visual client education
29:24 Where should a practice start?
37:35 Have AI interview you
39:13 Clinical decision support
45:04 The missing outcomes loop
46:07 Novel context and the bitter lesson

Dr. Adam Little, Robert Sanchez, and Dr. Aaron Massecar return with a fast-moving roundup of the biggest veterinary AI stories. The episode centers on a lawsuit involving Zoetis and a misdiagnosed cancer case, then expands into the practical realities of AI receptionists, scribe integrations, and what comes next for governance and security.In this episode, the hosts focus on how the profession should judge AI systems: by perfection, or by relative risk compared to current human-led workflows. They also dig into how voice AI, integrations, and agentic tools are changing daily practice, while warning that security and validation are now urgent priorities.

Key topics

• The Columbia Veterinary Hospital lawsuit against Zoetis, where an AI-assisted screening result allegedly contributed to a missed cancer diagnosis, repeat surgery, and the death of an 11-year-old dog.
• Whether the legal and ethical blame sits with the AI company, the veterinarian, the hospital, or the broader workflow around the case.
• Why the hosts keep returning to relative risk, not perfect performance, when evaluating AI in healthcare and veterinary medicine.
• The concern that companies may market AI with "shock and awe" claims while downplaying limits, failure modes, and validation data.
• How this case could influence veterinarian adoption by increasing caution, liability concerns, or skepticism toward AI-assisted pathology and radiology.
• The rise of AI receptionists and why a 25-minute failed booking experience for a Bordetella vaccine appointment matters as a client-experience case study.
• The idea that bad outcomes often come from workflow failures, not just model failures, especially when latency, context, or agency breaks down.
• Recent veterinary software movement toward deeper integrations, including two-way connections between scribes and PIMS systems, plus MCP-style access to platform data.
• Why integrations matter beyond copy-paste convenience: fewer windows, less friction, better focus, and more of the practice happening in one place.
• The next big concern after integration: security, including sandbox escape risks, hardened validation, and compliance language like ISO 27001 and SOC 2 Type II.

Timestamps

00:00 - Welcome back and what the veterinary AI news cycle has been missing
01:03 - Columbia Veterinary Hospital lawsuit and the Zoetis AI misdiagnosis claim
02:55 - Who is responsible when an AI-assisted decision goes wrong
04:21 - The veterinarian's responsibility versus specialist or lab guidance
06:13 - Was the product positioned clearly as a screening tool
07:35 - Why the hosts reject "is it perfect?" and focus on relative risk
09:14 - Why this lawsuit feels like a broader blame-diffusion problem
10:29 - Why this case is messy compared with a more direct AI failure
12:22 - Could this change veterinarian adoption of AI tools
14:19 - Why edge cases will multiply as more practices use AI daily
16:42 - Why AI literacy and fluency matter more than blind trust
19:01 - Workflow failures and the bridge to AI receptionists
20:27 - The Bordetella booking story and why client friction drives clinic switching
22:26 - Human call friction data and why status quo is not great either
24:20 - Why older AI phone experiences created lasting skepticism
25:41 - AI receptionists, client AI calls, and the demand for transactional workflows
28:53 - GPT Live and the leap in real-time voice interaction
31:43 - Three ingredients of a good phone experience: latency, context, agency
35:59 - New integrations with PIMS and AI tools, including two-way and MCP-style connections
38:46 - Why integration reduces app-switching and improves focus
41:27 - "Tool use" and the future of agents acting on behalf of the practice
44:38 - Why the first scribe workflows had a useful human-in-the-loop bug
46:32 - Validation as building the road, not just the car
47:28 - Security threats, sandbox escape, and why this is the next urgent infrastructure problem
51:35 - How much AI has already replaced in everyday work, from setup to form filling
53:30 - Final takeaways on adoption, implementation, and staying close to the frontier

Join us in this episode of the Veterinary AI Brief as we explore how veterinary professionals are actively transforming their practices with AI tools. From building custom workflows to improving communication and patient care, our guests share their journeys, projects, and practical tips for adopting AI in veterinary medicine.

Main Topics:

• Real-world AI applications in veterinary clinics
• Building and customizing AI tools without coding expertise
• Improving client communication and team workflows
• Practical steps for beginners to start experimenting with AI
• Future opportunities in proactive AI-driven patient management

In this episode:

• How Dr. William Lane leveraged platform tools like Notebook LM and Node-based workflows to streamline clinical tasks
• Dr. Carly Little's innovative communication mentorship program using AI transcripts and feedback
• Dr. Sam Lewis's development of automated patient records and hospital dashboards
• Insights on navigating the learning curve and building impactful tools without extensive coding
• Strategies for starting small, iterating, and tackling problems unique to your practice

Timestamps:

00:00 - Introduction to the episode and guest backgrounds
00:29 - The importance of structured AI workflows in veterinary practice
00:52 - Practical AI projects: From journal searching to patient record automation
01:33 - The impact of custom dashboards and communication tools in clinics
02:11 - Platforms and tools: Notebook LM, Node workflows, Replit, Cursor, Base44
02:36 - Building AI solutions without programming skills: Learning and iteration
03:27 - Developing team-wide and client-facing AI integrations
04:43 - Practical benefits: Patient care, staff efficiency, and client satisfaction
05:42 - Transitioning from reactive to proactive AI management
06:40 - How non-technical vets can start their AI journey
07:09 - Building and customizing AI tools to fit practice needs
08:11 - Project examples: Urgent care boards, pet portals, communication scoring
09:24 - The evolving role of veterinarian as builder of tailored solutions
10:02 - Overcoming the technical learning curve and resource strategies
11:23 - The power of starting small and iterating progressively
12:56 - How AI enhances team collaboration and client transparency
14:10 - Key insights: Cost-effective, flexible, and domain-specific AI tools
16:18 - Future outlook: Proactive, real-time AI interventions in veterinary care
17:45 - Adopting a problem-centric mindset to drive meaningful AI solutions
19:00 - Practical advice for non-technical vets: Use AI as a partner, not a coder
20:18 - The importance of iteration and patience in AI projects
21:23 - Building trust and custom solutions that fit your practice
22:45 - Making AI tools accessible and scalable for veterinary teams
24:10 - The role of AI in improving hospital workflows, diagnostics, and client communication
26:04 - Final takeaways: Ask what you need, start small, and build iteratively

Unlock the future of veterinary medicine with AI-driven breakthroughs proven to enhance diagnostic accuracy, reduce workloads, and transform clinical workflows. In this episode, we explore how cutting-edge research from Lancet's MASAI trials reveals AI's game-changing potential—catching 29% more cancers without increasing false positives and slashing radiologist workloads by nearly half. But what does this look like for vets? Is there a realistic path to integrating these advances into everyday practice?Join us as we break down the implications of AI in diagnostics, from human health to animal care. We delve into how machine learning models are helping detect early metastasis, streamline report writing, and elevate clinical decision-making—all while highlighting the critical importance of vet responsibility and trust. Our conversation with Dr. Tam of Colorado State University reveals why these innovations aren't just scalable for human health—they could redefine how we approach veterinary cancer screening, imaging, and workflow automation.

It's our holiday "step-back" episode: what actually mattered in veterinary AI in 2025—and what's most likely to hit practices hard in 2026. With special guest Jon Ayers, Robert, Adam and Aaron map the rapidly changing landscape: the breakout adoption of AI scribes, the "app explosion" happening around (not inside) PIMS platforms, and why the next year will be less about "tech toys" and more about AI as labor—tools that behave like employees and move spend from software budgets into staffing/COGS. We also get blunt about the reality in clinics: you only have so much change-management budget. So the question isn't "What's the coolest new tool?"—it's "What 1–3 changes will measurably improve throughput, capture more calls, and upgrade the pet owner experience without blowing up your workflow?" In this episode: Why many practices should not switch PIMS in 2026—and what to do instead How scribes became the fastest "new tech" adoption in vet med (and what that signals next) The PIMS platform dilemma: be the ecosystem enabler or get labeled the bottleneck The coming wave: AI receptionists, online booking, and next-gen client communications A practical way to "pick your shots" in 2026 so change doesn't stall everything The bigger stakes: pet-owner economics, access to care, and why regulation may need to evolve If you want a clear, actionable lens for evaluating the flood of tools—and choosing the moves that actually change outcomes—this is the one.

In this episode of the Veterinary AI Brief, co-hosts Robert Sanchez, Dr. Adam Little, and Aaron Massecar dive into the latest advancements in AI technology and its implications for the veterinary industry. The discussion kicks off with a focus on Google's release of Gemini 3, a groundbreaking AI model that promises to revolutionize the field with its multimodal capabilities. The hosts explore how these developments are set to transform veterinary practices, from enhancing diagnostic tools to improving client communication.

Participants

Robert Sanchez, Adam Little, Aaron Massecar

Key Takeaways:

1. Google's Gemini 3 is a game-changer in AI, offering state-of-the-art capabilities that surpass previous models.
2. The integration of AI in everyday applications like Gmail and Google Docs is set to enhance efficiency and user experience.
3. AI's role in veterinary medicine is expanding, with potential applications in diagnostics, client communication, and practice management.
4. The rapid adoption of AI tools in the veterinary industry highlights the demand for innovative solutions that improve workflow and patient care.

Chapters:

• [00:00] Introduction and Welcome
• [05:12] The Impact of Google's Gemini 3
• [15:45] AI in Veterinary Diagnostics
• [25:30] Enhancing Client Communication with AI
• [35:50] The Future of AI in Veterinary Practice Management