Visual Healthcare AI Meetup 👨‍💻

Link to Voxel51 Blog Post 

Link to talk on YouTube

Healthcare AI & ANIML Health Research

Healthcare AI is fundamentally transforming patient care by improving diagnostic accuracy, optimizing workflows, and enabling personalized treatment plans. Through advanced algorithms and data-driven insights, AI empowers healthcare professionals to make more informed decisions, driving better patient outcomes and operational efficiency. As this transformative technology continues to evolve, it is shaping a future where healthcare becomes more accessible, effective, and equitable for both patients and providers.

Recently, the ANIML Health team had the privilege of presenting to over 100 attendees at the Visual Healthcare AI Meetup, hosted by Voxel51. Voxel51’s platform enables AI research teams to implement, test, and evaluate AI models within clinical contexts, though its capabilities extend far beyond just healthcare applications. This makes it an ideal environment for advancing AI in medicine.

At the event, we focused on the growing field of comparative computational oncology, where animal healthcare serves as a platform for developing AI and computational systems that can be translated into the human medical space. This approach also promotes the adoption of AI in veterinary medicine. The benefits of this dual application are clear:

Veterinary medicine is less regulated, providing a unique “playground” for the development and testing of AI models. This allows academic researchers, veterinary teams, and companies to help shape the evolving AI landscape alongside regulators in ways that can inform both human and animal healthcare.

However, the lack of regulation comes with challenges. At ANIML Health, we are deeply committed to transparency because we have observed the rapid adoption of AI platforms in veterinary medicine with little to no reporting on performance metrics or rigorous evaluation of data. This can hinder the long-term success and safety of AI technologies in the field. Ensuring accountability in AI development is critical to maintaining the trust of clinicians, researchers, and, ultimately, the public.

We highlighted several of our projects that we’ve discussed here before, but we are super excited to showcase some preliminary data on oral tumour segmentation modelling using Segment Anything Models (SAM), fine-tuned medical SAMs (medSAM and medSAM2) as well as the development of our own fine-tuned veterinary SAM for oral tumour segmentation.

In the photo above, instead of simply identifying the tumor using computer vision (by placing a box around the affected area), we take it a step further by autosegmenting the precise margins of the tumour. This advanced approach enables both remote monitoring for clinical trials (patient-facing) and volumetric assessments for clinicians (clinician-facing). Additionally, analyzing the textures and pixel-to-pixel relationships enhances our ability to predict tumour histology, behaviour, and treatment response, improving clinical decision-making.

We are equally passionate about extending these techniques to spatial computing. By utilizing iPhone camera acquisition combined with Light Detection and Ranging (LiDAR) sensors, we capture not only images and positional data but also depth information. This technology enables us to create 3D models of patients (and their tumours), which can be used for treatment planning and shared directly with their family members. Additionally, it opens up a wide range of applications, from surgical planning and delineation to the advanced textural analysis and response evaluation mentioned earlier.

Check out the talk links above - we were so fortunate to have the opportunity to discuss what we’ve been up and we can’t wait to show you more!