Teen Builds AI Tool to Detect Arthritis in Dogs Early

When Anshul Bhatt's family dog Max was diagnosed with advanced arthritis, the damage was already irreversible. That heartbreak sparked a question: what if we could catch these conditions before it's too late?

The 17-year-old didn't wait for answers. He built them.

Using OpenAI's ChatGPT as his personal tutor, Anshul taught himself concepts usually reserved for graduate students. Topics like sensor fusion, motion tracking, and Kalman filters became less intimidating when he could ask questions and debug code in real time.

The result is PawPath, an AI-powered system that detects early signs of arthritis, ligament injuries, and neurological disorders in dogs. Four lightweight sensors attach to a dog's legs and record their movement hundreds of times per second. Machine learning analyzes those patterns to flag problems that might be invisible to the human eye.

What makes PawPath special isn't just the technology. It's the accessibility.

Unlike expensive lab equipment, PawPath is portable and affordable enough for small veterinary clinics and animal shelters. Large welfare centers are already testing the device, using it to prioritize which rescued dogs need immediate attention.

"This isn't about replacing vets," Anshul explains. "It's about giving them a faster, data-backed way to decide which animals need help first."

His approach to learning offers a glimpse into how AI is changing what's possible for young innovators. Without access to traditional research labs or mentors, Anshul turned to ChatGPT as his collaborator. It helped him grasp difficult ideas faster and move from theory to prototype.

The Ripple Effect

Anshul's work extends beyond dogs. During a summer program at MIT, he collaborated with Harvard Medical School to build voice-activated AI agents that automate repetitive tasks in radiology. The goal was reducing clinician fatigue and freeing up doctors to focus on complex cases.

Both projects share a common thread: using AI to extend human capability, not replace it. Whether helping veterinarians prioritize cases or supporting radiologists through long shifts, the technology serves the people doing the work.

Anshul has earned recognition along the way, including the Grand Award at the Regeneron International Science and Engineering Fair. But awards weren't the point. Making a tangible difference was.

"You don't need a lab or a team of experts to start," he says. "You just need curiosity and a willingness to build."

His story proves that the next generation of problem-solvers isn't waiting for permission or perfect resources. They're learning independently, exploring deeply, and creating tools that matter.