Insights for medical AI research.

Adding more data won't fix a bad medical AI model. Learn why data quality and annotation accuracy—not sheer volume—are the key levers for improving medical imaging algorithms.

Labelbox is one of the best-known platforms for AI data annotation — and for good reason. It is well-known in helping teams label images, video, text, and other datasets for machine learning. But as AI projects become more specialized, particularly in healthcare and medical imaging, many organizations are looking for platforms that offer more than annotation alone.

🎯 Objective Learn how IADX, a AI-focused startup I co-founded, in developing a generalist AI model for x-ray images, and learn how the AI sausage is made. 👨‍💻 Back story I am a computer scientist who specializes in medical AI research. I got my PhD in 2021 and since then have worked on a variety of projects helping medical researchers explore the possibility of AI to help clinicians diagnose and treat various conditions. But things started to get a bit more intense in 2021

Data in AI has been quoted as “the new oil” , and as “potentially the most under-valued and de-glamorised aspect of today’s AI ecosystem” . Although the main field of AI is advancing on this understanding, and industry moving with it, Medical AI is significantly behind the curve. One of the biggest misunderstandings right now is what “good data” means. To a Medical Professional, this means having good quality scans, with closed-ended, clearly defined outcomes, with little am

Don't wander into an AI project and try to manage it like a normal software project. You'll make a mess. The Awakening A few years ago I was asked to manage the software development process at Medo.AI, a company working on AI for ultrasound scans. The team was small, enthusiastic, and hardworking, but progress was unpredicable and the company needed to be able to plan better. I dove in, but it became pretty clear to me that a ton of the overall uncertainty was coming from the