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Mecha Health

Foundation models to automate x-ray analysis for radiologists

Y CombinatorWinter 2025ActiveHealthcareArtificial IntelligenceMachine LearningComputer VisionHealth Tech

What they do

Problem

Radiologists spend significant time manually interpreting x-rays and writing reports, limiting throughput and creating bottlenecks in diagnostic workflows.

Solution

A proprietary foundation model that analyzes medical x-ray images and generates accurate draft radiology reports on a per-scan basis.

Who it's for

Radiology practices and tele-radiology companies whose radiologists interpret x-ray scans and produce clinical reports.

Founders

Ahmed Abdulaal
Founder

Medical doctor @Imperial, where I gained entry to the most competitive deanery in the country. Microsoft PhD scholar in machine learning @UCL - I have more than 20 peer reviewed publications in medicine, digital healthcare, and machine learning - including first author publications in top AI venues such as ICLR. 360+ citations on google scholar. Ex Research Scientist at the Center for Artificial Intelligence @AstraZeneca. Building the future of automated radiology @Mecha Health.

Hugo Fry
Founder

Mathematics (BA) and Physics (MSc) graduate from the University of Cambridge, ranking top 30 in the university. British Physics Olympiad participant, ranking in the top 11 in the UK. Former ML interpretability research scholar (MATS), where I was the first in the world to use Sparse Autoencoders on vision models. My research has been cited by Anthropic and Google DeepMind. Currently working on building state of the art radiology report generation AI in a way that is interpretable and safe.

Ayodeji Ijishakin
Founder

I am a Machine Learning & Medical Imaging PhD candidate from University College London (UCL). During my PhD I published work at tier-one Machine Learning venues (NeurIPS, ICML & ICLR), and was invited to present my work at international medical imaging conferences, Digitas, & Harvard Medical School. Before this startup I was poached by my Professors from UCL including the Head of Computer Science to be CEO for their spin-out. Currently building Radiology 3.0 by Automating reporting.

Nina Montaña Brown
Founder

Solving the interpretive moment in radiology. ex-ML Engineer across multiple medical device startups, with experience in FDA and CE marked medical devices, as well as LLM safety in healthcare. My PhD in Medical Imaging (UCL) focussed on surgical vision; my work has been patented, cited 100+ times, and published across the world's highest impact scientific venues (Nature, NeurIPS, ICLR).

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