Foundation models to automate x-ray analysis for radiologists
Mecha Health builds foundation models to automate x-ray analysis for radiologists. We take medical images and process them using proprietary models to produce accurate draft medical reports. Our first model was built in less than two months, and beat Microsoft, Google, and OpenAI on clinical accuracy metrics. On top of that, it’s two orders of magnitude smaller and trained with a quarter of the data.
We are partnering with the largest privately owned radiology practice in the US and a multinational tele-radiology company to provide them with their own foundation model, enabling their radiologists to go from reading 1 scan per hour to 1 scan every 5 minutes. By charging on a per scan basis, x-ray report generation represents a 40B+ market opportunity.
Active Founders
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.
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.
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.
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.
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.
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.
Founder
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 (NeurIPS, ICLR). Currently building safe and interpretable radiology automations for the future @Mecha Health
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 (NeurIPS, ICLR). Currently building safe and interpretable radiology automations for the future @Mecha Health
Latest News
Nov 03, 2025
Company Launches
Mecha Health - Vision-language foundation models, that scale healthcare.
See original launch postToday we’re launching a new set of capabilities at Mecha: foundation models that interpret studies the way radiologists do, across anatomy, modality, views, and priors, while fitting into any workflow or software environment.
What’s new is less about “one more model”, and more about a human-like feature set for real workflow:
- Multi-anatomy interpretation
- Multi-modality support
- Multi-view reasoning with full clinical context
- Prior-aware comparison: identifies clinically relevant change vs prior exams
- Environment-agnostic delivery: a radiologist opens the study and finds a high-quality draft report already waiting in their template.
These new capabilities are designed to help frontier practices scale healthcare, without asking radiologists to relearn their workflow.
Our Ask
- If you’re evaluating next-generation reporting AI, or know a group that is, we’d love to talk.
Please reach out directly at ahmed@mecha-health.ai - you can also check us out at https://mecha-health.ai
Previous Launches
Mecha Health 🩻 Trustworthy medical image reporting using AI
Automated analysis of medical images that is accurate and interpretable.
