Nina Montaña Brown

Nina Montaña Brown
Occupation	Co-founder and CTO of Mecha Health
Known for	Co-founding Mecha Health

Nina Montaña Brown is a technology entrepreneur and the co-founder and chief technology officer (CTO) of Mecha Health, an applied artificial intelligence company that develops foundation models to automate x-ray analysis for radiologists. The company participated in Y Combinator's Winter 2025 batch.

Career

Montaña Brown co-founded Mecha Health, which is based in San Francisco and operates at the intersection of artificial intelligence, computer vision, and healthcare. The company builds proprietary foundation models that take medical images—primarily x-rays—and generate accurate draft medical reports for radiologists. According to the company, its first model was built in less than two months and outperformed models from Microsoft, Google, and OpenAI on clinical accuracy metrics, while being two orders of magnitude smaller and trained on a quarter of the data.

Mecha Health's system is designed to process DICOM, HL7, and FHIR medical image formats, producing editable draft reports in a matter of seconds. The company states that its technology can enable radiologists to increase their throughput from reading one scan per hour to one scan every five minutes. Mecha Health operates on a per-scan pricing model and identifies x-ray report generation as representing a market opportunity exceeding $40 billion.

The company has partnered with what it describes as the largest privately owned radiology practice in the United States, as well as a multinational tele-radiology company, to provide customized foundation models for their operations.

Mecha Health raised $4.1 million in seed funding, with the round led by Valia Ventures and including participation from Y Combinator, Rebel Fund, Reach Capital, and Phosphor Capital.

Montaña Brown has contributed to academic research in the field of medical AI. She is a co-author of a paper published in Nature titled "A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation," which addresses the integration of large language models into healthcare settings. She is also a co-author of a paper accepted at ICLR 2026 titled "Balancing Act: Diversity and Consistency in Large Language Model Ensembles."