OpenAI Acquires Health Records Startup Torch as ChatGPT Health Debuts

OpenAI has bought Torch, a small health records startup, in a deal that sources value at about $100 million in equity, in a bid to bolster its newly launched ChatGPT Health service.

The acquisition brings Torch’s four-person team into OpenAI and folds its core technology straight into the health product unveiled in January 2026. 

Torch gives OpenAI a ready-made system for pulling together scattered medical data at a moment when the company wants to enhance its focus on personal health tools.

Torch had been building what it described as “a medical memory for AI, unifying scattered records into a context engine.” The idea is to take health information spread across clinics, labs, wearables and wellness apps, and make it usable in one place. 

That work now sits at the heart of ChatGPT Health, which allows users to securely link medical records and daily health data inside the chatbot.

While OpenAI did not disclose the price, reports vary. Some put the value near $100 million in equity, others closer to $60 million. Either way, the structure points to an acqui-hire. The team joins; the product becomes infrastructure.

This development lands just over a year after a very different ending for the same founders. Torch’s team met while working at Forward Health, a high-profile clinic startup built around automated care. 

Forward raised close to $400 million before shutting down abruptly in late 2024, laying off staff and closing its doors. Torch’s sale shows how fast fortunes can turn in health technology, where ideas outlive companies.

ChatGPT Health itself is standing carefully. OpenAI says it is a secure, separate space within ChatGPT, designed to help people organise information, prepare questions and understand records, not to replace doctors. More than 260 physicians were involved in building safeguards around how responses are delivered.

With Torch in-house, OpenAI wants to solve one of the hardest problems in digital healthcare; making sense of messy, fragmented data without losing context or trust.