@MaziyarPanahi: A year ago, OpenMed didn't exist. Today: 340M model downloads. 1,500+ open medical models, all Apache 2.0. 650+ run on …
Summary
A year after its inception, OpenMed has achieved 340 million model downloads, offering over 1,500 open medical models under Apache 2.0, with 650+ capable of running on-device on iPhones.
View Cached Full Text
Cached at: 06/29/26, 10:44 PM
🚨 A year ago, OpenMed didn’t exist.
Today: 340M model downloads. 1,500+ open medical models, all Apache 2.0. 650+ run on your iPhone, fully on-device.
Started by one person. Now built by a community.
You should own your AI, especially in healthcare. 👇 https://t.co/AlrlxjTEi8
Similar Articles
maziyarpanahi/openmed
OpenMed is an open-source local-first healthcare AI toolkit that provides entity extraction, PII de-identification, and over 1,000 specialized medical models, all running on-device with no cloud dependency.
@MaziyarPanahi: 6,000,000+ PyPI downloads in under a year. OpenMed 1.5.5 ships today: batch PII, on-device redaction in 9 languages, pl…
OpenMed 1.5.5 ships with batch PII redaction on-device in 9 languages, open-source under Apache 2.0, having surpassed 6 million PyPI downloads in under a year.
@MaziyarPanahi: Open-source clinical AI is a niche that most days feels like shouting into a PubMed-shaped void. And yet. 5,000 of you …
A developer thanks 5,000 followers for supporting OpenMed, an open-source clinical AI project, and hints at upcoming model and dataset releases.
OpenMed 1.8: Apache-2.0 clinical de-identification that runs fully local, now on Android, iOS, and in the browser. 400+ open issues if you want in on 1.9
OpenMed 1.8 is an Apache-2.0 toolkit for clinical de-identification that runs entirely locally, with new support for Android, iOS, and browser platforms, and invites community contributions for version 1.9.
OpenMHC: Accelerating the Science of Wearable Foundation Models
OpenMHC introduces the largest open-access wearable health dataset with over 60 million hours of data and open-source implementations of wearable foundation models, including a unified benchmark for prediction, imputation, and forecasting.