Can open datasets help machine learning solve medical mysteries?

Jennifer E. Engen

The medical data housed in patient records are a clinical researcher’s dream: the key, potentially, to better tools to treat disease and screen with precision. They’re also a computer scientist’s nightmare: locked away in hospital systems, subject to restrictive data-sharing agreements, and often too messy to make use of.

A new open science project wants to accelerate ethical AI in medicine by doing the hard work of collecting and cleaning that data. Nightingale Open Science launched in December with $6 million in funding, led by Schmidt Futures, the philanthropy of ex-Google CEO Eric Schmidt. (It has no affiliation with Google’s controversial health record-mining partnership with Ascension, which went by the code name Project Nightingale). It will freely share de-identified clinical datasets with researchers, linking medical images like X-rays, ECG results, and biopsy slides — 40 terabytes worth, to start — to outcomes from partnered health systems. Hundreds of researchers have signed up for access in its first month.

Unlock this article by subscribing to STAT+ and enjoy your first 30 days free!


Can open datasets help machine learning solve medical mysteries?

Next Post

Inclusive fitness is the alternative to toxic diet and weight-loss culture

Like most social media apps, the fitness side of TikTok is full of content — workout regimes, food videos, and body positive influencers float around For You Pages sharing an overwhelming amount of information about personal health and body image. While some FYPs are awash in hundreds of gym bros, […]