Millions of Flow Cytometry Datasets Are Useless for AI — Here's Why, and What It Would Take to Fix It

The Search That Started This I was looking for something specific: what happens when you try to feed flow cytometry data into a machine learning model trained on data from a different lab? The answer, it turns out, is almost always: nothing useful . But the reason is what makes this story worth telling. It's not that the algorithms don't work. It's that the data is a mess — and the mess runs so deep that NIST, the FDA, and NIAID had to convene a joint workshop just to begin addressing it. The Workshop Nobody Expected In June 2025, NIST co-organized a two-day virtual workshop with the FDA and NIAID titled "AI and Flow Cytometry" [1]. The participating institutions read like a who's who of cytometry: Stanford, Yale, University of Rochester, Oregon Health & Science University, BD Life Sciences, Revvity, Mayo Clinic. The workshop's central finding was stark: millions of existing flow cytometry datasets are siloed and unsuitable for AI applications due to inconsistent quality and lack of st