Essays
Long-form writing on AI in oncology, clinical informatics, cancer data standards, and what it takes to lead AI inside a health system.
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Clinical AI's real bottleneck is the data layer, not the model
Health systems keep buying models and underfunding the structured data those models need. That ordering is backwards.
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The research-informatics team you need, and how to build it
You cannot hire a research-informatics function fully formed off the market; you have to manufacture most of it on purpose.
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Bringing genomics out of the PDF and into the chart
A tumor sequencing result that lives as a faxed PDF can't trigger an alert, match a trial, or warn the next oncologist, and most of them still do.
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The oncologist's case for clinical informatics as a discipline
If you've never written a note in the system you're trying to fix, you will optimize the wrong thing.
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Predicting immunotherapy toxicity before it happens
The data to flag who will develop immune-related toxicity is already in the chart; we just haven't been reading it for that.
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The academic-industry AI partnership that actually shipped
Most academic-industry AI collaborations end in a data dump and a dead model; the ones that produce deployable work make a few structural choices on purpose.