Integrating Clinical Priorities into the Technical Validation of Machine Learning Classifiers: A Generalizable Framework Demonstrated on Opioid Misuse Screening
Jacob Washton, Milan Toma · 2026
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Description
Jacob Washton and Milan Toma argue in 2026 that machine learning tools for medicine should be graded by clinical stakes, not just by tidy accuracy scores. A screening model can look strong on paper while taking shortcuts that fail on real patients, and in high-stakes screening those failures hurt people.
The authors present a general validation framework that folds clinical priorities into the technical checks. They demonstrate it on classifiers meant to flag prescription opioid misuse. The framework is meant to travel: any team building a medical classifier can use it to test whether good numbers mean good medicine.
Catalog record
| CREATOR | Jacob Washton, Milan Toma |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 159 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |