Precision medicine's inevitable trajectory toward rare-disease-sized cohorts: implications for machine learning and deep learning.
As precision medicine fragments patient populations into smaller groups, researchers propose practical methods like shared learning to maintain statistical power in rare subsets.
This Lancet Digital Health perspective argues that molecular stratification in precision medicine is inevitable creating rare-disease-sized cohorts, challenging conventional ML/DL approaches that require large training datasets. The authors outline implications and propose methodological adaptations including federated learning, transfer learning, and few-shot approaches.
What the study was
- Study design
- Perspective/Review
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- The Lancet Digital Health
Why it surfaced
Thoughtful perspective in Lancet Digital Health addressing a real methodological challenge at the AI + rare disease + precision medicine intersection. Score 6/10: moderate novelty (the problem is known, solutions are proposed, 2), relevant for AI+precision medicine field (2), perspective design (1), rare disease population relevance (1).
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