Personalization of Large Foundation Models for Health Interventions

The "Generalizability Paradox" reveals a fundamental problem: AI models that perform well in one clinical study often perform at chance level in others, showing that achieving high accuracy and broad applicability are in tension—a critical concern for deploying personalized health AI across different populations and settings.

Multiple contradictions undermine AI personalization in healthcare: Beyond generalizability issues, the paper identifies the privacy-performance paradox (better personalization often requires more data), the scale-specificity paradox (models work well at one scale but not others), and the automation-empathy paradox (automated recommendations may reduce human clinical judgment), suggesting no single technical solution exists.

Large foundation models alone cannot determine what treatment works for individual patients: While LFMs can predict patterns from population data, the paper emphasizes they lack the causal understanding needed for truly personalized medicine—they can identify correlations but not prove that a specific intervention will work for a specific person.

N-of-1 trials (personal self-experiments) should complement, not be replaced by, AI: The paper proposes combining LFMs' strength in generating hypotheses from large datasets with N-of-1 trials' ability to test what actually works for individuals, while preserving privacy through local experimentation rather than centralizing personal health data.

Responsible AI deployment requires acknowledging fundamental tensions: Rather than claiming AI can solve personalization alone, developers should recognize inherent trade-offs between accuracy, privacy, generalizability, and human judgment in healthcare applications.