Vision language models applied to daily-life photos quantify visual environmental features that correlate with momentary affect and chronic stress, establishing a paradigm for visual exposomics.
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Health AI benchmarks exhibit a validity gap, with only 42% referencing objective data (mostly wellness wearables), rare complex inputs like labs or imaging, and minimal coverage of vulnerable groups or chronic care.
citing papers explorer
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Quantifying the human visual exposome with vision language models
Vision language models applied to daily-life photos quantify visual environmental features that correlate with momentary affect and chronic stress, establishing a paradigm for visual exposomics.
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The Validity Gap in Health AI Evaluation: A Cross-Sectional Analysis of Benchmark Composition
Health AI benchmarks exhibit a validity gap, with only 42% referencing objective data (mostly wellness wearables), rare complex inputs like labs or imaging, and minimal coverage of vulnerable groups or chronic care.