Transforming wearable data into personal health insights using large language model agents

Abstract Deriving personalized insights from popular wearable trackers requires complex numerical reasoning that challenges standard LLMs, necessitating tool-based approaches like code generation. Large language model (LLM) agents present a promising yet largely untapped solution for this analysis a...

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Hlavní autoři:	Mike A. Merrill, Akshay Paruchuri, Naghmeh Rezaei, Geza Kovacs, Javier Perez, Yun Liu, Erik Schenck, Nova Hammerquist, Jake Sunshine, Shyam Tailor, Kumar Ayush, Hao-Wei Su, Qian He, Cory Y. McLean, Mark Malhotra, Shwetak Patel, Jiening Zhan, Tim Althoff, Daniel McDuff, Xin Liu
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Nature Portfolio 2026-01-01
Edice:	Nature Communications
On-line přístup:	https://doi.org/10.1038/s41467-025-67922-y
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