A Principle-based Framework for the Development and Evaluation of Large Language Models for Health and Wellness

February 09, 2026

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📝 Original Info

Title: A Principle-based Framework for the Development and Evaluation of Large Language Models for Health and Wellness
ArXiv ID: 2512.08936
Date: 2025-10-23
Authors: Brent Winslow, Jacqueline Shreibati, Javier Perez, Hao-Wei Su, Nichole Young-Lin, Nova Hammerquist, Daniel McDuff, Jason Guss, Jenny Vafeiadou, Nick Cain, Alex Lin, Erik Schenck, Shiva Rajagopal, Jia-Ru Chung, Anusha Venkatakrishnan, Amy Armento Lee, Maryam Karimzadehgan, Qingyou Meng, Rythm Agarwal, Aravind Natarajan, Tracy Giest

📝 Abstract

The incorporation of generative artificial intelligence into personal health applications presents a transformative opportunity for personalized, data-driven health and fitness guidance, yet also poses challenges related to user safety, model accuracy, and personal privacy. To address these challenges, a novel, principle-based framework was developed and validated for the systematic evaluation of LLMs applied to personal health and wellness. First, the development of the Fitbit Insights explorer, a large language model (LLM)-powered system designed to help users interpret their personal health data, is described. Subsequently, the safety, helpfulness, accuracy, relevance, and personalization (SHARP) principle-based framework is introduced a...

📄 Full Content

A fundamental shift is underway in personal health management, as individuals transition from episodic, reactive care to a proactive model driven by personal informatics (Spatz et al., 2024). This transformation is being enabled by consumer health sensing applications, such as wearable devices and m

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