AUTHOR=Moëll Birger , Sand Aronsson Fredrik 

TITLE=Journaling with large language models: a novel UX paradigm for AI-driven personal health management

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1567580

DOI=10.3389/frai.2025.1567580

ISSN=2624-8212

ABSTRACT=IntroductionThe integration of large language models (LLMs) into personal health management presents transformative potential, but faces critical challenges in user experience (UX) design, ethical implementation, and clinical integration.MethodThis paper introduces a novel AI-driven journaling application, a functional prototype available open source, designed to encourage patient engagement through a natural language interface. This approach, termed “AI-assisted health journaling,” enables users to document health experiences in their own words while receiving real-time, context-aware feedback from an LLM. The prototype combines a personal health record with an LLM assistant, allowing for reflective self-monitoring and aiming to combine patient-generated data with clinical insights. Key innovations include a three-panel interface for seamless journaling, AI dialogue, and longitudinal tracking, alongside specialized modes for interacting with simulated healthcare expert personas.ResultPreliminary insights from persona-based evaluations highlight the system's capacity to enhance health literacy through explainable AI responses while maintaining strict data localization and privacy controls. We propose five design principles for patient-centric AI health tools: (1) decoupling core functionality from LLM dependencies, (2) layered transparency in AI outputs, (3) adaptive consent for data sharing, (4) clinician-facing data summarization, and (5) compliance-first architecture.DiscussionBy transforming unstructured patient narratives into structured insights through natural language processing, this approach demonstrates how journaling interfaces could serve as a critical middleware layer in healthcare ecosystems-empowering patients as active partners in care while preserving clinical oversight. Future research directions emphasize the need for rigorous trials evaluating impacts on care continuity, patient-provider communication, and long-term health outcomes across diverse populations.