ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1567580
Journaling with Large Language Models: A Novel UX Paradigm for AI-Driven Personal Health Management
Provisionally accepted
- 1Division of Speech Music and Hearing, KTH Royal Institute of Technology, Stockholm, Sweden
- 2Karolinska Institutet (KI), Solna, Stockholm, Sweden
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The 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. This paper introduces a novel AI-driven journaling application that reimagines patient engagement through a natural language interface, enabling users to document health experiences while receiving real-time, context-aware feedback. The prototype combines a secure personal health record with an LLM assistant, fostering reflective self-monitoring and bridging 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 different healthcare experts. Preliminary insights 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) clinicianfacing data summarization, and (5) compliance-first architecture. By 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.
Keywords: Large Language Models (LLMs), AI-driven journaling, patient engagement, Health Literacy, Explainable AI, Data privacy, natural language processing (NLP), Medical AI
Received: 27 Jan 2025; Accepted: 03 Jun 2025.
Copyright: © 2025 Moell and Sand Aronsson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Birger Moell, Division of Speech Music and Hearing, KTH Royal Institute of Technology, Stockholm, Sweden
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