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MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling

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arxiv 2310.05231 v2 pith:OF3DVTTI submitted 2023-10-08 cs.HC cs.AIcs.CL

classification cs.HCcs.AIcs.CL
keywords patientsmindfuldiarydailyhealthmentalclinicaldomainjournaling
verification ladder T0 review T1 audit T2 compute T3 formal
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In the mental health domain, Large Language Models (LLMs) offer promising new opportunities, though their inherent complexity and low controllability have raised questions about their suitability in clinical settings. We present MindfulDiary, a mobile journaling app incorporating an LLM to help psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals (MHPs), MindfulDiary takes a state-based approach to safely comply with the experts' guidelines while carrying on free-form conversations. Through a four-week field study involving 28 patients with major depressive disorder and five psychiatrists, we found that MindfulDiary supported patients in consistently enriching their daily records and helped psychiatrists better empathize with their patients through an understanding of their thoughts and daily contexts. Drawing on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. NoRe: Augmenting Journaling Experience with Generative AI for Music Creation

    cs.HC 2025-06 conditional novelty 6.0 of 10

    Journal-based AI-generated music was rated as reasonably reflective of the writing, and users reported gains in reminiscence, emotional regulation, and self-understanding in a seven-day field study.

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