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

要旨

Large Language Models (LLMs) offer promising opportunities in mental health domains, although their inherent complexity and low controllability elicit concern regarding their applicability in clinical settings. We present MindfulDiary, an LLM-driven journaling app that helps psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals, 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 examined how MindfulDiary facilitates patients' journaling practice and clinical care. The study revealed that MindfulDiary supported patients in consistently enriching their daily records and helped clinicians 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.

著者
Taewan Kim
KAIST, Daejeon, Korea, Republic of
Seolyeong Bae
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
Hyun AH Kim
NAVER Cloud, Gyeonggi-do, Korea, Republic of
Su-woo Lee
Wonkwang university hospital, iksan-si, Korea, Republic of
Hwajung Hong
KAIST, Deajeon, Korea, Republic of
Chanmo Yang
Wonkwang University Hospital, Wonkwang University, Iksan, Jeonbuk, Korea, Republic of
Young-Ho Kim
NAVER AI Lab, Seongnam, Gyeonggi, Korea, Republic of
論文URL

https://doi.org/10.1145/3613904.3642937

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Writing and AI A

311
4 件の発表
2024-05-16 18:00:00
2024-05-16 19:20:00