Accepted author manuscript, 322 KB, PDF document
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Review of AI-Based Mental Health Apps
AU - Alotaibi, Abeer
AU - Sas, Corina
PY - 2023/7/27
Y1 - 2023/7/27
N2 - The last decade has seen a significant growth of HCI research in mental health technologies while AI raises both challenges and opportunities to better support symptom identification or personalization of interventions. There has been also a growth of commercial AI-based mobile apps for mental health. Despite emerging HCI work on reviewing mental health apps, those that are AI- based have received limited attention. To address this gap, we report a functionality review of 13 such apps selected from 127 apps from the Apple Store. Findings indicate that apps support functions such as tracking and detecting emotions and moods, providing recommendations for therapy and well-being interventions, and supporting talking therapy through conversational agents powered by NLP models. A critical finding is apps’ limited support for AI literacy and explainability, as well as limited consideration for ethical concerns regarding personal data, its reliability, and algorithmic biases. Our paper concludes with three design implications for AI-based mental health apps towards developing conversational agents to support CBT interventions based on tracked multimodal data, addressing the ethics of NLP biases, and user exploration of AI-based models and their XAI.
AB - The last decade has seen a significant growth of HCI research in mental health technologies while AI raises both challenges and opportunities to better support symptom identification or personalization of interventions. There has been also a growth of commercial AI-based mobile apps for mental health. Despite emerging HCI work on reviewing mental health apps, those that are AI- based have received limited attention. To address this gap, we report a functionality review of 13 such apps selected from 127 apps from the Apple Store. Findings indicate that apps support functions such as tracking and detecting emotions and moods, providing recommendations for therapy and well-being interventions, and supporting talking therapy through conversational agents powered by NLP models. A critical finding is apps’ limited support for AI literacy and explainability, as well as limited consideration for ethical concerns regarding personal data, its reliability, and algorithmic biases. Our paper concludes with three design implications for AI-based mental health apps towards developing conversational agents to support CBT interventions based on tracked multimodal data, addressing the ethics of NLP biases, and user exploration of AI-based models and their XAI.
M3 - Conference contribution/Paper
BT - 36th International BCS Human-Computer Interaction Conference
ER -