Paper Type
Complete
Paper Number
PACIS2025-1463
Description
By using bibliometric analysis, 820 publications were extracted from Scopus database to explore on research trend and patterns, and co-word analysis. There is positive growth in research for this topic, with a strong focus on adult and aged populations. Medicine and Computer Science dominate the field, highlighting AI and machine learning role in mental health research. The highly cited researches were using multimodal datasets and deep learning algorithms, but other approaches such as hybrid models should be explored for improved accuracy. Future research should focus on clinical validation, ethical AI governance, and real-world applications, integrating AI-driven self-care apps, workplace monitoring, and wearable technologies for early depression detection.The findings and discussion from this analysis can be used to create a better policy and intervention program related to mental health in workplace by intergrating AI and machine learning in mental health program.
Recommended Citation
Mazlan, Nurafira Suraya and Abdul Rahman, Hamirahanim, "Leveraging AI for Workforce Well-Being: A Bibliometric Analysis of Machine Learning in Depression Detection" (2025). PACIS 2025 Proceedings. 18.
https://aisel.aisnet.org/pacis2025/aiandml/aiandml/18
Leveraging AI for Workforce Well-Being: A Bibliometric Analysis of Machine Learning in Depression Detection
By using bibliometric analysis, 820 publications were extracted from Scopus database to explore on research trend and patterns, and co-word analysis. There is positive growth in research for this topic, with a strong focus on adult and aged populations. Medicine and Computer Science dominate the field, highlighting AI and machine learning role in mental health research. The highly cited researches were using multimodal datasets and deep learning algorithms, but other approaches such as hybrid models should be explored for improved accuracy. Future research should focus on clinical validation, ethical AI governance, and real-world applications, integrating AI-driven self-care apps, workplace monitoring, and wearable technologies for early depression detection.The findings and discussion from this analysis can be used to create a better policy and intervention program related to mental health in workplace by intergrating AI and machine learning in mental health program.
Comments
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