Mental health prediction in the era of AI: the review of machine learning techniques and ethical concerns
Year: 2026
Type: article
Abstract: The human mental sensation is important for overall moral health and affects our social lives, our daily activities, and the quality of our lives. Since depression, anxiety, and chronic stress are becoming more commonly discussed worldwide, there is an urgent need for models that can quickly, precisely, and easily help predict them. Most traditional ways of diagnosing involve subjective views and clinicians’ intuition, and these can lead to inconsistency, slow results, and be out of reach for many communities. Because of machine learning (ML), it is now possible to examine complex data from many sources, such as information shared on questionnaires, worn sensors and devices, observed online activity, and various therapy platforms. This work examines the latest advances in using machine learning for predicting mental well-being. The research looks at many types of algorithms, including Support Vector Machines, Random Forests, and deep learning models, which have helped to spot early signals of mental struggles. Subsequently, the work’s major focus is given to the kind of data processed, significant performance factors, and the results observed with a model. Along with the paper, emphasis areas such as blending context-specific and user-adapted models were introduced as likely approaches for boosting accuracy and relevance. Apart from reviewing technical aspects, the paper looks at ethical and practical issues that cover privacy of data, discrepancies in algorithms, making sense of the data, and obeying regulations. The paper aims to bring together new studies, point out promising developments, and identify missing elements that must be handled for ML to succeed in mental health care. The information in this research can act as a reference for researchers, practitioners, and policymakers.
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7224981166