Use of Artificial Intelligence in Mental Healthcare, Health Psychology, and Related Research: A Narrative Review to Address Challenges and Opportunities

Abstract

Background and Aims: Artificial intelligence (AI) is the process by which a machine learns from a pool of data and can then respond to questions beyond that data set. AI has been implemented in many other fields, and a lot of interest is placed on how AI will advance patient-provider interactions. One of the greatest areas of need is in the field of psychology, as there are comparatively few providers when looking at the inordinate number of patients requiring counseling. System-on-chip technology, its usage in emotion-predicting AI models, and risks of nonclinical chatbots. This review examines the integration of AI in psychology, emphasizing emotion prediction through system-on-chip technologies. It also highlights key concerns surrounding nonclinical chatbot use, including data bias, privacy risks, and the urgent need for responsible and ethical AI deployment. Methods: An extensive literature search (Scopus and Web of Science) was performed in PubMed using keywords "artificial intelligence," "psychology," "mental health," "cognitive psychology," "psychological diagnosis," and "psychology apps," which yielded 112 articles. A total of 76 articles were excluded because of a misalignment with the focus on AI in psychology and mental care. We extracted relevant information from the remaining 36 articles, of which 6 were excluded as they did not meet the predefined criteria of AI application in this context. Results: Although AI models have demonstrated potential in prediction, decision, and patient support, there are no FDA-approved diagnostic tools in clinical psychology. System-on-chip (SOC) models show evidence for high accuracy in real-time emotion recognition, and chatbots support patients with home symptom monitoring. However, AI in mental health is limited by biased or incomplete data sets, raising concerns about reliability. Privacy risks, particularly with nonclinical chatbots like ChatGPT, further complicate implementation. Regulatory and ethical barriers remain unresolved, and despite strong research promise, clinical adoption is still limited. These challenges highlight the need for cautious, evidence-based integration. Conclusions: AI has the potential to assist clinicians and researchers in psychology. Careful consideration is needed of ethical, regulatory, and methodological concerns with new AI-based models and tools. With use rooted in evidence, integration of AI tools would enhance provider efficiency and access.

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