Mind Meets Machine: A Narrative Review of Artificial Intelligence Role in Clinical Psychology Practice

Clinical psychology is undergoing a profound transformation with the integration of artificial intelligence (AI) technologies. While the field has traditionally advanced from psychodynamic theories to cognitive-behavioural and evidence-based approaches, the emergence of AI presents both unprecedented opportunities and new challenges. This narrative review accordingly critically evaluates the integration of AI into clinical psychology practice, encompassing the continuum from assessment and diagnosis to intervention and follow-up care. It applies an analytic lens that differentiates clinically validated tools from experimental prototypes, and it appraises study quality against contemporary standards.

We conducted a comprehensive narrative review of the recent literature, focusing on studies and expert opinions regarding the application of machine learning (ML), deep learning (DL) and natural language processing (NLP) in psychological assessment and therapy, the use of digital phenotyping through smartphones and wearables and the implementation of AI-assisted care models in clinical practice.

AI technologies are increasingly used to automate assessment scoring and analyse multimodal data, enhancing objectivity and scalability while requiring ongoing clinician oversight to address bias and interpretability issues. In therapeutic contexts, AI-driven conversational agents and hybrid 'AI-in-the-loop' systems show promise in supporting engagement and personalization, though ethical and relational concerns remain.

AI offers opportunities for selected assessment, monitoring and triage tasks, but evidence remains heterogeneous. Clinical integration should be incremental and evaluative, prioritizing external validation, calibration and patient-centred outcomes.