Abstract

Artificial Intelligence (AI) has shown strong potential in medicine, particularly in oncology, dermatology, and radiology. However, its application in mental health care remains limited due to insufficient data availability. This research investigates knowledge-sharing practices among mental health practitioners to determine how data can be effectively captured and utilized for AI integration.

Using qualitative interviews and surveys with mental health professionals identifies barriers such as time con straints, digital skills gaps, resistance to change, and ethical concerns under GDPR, while also highlighting potential benefits of AI, including early detection of behavioural patterns, improved diagnosis, and enhanced patient independence. Nevertheless, respondents recognised AI's potential to support earlier detection of be havioural patterns, enhance communication, and improve patient independence.

By applying Nonaka's SECI model to mental health knowledge flows, this study proposes the Knowledge-Shar ing AI Model (KSAI-Model). The model illustrates how tacit clinician insights can be externalised, combined within Electronic Patient Records (EPRs), analysed by AI, and reintegrated into practice under clinician oversight.

Findings demonstrate that effective knowledge-sharing is central to overcoming current limitations in the integration of AI in mental health.

DOI: doi.org/10.63721/25JNRP0104

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