Explainable AI for Mental Health Diagnosis

Suman Swarnkar; Dr Yogesh Kumar Rathore

doi:10.59367/rmb0m884

Authors

Dr Suman Kumar Swarnkar Associate Professor, Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology Raipur, Chhattisgarh, India (492015)
Dr Yogesh Kumar Rathore Assistant Professor, Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology Raipur, Chhattisgarh, India (492015)

DOI:

https://doi.org/10.59367/rmb0m884

Keywords:

Explainable Artificial Intelligence, Mental Health, Diagnosis, Interpretability, Social Media Analysis, Multimodal Data

Abstract

Explainable Artificial Intelligence (XAI) integration in mental health diagnosis has become a revolutionary strategy to meet transparency, trust, and interpretability in AI-based healthcare systems. The current review integrates recent progress in the use of XAI for detection, diagnosis, and management of mental health using varied methodologies including linguistic analysis, social media mining, wearable biosensors, and deep learning models. Research identifies the value of XAI in explaining opaque AI decisions, building clinician trust, and promoting ethical deployment in delicate mental health environments. Prominently, applications extend across depression and psychotic disorder prediction, autism spectrum disorder evaluation, and suicide risk assessment. New trends are multimodal data fusion, logic-based neural networks, and human-centered interfaces focusing on personalization and clinical usability. There are still challenges of data quality, model generalizability, and user understanding that need to be addressed through interdisciplinary work. The review highlights the significance of XAI not only in enhancing diagnostic correctness but also in facilitating responsible AI implementation in psychiatry. As mental health issues become an urgent public health concern, the integration of explainability with deep AI has exciting avenues for interpretable, accessible, and effective solutions to mental healthcare.

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