AI-Powered Diagnosis of Mood and Psychotic Disorders: A Systematic Review and Meta-Transfer Learning Framework
Keywords:
Mood and Psychotic Disorders (MPD), Artificial Intelligence (AI), Meta-Transfer Learning (MetaTrans), Machine Learning (ML), Deep Learning (DL), MRI-Based Diagnosis, Rare Disorders, Mental Health AI, Transfer Learning (TL), Automated Psychiatric DiagnosisAbstract
Mood and psychotic disorders significantly impact global mental health, presenting challenges in accurate diagnosis and effective treatment. Among these, conditions such as bipolar disorder, schizophrenia, and schizoaffective disorder remain difficult to detect due to overlapping symptoms, data scarcity, and the lack of robust diagnostic models. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown potential in bridging diagnostic gaps. However, existing AI models often face issues such as small sample sizes, class imbalances, and difficulty in generalizing findings across diverse populations.
This paper presents a systematic review (2018–2024) on the use of ML, DL, and meta-learning (MtL) in diagnosing mood and psychotic disorders (MPD). It highlights existing research gaps and proposes an innovative MRI-based Meta-Transfer Learning (MetaTrans) framework. This framework integrates transfer learning (TL) for common psychiatric conditions with meta-learning strategies to improve adaptability for underrepresented psychotic disorders. The proposed approach aims to enhance diagnostic accuracy, improve generalizability, and facilitate early detection of complex psychiatric disorders. Additionally, the study examines these developments in the context of India’s healthcare system, addressing critical challenges in AI-driven psychiatric diagnostics.
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