Machine learning from data to diagnosis: A comprehensive review of AI applications in mental health assessment

Abstract

This systematic review examines machine learning and artificial intelligence applications in mental health disorder diagnosis, prediction, and management to identify current trends, knowledge gaps, and future research directions. We conducted comprehensive database searches across PubMed, IEEE Xplore, Scopus, and ScienceDirect from 2013-2024, following PRISMA guidelines for systematic review methodology. After screening 1,156 records, 46 peer-reviewed studies met inclusion criteria for analysis. Studies employed supervised learning methods, deep learning architectures, and natural language processing techniques across depression, anxiety, schizophrenia, bipolar disorder, autism spectrum disorder, ADHD, and OCD. Data sources included neuroimaging, wearable sensors, social media content, electronic health records, and multimodal data integration approaches. Depression and schizophrenia dominated research focus due to public health impact, with publication frequency increasing significantly after 2017. Most studies reported performance using accuracy, sensitivity, specificity, and F1-scores, though validation protocols varied considerably across investigations. Machine learning models demonstrated promising diagnostic accuracy and early detection capabilities across multiple mental health conditions. However, significant challenges persist including limited model generalizability, inconsistent external validation, data quality heterogeneity, algorithmic bias concerns, and clinical implementation barriers. Future research should prioritize developing explainable AI models, establishing standardized evaluation frameworks, implementing robust ethical guidelines, and fostering interdisciplinary collaboration between technology developers and healthcare providers to translate AI innovations into clinical practice for improved early detection, personalized treatment approaches, and enhanced diagnostic accuracy in mental healthcare.