AI-Driven Early Detection of Depression, Anxiety, Schizophrenia, and Bipolar Disorder: A Review of Current Evidence (Preprint)

UNSTRUCTURED Artificial Intelligence (AI) is accelerating the transformation of mental health care, with one of the most exciting uses being early diagnosis of psychiatric illness like depression, anxiety, schizophrenia, and bipolar disorder. Early detection can greatly enhance the prognosis, alleviate symptom intensity, and maximize treatment success. This chapter summarizes and integrates findings from current peer-reviewed evidence on AI-based methods from the learning (ML) and deep learning to natural language processing (NLP) and multimodal digital phenotyping that have been utilized to identify early indicators of these conditions. For depression and anxiety, digital phenotyping research has shown that passive smartphone and wearable data like movement patterns, sleep duration, frequency of social interaction, and speech features can foretell disturbances in mood at moderate to high accuracy levels (Ben-Zeev et al., 2020; Jacobson et al., 2022). Social media analysis with NLP has found linguistic patterns and shifts in sentiment foretelling depression months in advance of clinical diagnosis (Guntuku et al., 2019) In schizophrenia, AI models comparing neuroimaging and EEG biomarkers have obtained very good classification accuracy (up to 99 %) for separating individuals at high risk (Wang et al., 2021). Speech disorganization measurements, based on NLP, have also indicated potential for detecting prodromal states (Bedi et al., 2015). Through the use of wearable technology and historical medical information, real-time mood state monitoring for bipolar disorder has made it easier to identify the early phases of manic and depressive episode transitions and enable prompt intervention (Faurholt-Jepsen et al., 2019). Additionally, bipolar disorder has been successfully differentiated from major depressive illness with over 90% accuracy using deep learning based on resting-state fMRI (Rashid et al., 2022). Hybrid methods combining clinician knowledge with AI forecasts across conditions always provide greater reliability compared to isolated models (Shatte et al., 2019). Typical sources of data in the studies considered here are electronic health records (EHRs), neuroimaging archives, patient self-reported measures, and consumer devices behavioral data. Nevertheless, challenges persist. Privacy concerns, algorithmic bias, inability to generalize across cultural and demographic settings, and limited interpretability pose challenges to widespread clinical use (Vollmer et al., 2020). Most models must be validated on diverse, real world populations to prevent biased predictions. Explainable AI frameworks are critical for establishing clinical trust and enabling ethical deployment. This chapter concludes that while AI is not a substitute for clinical knowledge, it can be an effective augmentation device for early screening, facilitating personalized interventions and prevention strategies. Federated learning to overcome data-sharing constraints, multimodal fusion to enhance predictive strength, and culturally adaptive algorithms to facilitate greater inclusivity are areas of future research that should be given higher priority. With strong validation and ethical regulation, AI can be a key solution to bridging detection gaps in mental health and enhancing outcomes for people globally.