AI-MindScan: Designing an Adaptive Decision Support System for precise identification of Mental Health issues

Current diagnostic procedures are complicated, time consuming, and need interpretation for a large portion of the population with mental health issues. Limited enrollment, completion, and study fault diagnosis may result from these constraints. To overcome these challenges, this work offers a DCANM and composite machine learning system-based mobile mental health diagnostics DSS. The recommended DSS evaluates in real time using participant responses and an algorithm. The demographic profile, behavior, and clinical data influence question collection, reducing computational time and effort. This talent improves user engagement and completion. The system uses basic deep learning transformer and decision tree models. To improve diagnosis accuracy and population subgroup adaptability, these models assess several multimodal data inputs, including historical registries and current inputs. The proof of concept shows that with 18 dynamically produced questions, the system offers 96% accuracy above established diagnostic methods. The DSS provides diagnostic decision transparency to clinicians and patients using explainable artificial intelligence (XAI). Partnerships help health care personnel make smart choices. Clinically scalable, the DSS interfaces with healthcare information systems via secure cloud environments. When paired with flexibility, precision, and openness, this groundbreaking mental health diagnostic tool may change clinical practice worldwide.