A Futuristic AI-based Mental Status Examination (AI-MSE): A conceptual framework

Abstract The Mental Status Examination (MSE) remains a cornerstone of psychiatric and psychological assessment, offering clinicians a systematic and observational tool to evaluate a patient's cognitive, emotional, and behavioural functioning. Even with the presence of sophisticated diagnostic tools, MSE and comprehensive case history remain the building blocks toward a proper and thorough clinical diagnosis. This article introduces a new AI-enhanced framework that is specifically tailored to upgrade the traditional MSE by incorporating real-time, interactive technologies within the evaluation process. This system would be devised in such a way that it uses AI to dynamically assess all ten primary areas of MSE—Appearance, Memory and Attention, Motor Activity, Speech, Affect, Sensory-Perception, Thought Content, Thought Process, Logical Reasoning, and Insight—using multimodal inputs such as audio, video, and user interaction information. By decreasing the test time without sacrificing depth or diagnostic integrity, the model overcomes limitations in current practice while enhancing precision and consistency in mental health assessment. Additionally, the framework assumes reducing human bias, enabling early detection of psychopathological symptoms, and assisting clinicians in providing timely, tailored care. This inter-disciplinary effort highlights the need for collaboration between AI developers, mental health professionals, and policy stakeholders to guarantee ethical deployment and clinical effectiveness. KEYWORDS: Mental status examination, Artificial intelligence, Psychological assessment, Mental healthcare, Psychiatric diagnosis