Clinical Interview

Clinical interviews are being revolutionized by the application of machine learning to automate diagnosis and assessment of mental health conditions like depression and PTSD. Current research focuses on developing and refining algorithms, including large language models, graph convolutional networks, and Bayesian approaches, to analyze both textual and audio data from these interviews, aiming for accurate and reliable detection of symptoms and severity. This work holds significant promise for improving access to mental healthcare by assisting clinicians, potentially leading to earlier interventions and more efficient diagnostic processes. However, challenges remain, such as mitigating biases introduced by interviewer prompts and ensuring the robustness and generalizability of these automated systems.