AI is transforming mental health care by enhancing diagnosis, treatment, and accessibility. From AI-powered chatbots that provide real-time emotional support to machine learning models that can detect early signs of mental illness, AI is being used to bridge gaps in traditional mental health care. With rising demand for services and a shortage of professionals, AI-driven tools can assist with screening, therapy support, crisis intervention, and personalized treatment plans. Additionally, AI is advancing mental health research by analyzing large datasets to identify trends and risk factors associated with psychological disorders.

In this module, you'll examine bias and fairness in AI systems, focusing on mental health applications. You'll learn how to identify and mitigate bias while exploring fairness as a key factor in AI decision-making. Key topics include the limitations of current IRBs and ethics processes, real-world examples of bias and fairness, and the challenges of applying traditional bioethics frameworks to AI. By the end of this module, you'll understand why fairness matters in AI-driven mental healthcare and how to develop more equitable and ethical AI systems.

In this module, you'll explore the integration of AI into mental health care, highlighting its evolution, current applications, and limitations. You'll learn about the impact of social determinants of health and stigma on AI effectiveness and watch dedicated videos on these topics. You'll gain an understanding of the ethical considerations and the importance of integrating human expertise with AI in mental health care.