Unlock the future of AI-driven mental health care while tackling the critical ethical challenges shaping the field today. From bias and misinformation to privacy and patient safety, this course dives into the complexities of AI’s role in mental health. Explore cutting-edge advancements in computing and social robotics, and compare basic and advanced NLP techniques used in mental health analysis. Gain insight into emerging trends that are transforming therapy, diagnostics, and patient support, and examine how AI can be both a powerful tool and a potential risk in mental healthcare. Designed for mental health professionals, policymakers, and tech leaders, this course empowers you to shape responsible AI frameworks that prioritize fairness, transparency, and safety. Whether you're looking to influence policy, integrate AI into healthcare, or understand the future of mental health technology, this course provides the expertise to make an impact. Join us and be at the forefront of building ethical, effective, and human-centered AI for mental health.
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March 2025
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There are 4 modules in this course
This module sets the foundation for understanding critical ethical issues and practical challenges in deploying AI technologies in mental health contexts. You will gain essential skills to advocate for responsible AI practices, assess potential risks, and identify biases in AI systems.
What's included
4 videos7 readings3 assignments2 discussion prompts
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.
What's included
6 videos6 readings3 assignments1 discussion prompt
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.
What's included
7 videos3 readings3 assignments1 discussion prompt
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.
What's included
4 videos4 readings2 assignments1 peer review
Instructor
Offered by
Recommended if you're interested in Machine Learning
Johns Hopkins University
Coursera Instructor Network
Amazon Web Services
Northeastern University
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