Generative Artificial Intelligence in Mental Healthcare: An Ethical Evaluation (1 credit hour)

Program Summary:  This course explores the benefits and ethical implications of generative AI’s growing presence in mental healthcare.  Using a framework of biomedical ethics, the ethical principles of patient autonomy, beneficence, nonmaleficence, justice, and privacy are each highlighted.  The course examines generative AI tools and offers questions for future research. The course includes an explainer of artificial intelligence tools and resources.

This course is recommended for social workers and is appropriate for beginning and intermediate levels of practice.  This course is not recommended for NBCC ethics credit.

Reading 1: Generative Artificial Intelligence in Mental Healthcare: An Ethical Evaluation Author:  Charlotte Blease, Adam Rodman Publisher: Current Treatment Options in Psychiatry

Reading 2:  Glossary- Artifical Intelligence (AI):  Tools and Resources Publisher: LibGuides at Midwester State University

“Book  Open the Course Reading Here.

Course Objectives:  To enhance professional practice, values, skills and knowledge by evaluating the ethics of generative AI tools in mental healthcare.

Learning Objectives:   Describe the benefits of generative AI tools in mental healthcare.  Describe potential ethical concerns and consequences of AI tools in mental healthcare.  Describe recommendations for future research.

Course Available Until: December 31, 2029.

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1: Which ethical principle requires offering patients relevant, accessible, and timely information about their health and treatment options, so that individuals can exercise their right to healthcare determination?
 
 
 
 
 
2: Clinical records have historically been designed to
 
 
3: A major strength of generative AI is its capacity to rapidly generate summaries of complex data and content and translate such information into ____________ .
 
 
 
4: A well-documented tendency for LLMs to make up false information is referred to as
 
 
 
 
5: ChatGPT is specifically trained on medical data.
 
 
6: The potential for "hallucinations" and medical errors that lead to harm describe concerns related to
 
 
 
 
 
7: Unfair treatment, such as algorithmic biases that exacerbate discrimination, describe concerns related to
 
 
 
 
 
8: Reading 2//  Many machine learning algorithms are "black boxes" meaning that we have a clear understanding of how a system is using features of the data when making their decisions.
 
 
9: In machine learning, _________ will identify rules and patterns in the data.
 
 
10: When using machine learning to solve AI problems a human always understands the rules the algorithm is creating and using to make decisions.
 
 

In order to purchase or take this course, you will need to log in. If you do not have an account, you will need to register for a free account.

After you log in, a link will appear here that will allow you to purchase this course.

Review our pre-reading study guide.

G.M. Rydberg-Cox, MSW, LSCSW is the Continuing Education Director at Free State Social Work and responsible for the development of this course.  She received her Masters of Social Work in 1996 from the Jane Addams School of Social Work at the University of Illinois-Chicago and she has over 20 years of experience.  She has lived and worked as a social worker in Chicago, Boston, and Kansas City.  She has practiced for many years in the area of hospital/medical social work.  The reading materials for this course were developed by another organization.

 

Course Available Until: December 31, 2029.
Filed under: Ethics, Technology