# Project #project/AIbasedlearning #note/sourcereview/article ## Topics: #on/generativeai #on/mentalhealthcare ### Source: Kellogg, K. C., & Sadeh-Sharvit, S. (2022). Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians. _Frontiers in Psychiatry_, _13_, 990370. [https://doi.org/10.3389/fpsyt.2022.990370](https://doi.org/10.3389/fpsyt.2022.990370) %% ### Hookmark Link: [Kellogg and Sadeh-Sharvit - 2022 - Pragmatic AI-augmentation in mental healthcare Ke.pdf](hook://file/Rm9fc5agL?p=c3RvcmFnZS9RRjhJUFU0WQ==&n=Kellogg%20and%20Sadeh%2DSharvit%20%2D%202022%20%2D%20Pragmatic%20AI%2Daugmentation%20in%20mental%20healthcare%20Ke%2Epdf) %% ### Notes: “In this Perspective, we describe a framework for “pragmatic AI-augmentation” that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice—automation, engagement, and clinical decision support technologies.” (Kellogg and Sadeh-Sharvit, 2022, p. 1) “Three categories of AI technologies that are of particular interest to MH clinicians are automation technologies, engagement technologies, and clinical decision support technologies (8, 9).” (Kellogg and Sadeh-Sharvit, 2022, p. 2) ### Key AI Technologies ![[CleanShot 2023-05-26 at 06.52.08.jpg]] ### Challenges and potential solutions to AI in mental health care ![[CleanShot 2023-05-26 at 06.52.15.jpg]]