Building AI competence in the healthcare workforce with the AI for clinical care workshop: A Bridge2AI for clinical CHoRUS project
Year: 2025
Type: article
Abstract: Background: The implementation of artificial intelligence (AI) tools into clinical spheres emphasizes the critical need for an AI-competent healthcare workforce that can interpret AI output and identify its limitations. Without comprehensive training, there is a risk of misapplication, mistrust, and underutilization. Workforce skill development events such as workshops and hackathons can increase AI competence and foster interdisciplinary collaboration to promote optimal patient care. Methods: The University of Florida hosted the AI for Clinical Care (AICC) workshop in April 2024 to address the need for AI-competent healthcare professionals. The hybrid workshop featured a beginner and advanced track with interactive sessions, hands-on skill development, and networking opportunities led by experts. An anonymous, voluntary post-workshop survey asked participants to score their knowledge and skills before and after the AICC workshop. A second, follow-up survey was administered approximately nine months later. Results: Ninety participants attended the AICC workshop, forty-one attendees completed the post-workshop survey, and six attendees completed the follow-up survey. Paired T -tests of the post-workshop survey revealed statistically significant ( P < .001) increases in self-reported knowledge gain across all six beginner track learning objectives and significant ( P < .05) increases across all five advanced track objectives. Feedback indicated participants appreciated the interactive format, although communication and networking needed improvement. Conclusion: The AICC workshop successfully advanced AI literacy among biomedical professionals and promoted collaborative peer networks. Continued efforts are recommended to enhance participant engagement and ensure equitable access to AI education in clinical settings.
Authors: Andrea E. Davidson, Aiden Jose, Benjamin Shickel, Kaleb E. Smith, Parisa Rashidi, Yulia Levites Strekalova, Azra Bihorac
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,200
OpenAlex ID: W4414774108