Learn2Trust: A video and streamlit-based educational programme for AI-based medical image analysis targeted towards medical students
Project Overview
The document outlines the creation and execution of Learn2Trust, an innovative online educational program tailored for medical students to enhance their understanding of artificial intelligence (AI) in medical image analysis. This course aims to foster trust in AI technologies by equipping students with essential knowledge about AI and machine learning, while also tackling the skepticism frequently surrounding AI's application in healthcare. Through interactive elements and hands-on exercises, Learn2Trust not only provides theoretical foundations but also emphasizes practical skills, thereby boosting learners' confidence in utilizing AI in their future medical practices. The findings suggest that such educational initiatives can significantly improve students' perceptions of AI, ultimately preparing them to integrate these technologies into their professional lives effectively. Overall, the program exemplifies the potential of generative AI in education, particularly in enhancing trust and competency among future medical practitioners regarding advanced technological tools in their field.
Key Applications
Learn2Trust - an online course for AI in medical image analysis
Context: Medical students at the University of Lübeck
Implementation: The course was implemented as an elective with modules combining theory from videos and interactive exercises using Streamlit.
Outcomes: Increased understanding of AI, reduced skepticism towards AI use, and enhanced willingness to use AI in practice.
Challenges: Participants still reported some skepticism and found the content slightly challenging.
Implementation Barriers
Skepticism
Mistrust in AI due to its 'black box' nature and lack of understanding among medical professionals.
Proposed Solutions: Improving the understanding of AI through educational programs like Learn2Trust.
Complexity of content
Some participants found the course content slightly too challenging.
Proposed Solutions: Further refinement of course materials to ensure they are appropriately challenging while still engaging.
Project Team
Hanna Siebert
Researcher
Marian Himstedt
Researcher
Mattias Heinrich
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Hanna Siebert, Marian Himstedt, Mattias Heinrich
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai