Session Design Submission Review

Yes

• AI

Artificial intelligence (AI) methods, including machine learning, hold tremendous promise to advance surgical education, offering new tools to enhance learning, improve skill acquisition, and personalize training for surgical trainees.  However, while the potential of AI in this space is vast, it also brings significant challenges that must be addressed to ensure successful and safe integration.

This panel session will explore the potential and the barriers that AI presents in surgical education. It will also present practical strategies for overcoming these challenges to create a more effective and inclusive learning environment for the next generation of surgeons. The discussion will be focused on the following topics

1. Application of AI in Surgical Education, current landscape : AI is being used in surgical education to augment traditional surgical training through automated performance analysis and feedback. The panel will highlight the potential with examples from undergraduate and graduate medical education.
2. Barriers to Implement AI in Surgical education: While AI holds promise, its integration into surgical education isn’t without hurdles. These include the need for large, high-quality data sets to develop AI models, concerns about the cost and scalability of AI technologies across institutions, gaps in obtaining funding to advance AI research in surgical education. The session will explore how educators can address these issues and make AI solutions more accessible.
3. Recent Advances in AI models including vision, synthetic data to augment training and LLM in surgical education (Innovation): The panel will highlight recent advances in various aspects of AI including GenAI, LLM and vision models as well as use of synthetic data generation for training.
4. Mitigating Bias in Training Data – Safe integration of AI in Surgical Education (Challenges): When developing and implementing AI in surgical education, one must be aware of the bias in data that is used for training. The panel will highlight various sources of bias and discuss strategies in mitigating them.

Targeted Audience:

ASE members with emphasis on

1. Surgical educators looking to incorporate AI into their curricula.
2. Medical students and surgical residents interested in how AI may shape their training.
3. AI researchers and developers working on healthcare applications.

Gain a clear understanding of how AI is currently being used to enhance surgical training, including tools like simulation technologies, automated skill assessment , and personalized learning pathways.

Recognize and discuss the major barriers to implementing AI in surgical education, including technical limitations, data quality requirements, cost concerns, and institutional resistance and funding.

Gain a clear understanding on various way to augment their data for training AI models

Understanding and learn mitigating strategies for managing sources of bias in the data and its effect on training an AI model