Liam (Alireza) Ghiam, Clinical Associate in the UCI Health, Integrative and Functional Medicine Fellow at the Susan Samueli Integrative Health Institute, shared a post on Linkedin:
“When it comes to AI auto-contouring, are residents and faculty seeing it the same way?
A new study by Alexander Qian and Nikhil Kotha shows differences between the two groups.
Residents reported:
- Faster CT-based anatomy learning
- Major time savings they can use for higher-value learning
- Smoother workflow from simulation to approval
- Better well-being
Faculty reported:
- Less convinced auto-contouring improves anatomic learning
- Real time savings
- Clear workflow improvements
- Concern about “deskilling” as manual work decreases
Good news: Residents are checking and correcting. The basics are still being followed. Across more than 1500 OAR contours, 97% of edits were made by residents before attendings even opened the case.
AI contouring is here and becomes a bigger part of our everyday clinical practice. This is very important because contouring shapes patient outcomes.
We need new training strategies that fit how we practice today and how we will practice tomorrow while keeping manual contouring skills strong. Stay tuned for more on how residents use AI, what they think about auto-contouring, and the advice they have for current and future trainees.
A full discussion is coming in 2026.”
Title: The Impact of Artificial Intelligence Auto Contouring on Resident Education
Authors: Alexander S. Qian, Alexander Qian, Nikhil V. Kotha, Evan Porter, Lisa Ni, Christina Phuong, Farnam Mohebi, Rachel A. Sabol, Jie Jane Chen, Hilary P Bagshaw, Julian C Hong, Steve Braunstein
Read the Full Article.
More posts featuring Liam (Alireza) Ghiam.