Artificial intelligence (AI) and machine learning (ML) are relatively new fields, and only recently have we begun to develop the application of these models to surgical practice and education. AI and ML models may be useful tools in predicting surgical risk to reduce unnecessary interventions for low-risk patients and stratifying high-impact interventions for high-risk patients . However, most surgeons lack the opportunity to learn about his AI and ML, lack basic knowledge of these data-driven processes and systems, and recognize their potential applications in surgical practice. I have not.
This introductory course from the ACS Education Division focuses on the key principles of AI and ML and their application to support decision-making and enhance surgical care. Attendees will develop a foundational knowledge of the principles on which AI and ML are built, and potentially identify opportunities to apply the technology to their practice. Further development will enable data-driven machine intelligence to inform clinical decision-making, allowing surgeons to more accurately assess risk, predict disease progression and manage patients in the early stages of disease. It is considered to be.
Specific examples and algorithms covered in this course include:
- Predict risk of small bowel obstruction for a specified period after procedure
- Strengthening the diagnosis and malignancy prediction of irregular thyroid nodules
- Improving patient diagnosis and management in the early stages of breast cancer
It also discusses practical ML limitations and ethical considerations. This course will help the surgeon and surgical leader attain some fluency in her AI and ML principles and language to facilitate conversations between leaders and technical experts.
Course module
This course contains 8 online modules:
- Course introduction
- Using Machine Learning in Patient Services
- Using machine learning in the diagnosis and treatment of small bowel obstruction
- Prediction of malignancy of thyroid nodules using machine learning
- Using Machine Learning to Diagnose Mammographically Detected Breast Cancer
- What value will AI bring to the surgical profession?
- Automated analysis of surgical videos
- Improving surgical performance and safety with explainable AI
- Machine learning ethical considerations
- Conclusion: The Future of Machine Learning for Surgeons
