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Artificial Intelligence and Machine Learning: Transforming Surgical Practice and Education
This introductory course presents fundamental concepts of artificial intelligence (AI) and machine learning (ML) and suggests some initial potential applications to surgical practice. The goal is to stimulate interest, initiate the learning curve, and encourage surgeons to seek opportunities to learn more and keep abreast of developments in AI and ML. In the future, data-driven machine intelligence most likely will be able to inform clinical decision-making and allow surgeons to more accurately assess risk, predict disease progression, and manage patients with early stages of disease. Other applications may improve surgical performance and operating room safety, reduce variability, and positively impact educational and patient outcomes. Few formal educational activities specifically designed for surgeons in the areas of AI and ML are available. This introductory course from the ACS Division of Education highlights the essential principles of AI and ML and their application to support decision making and enhance surgical care.
Course Lessons
The course includes the following eight on-line lessons:
- Course Introduction
- Lesson 1: Using Machine Learning in the Service of our Patients
- Lesson 2: Using Machine Learning in the Diagnosis and Treatment of Small Bowel Obstruction
- Lesson 3: Using Machine Learning for the Prediction of Malignancy in Thyroid Nodules
- Lesson 4: Using Machine Learning to Diagnose Mammographically Detected Breast Cancer
- Lesson 5: What Value does AI Bring in the Surgical Profession?
- Lesson 6: Automated Analysis of Surgical Video
- Lesson 7: Explainable AI for Improving Surgical Performance and Safety
- Lesson 8: Ethical Considerations for Machine Learning
- Conclusion: The Future of Machine Learning for Surgeons
Target Audience
Designed for surgeons, residents, administrators, and high-level decision-makers in surgery departments, this on-line course provides a fundamental understanding of the principles of artificial intelligence (AI) and machine learning (ML) and how they can be used to improve the delivery of healthcare and surgical patient outcomes. The course from the ACS Division of Education focuses on case studies that illustrate the application of machine learning to surgery. Limitations and ethical considerations are also presented. Surgeons and department leaders interested in being able to converse with AI and ML technical experts will find the course beneficial.
Learning Objectives
At the conclusion of this activity, participants will be able to:
- Outline the foundational principles of artificial intelligence and machine learning that are most relevant to the application of artificial intelligence and machine learning in surgical practice and education.
- Summarize surgical case scenarios in which artificial intelligence and machine learning could be applied to enhance care.
- Describe the opportunities and limitations associated with the use of artificial intelligence and machine learning to support decision making of surgeon leaders.
Contact
- For technical assistance, please contact learning@facs.org.
Course Description
Artificial intelligence (AI) and machine learning (ML) are comparatively new fields and only recently have efforts begun to develop applications 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 in stratifying high impact interventions for high-risk patients. Most surgeons, however, have not had the opportunity to learn about AI and ML, lack basic knowledge of these data-driven processes and systems, and are unaware of their potential applications in surgical practice.
This on-line course from the ACS Division of Education provides an introduction to artificial intelligence (AI) and machine learning (ML). Participants will develop the foundational knowledge of the principles on which AI and ML are structured and may be able to identify opportunities for possible application of the technologies to their practices. With further development, data-driven machine intelligence most likely will be able to inform clinical decision-making and allow surgeons to more accurately assess risk, predict disease progression, and manage patients with early stages of disease. Specific examples and algorithms illustrated in this course include 1) predicting the risk of small bowel obstruction during a specified period of time after a procedure, 2) enhancing the diagnosis and malignancy prediction of indeterminate thyroid nodules, and 3) improving the diagnosis and management of patients in the earliest stage of breast cancer (DCIS). Limitations and ethical considerations of machine learning in practice are also presented. This course should assist surgeons and leaders in surgery departments with developing some fluency in the principles and language of AI and ML to facilitate conversations between leadership and technical experts.
Course Lessons
The course includes the following eight on-line lessons:
- Course Introduction
- Lesson 1: Using Machine Learning in the Service of our Patients
- Lesson 2: Using Machine Learning in the Diagnosis and Treatment of Small Bowel Obstruction
- Lesson 3: Using Machine Learning for the Prediction of Malignancy in Thyroid Nodules
- Lesson 4: Using Machine Learning to Diagnose Mammographically Detected Breast Cancer
- Lesson 5: What Value does AI Bring in the Surgical Profession?
- Lesson 6: Automated Analysis of Surgical Video
- Lesson 7: Explainable AI for Improving Surgical Performance and Safety
- Lesson 8: Ethical Considerations for Machine Learning
- Conclusion: The Future of Machine Learning for Surgeons
Disclaimer
PROTECTED INFORMATION
Copyright © 2023 American College of Surgeons
All rights reserved.
The educational materials for the course titled Artificial Intelligence and Machine Learning: Transforming Surgical Practice and Education offered by the American College of Surgeons, Division of Education, are intended for use only by the course participant. As a course participant, you agree not to forward, copy, distribute, or use the materials without authorization. Information in the course material is protected by the Copyright Act and Laws of the United States. Unauthorized forwarding, copying, distribution, or such use of this information is strictly prohibited, is unlawful, and may subject any person engaging in such activities to legal action, damages, and/or fines.
The information presented in this course is not verified or endorsed by the American College of Surgeons, which does not guarantee the accuracy, thoroughness, reliability, or quality of the presentations or the material, and it will not be responsible for any errors, inaccuracies, or omissions. The presenters of this course and the authors of the course material are solely responsible for the accuracy and completeness of the presentations and the material, including references. The American College of Surgeons will not be liable for any loss or damage caused by reliance on information obtained from the presentations or the material. The American College of Surgeons makes no representations or warranties, express or implied, regarding the presentations or content, which are provided “as is”.
Disclosure Information
In accordance with the ACCME Accreditation Criteria, the American College of Surgeons must ensure that anyone in a position to control the content of the educational activity (planners and speakers/authors/discussants/moderators) has disclosed all financial relationships with any commercial interest (termed by the ACCME as “ineligible companies”, defined below) held in the last 24 months (see below for definitions). Please note that first authors were required to collect and submit disclosure information on behalf all other authors/contributors, if applicable.
Ineligible company
The ACCME defines an “ineligible company” as any entity producing, marketing, re-selling, or distributing health care goods or services used on or consumed by patients. Providers of clinical services directly to patients are NOT included in this definition.
Financial Relationships
Relationships in which the individual benefits by receiving a salary, royalty, intellectual property rights, consulting fee, honoraria, ownership interest (e.g., stocks, stock options or other ownership interest, excluding diversified mutual funds), or other financial benefit. Financial benefits are usually associated with roles such as employment, management position, independent contractor (including contracted research), consulting, speaking and teaching, membership on advisory committees or review panels, board membership, and other activities from which remuneration is received, or expected. ACCME considers relationships of the person involved in the CME activity to include financial relationships of a spouse or partner.
Conflict of Interest
Circumstances create a conflict of interest when an individual has an opportunity to affect CME content about products or services of an ineligible company with which he/she has a financial relationship.
The ACCME also requires that ACS manage any reported conflict and eliminate the potential for bias during the educational activity. Any conflicts noted below have been managed to our satisfaction. The disclosure information is intended to identify any commercial relationships and allow learners to form their own judgments. However, if you perceive a bias during the educational activity, please report it on the evaluation.
Faculty and Disclosures
Daniel Buckland, MD, MS, PhD - Nothing to Disclose
Lawrence Carin, PhD - Nothing to Disclose
David Dov, PhD - Nothing to Disclose
Timothy W. Dunn, PhD - Nothing to Disclose
Nita Farahany, PhD, JD - Illumina: Ethics Consulting, Consulting Fee; Helix: Ethics Consulting, Consulting Fee
Teodor Grantcharov, MD, PhD, FACS - SST Inc: Founder, IP & Salary
Daniel Hashimoto, MD, FACS - Johnson & Johnson: Consulting, Consulting Fee, Verily Life Sciences: Consulting; Worrell: Consulting; Olympus Corporation: Sponsored Research Agreement, Research Support; McGraw Hill Education: Textbook Editor, Royalty Agreement
Ricardo Henao, PhD - Nothing to Disclose
Erich S. Huang, MD, PhD – kelaHealth: Stratus Medicine, Founder, Equity; Clinetic: Founder, Equity; Optimize Health: Advisor, Equity; Valo Health: Consulting; Consulting Fee
Eun Sil S. Hwang, MD, FACS - Nothing to Disclose
Allan D. Kirk, MD, PhD, FACS - Clinetic: Board Chair, Stock; CareDX: Consulting, FMV Consulting Fees; Sanofi: Consulting, FMV Consulting Fees; Eurofins: Consulting, FMV Consulting Fees; Vertex: Consulting, FMV Consulting Fees; Novartis: Consulting, FMV Consulting Fees
Carla M. Pugh, MD, PhD, FACS - Medical Teaching Systems: Founder, Personal Financial Investment; 10 Newtons, Inc: Founder, Personal Financial Investment; 10 Newtons Women’s Wellness Foundation: Founder, Personal Financial Investment; Intuitive Surgical Foundation: Consultant, Consultant Fee; PrecisionOS: Consultant, Consultant Fee
Danielle Elliott Range, MD - Nothing to Disclose
Guy Rosman, PhD - Toyota Research Institute: Senior Research Scientist, Salary; Von Bispharma: Senior Research Scientist, Salary; Olympus: Consultant, Consulting Fee
Frank Rudzicz, PhD - Surgical Safety Technologies Inc: Director of AI, Salary
Ajit K. Sachdeva, MD, FACS, FRCSC, FSACME, MAMSE - Nothing to Disclose
Steering Committee
Patrice Gabler Blair, DrPH, MPH – Nothing to Disclose
Allan D. Kirk, MD, PhD, FACS - Clinetic: Board Chair, Stock; CareDX: Consulting, FMV Consulting Fees; Sanofi: Consulting, FMV Consulting Fees; Eurofins: Consulting, FMV Consulting Fees; Vertex: Consulting, FMV Consulting Fees; Novartis: Consulting, FMV Consulting Fees
Ajit K. Sachdeva, MD, FACS, FRCSC, FSACME, MAMSE - Nothing to Disclose
Special Acknowledgement
The ACS Division of Education extends its gratitude and appreciation to the Duke University Department of Surgery and School of Medicine faculty and staff who shared their expertise and worked diligently to complete this program.
Continuing Medical Education Credit Information
Accreditation
The American College of Surgeons is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
AMA PRA Category 1 Credits™
The American College of Surgeons designates this enduring activity for a maximum of 4.50 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Successful completion of this CME activity, which includes participation in the evaluation component, enables the learner to earn credit toward the CME of the American Board of Surgery’s Continuous Certification program.
Release, Review, and Termination Dates
Release date: 4/3/2023
Termination date: 4/3/2026
CME Credit Claiming Information
In order to claim a CME Certificate or a Certificate of Completion, the following requirements will need to be completed:
- Review all course materials
- Course evaluation
Participants may only claim a maximum of 4.50 AMA PRA Category 1 Credits™ for this activity.
Available Credit
- 4.50 AMA PRA Category 1 Credit™
- 4.50 Certificate of Completion
Course Registration
You will be asked to register first to complete the registration process. Once you have completed the registration process, an email will be sent to you with the confirmation and course login information.
Course Fee:
- ACS Fellows - $150
- Nonmembers - $250
- ACS Resident members - $95
- Resident nonmembers - $145
To Start Course
Once you have completed the registration process, you will see the "Take Course" button above, click on the button to start/access the educational activity.