Artificial Intelligence in Surgery. Understanding the Role of AI in Surgical Practice

Section 1: An Introduction to Artificial Intelligence1. A Brief History of AI - Daniel A. Hashimoto, MD MS (Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital | Harvard Medical School) and Guy Rosman, PhD (Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology)2. Large Databases in Surgery - Gabriel Brat, MD MPH (Department of Surgery, Beth Israel Deaconess Medical Center | Department of Bioinformatics, Harvard Medical School))3. Major Subfields of AI  i. Machine Learning and Medicine - John Guttag, PhD (Computer Science and Artificial Intelligence Laboratory, MIT) ii. Neural Networks and Deep Learning - Synho Do, PhD (Laboratory of Medical Imaging and Computation, Massachusetts General Hospital) iii. Natural Language Processing - Dr. Regina Barzilay, PhD (Computer Science and Artificial Intelligence Laboratory, MIT) iv. Computer Vision - Polina Golland, PhD (Computer Science and Artificial Intelligence Laboratory, MIT)4. Limitations of AI - Daniel A. Hashimoto, MD MS and Ozanan Meireles, MD (Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital | Harvard Medical School) 
Section 2 - Applications of AI in Surgery5. AI for Surgical Education and Simulation - Ganesh Sankaranarayanan, PhD (Center for Evidence-based Simulation, Baylor University)6. Preoperative Risk Stratification - Haytham Kaafarani, MD (Department of Surgery, MGH) and Dimitris Bertsimas, PhD (Operations Research Center, MIT)7. Presurgical Planning with Machine Learning - Omar Arnaout, MD (Department of Neurosurgery, Brigham & Women's Hospital)8. Intraoperative Video Analysis - Daniel Hashimoto, MD MS, Guy Rosman, PhD, and Ozanan Meireles, MD (Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital | Harvard Medical School)9. The OR Black Box and Tracking of Intraoperative Events - Teodor Grantcharov, MD PhD (Department of Surgery, University of Toronto)10. Natural Language Processing for Clinical Documentation - David Ting, MD (Chief Medical Information Officer, Massachusetts General Physicians Organization)11. Leveraging Artificial Intelligence in the EMR - David Sontag, PhD (Institute for Medical Engineering and Science, MIT)12. Prediction and Prevention of Postoperative Infections - Erica Shenoy, MD PhD (Infection Control Unit, MGH) 
Section 3 - Societal and Policy Implications of AI in Surgery13. Ethics of Artificial Intelligence in Surgery - Danton Char, MD (Department of Anesthesiology, Stanford University) and David Magnus, PhD (Stanford Center for Biomedical Ethics, Stanford University)14. Machine Learning and Health Policy - Sherri Rose, PhD (Department of Healthcare Policy, Harvard Medical School)
Section 4 - Using AI for Surgical Research15. Machine Learning: Choosing the Right Approach - Manisha Desai, PhD (Department of Biomedical Data Science, Stanford University)16. Utilizing Computer Vision - Elan Witkowski, MD MPH (Department of Surgery, MGH) and Synho Do, PhD (Laboratory of Medical Imaging and Computation, Massachusetts General Hospital)17. Assessment of AI Research - Ziad Obermeyer, MD MPhil (Department of Emergency Medicine, Brigham and Women's Hospital)
Section 5 - The Future of Surgery18. The Collective Surgical Consciousness - Daniel Hashimoto, MD MS, Guy Rosman, PhD, and Ozanan Meireles, MD (Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital | Harvard Medical School)
AppendicesAppendix I - AI Quick Referencei. Table of all the AI techniques discussed throughout the book that includes a brief summary of the technique, examples of use cases, advantages, and limitationsAppendix II - Glossary of termsii. Glossary of key terms in artificial intelligence used throughout the textAppendix III - Additional Resourcesiii. A listing of additional resources such as additional books in AI, online courses, conferences, etc. where readers can expand their knowledge after reading this book

The first surgical AI guide of its kind! Machine learning, neural networks, and computer vision for surgical education, practice, and research.

While radiology and pathology are on the leading edge of AI in healthcare, surgery is showing tremendous potential for disruption by AI. Written for anyone not steeped in mathematics, technology, or engineering, this matchless guide gets ahead of the knowledge curve now—so you can evaluate new technologies with a critical eye and make informed decisions about bringing AI into your practice.

Artificial Intelligence in Surgery covers the history, principles, and main subfields of AI, offering examples of current and near-future use cases for AI in surgery. It gives you a clear understanding of the ethical implications of AI, its potential impact on healthcare policy, and how to read and interpret papers that use AI. The appendix includes a quick reference on AI techniques, their use cases, strengths, and limitations; glossary of terms; important learning resources; and techniques (including examples of appropriate use cases, advantages, and limitations)—all of which can be used to interpret claims made by studies or companies using AI.