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Section 1: An Introduction to Artificial Intelligence
1. 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 Surgery
5. 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 Surgery
13. 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 Research
15. 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 Surgery
18. 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)
Appendices
Appendix I - AI Quick Reference
i. Table of all the AI techniques discussed throughout the book that includes a brief summary of the technique, examples of use cases, advantages, and limitations
Appendix II - Glossary of terms
ii. Glossary of key terms in artificial intelligence used throughout the text
Appendix III - Additional Resources
iii. 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
