CONTENTS :

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