Response: Artificial Intelligence in Surgery Requires Interdisciplinary Collaboration and Understanding.

1. Hashimoto DA, Rosman G, Rus D, et al. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268:70–76. 2. Feng R, Leung CS, Constantinides AG, et al. Lagrange programming neural network for nondifferentiable optimization problems in sparse approximation. IEEE Trans Neural Netw Learn Syst. 2017;28:2395–2407. 3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444. 4. Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA. 2017;318:517–518. 5. Faris O, Shuren J. An FDA viewpoint on unique considerations for medical-device clinical trials. N Engl J Med. 2017;376:1350–1357.

[1]  G. Rosman,et al.  Artificial Intelligence in Surgery: Promises and Perils , 2018, Annals of surgery.

[2]  Guy Rosman,et al.  Machine learning and coresets for automated real-time video segmentation of laparoscopic and robot-assisted surgery , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[3]  K. Hasegawa,et al.  Low Platelet Counts and Prolonged Prothrombin Time Early After Operation Predict the 90 Days Morbidity and Mortality in Living-donor Liver Transplantation. , 2016, Annals of surgery.

[4]  Thomas Neumuth,et al.  Multi-perspective workflow modeling for online surgical situation models , 2015, J. Biomed. Informatics.

[5]  Armand Joulin,et al.  Deep Fragment Embeddings for Bidirectional Image Sentence Mapping , 2014, NIPS.

[6]  N. Mueller,et al.  Low platelet counts after liver transplantation predict early posttransplant survival: The 60‐5 criterion , 2014, Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society.

[7]  Germain Forestier,et al.  Classification of surgical processes using dynamic time warping , 2012, J. Biomed. Informatics.

[8]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[9]  M. Nijsten,et al.  Immediate Postoperative Low Platelet Count is Associated With Delayed Liver Function Recovery After Partial Liver Resection , 2010, Annals of surgery.

[10]  Max Q.-H. Meng,et al.  Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images , 2009, IEEE Transactions on Biomedical Engineering.

[11]  J. Belghiti,et al.  Prospective Validation of the “Fifty-Fifty” Criteria as an Early and Accurate Predictor of Death After Liver Resection in Intensive Care Unit Patients , 2009, Annals of surgery.

[12]  O. Farges,et al.  The “50-50 Criteria” on Postoperative Day 5: An Accurate Predictor of Liver Failure and Death After Hepatectomy , 2005, Annals of surgery.