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Kitaguchi, D., Takeshita, N., Matsuzaki, H., Takano, H., Owada, Y., Enomoto, T., Oda, T., Miura, H., Yamanashi, T., Watanabe, M., Sato, D., Sugomori, Y., Hara, S. and Ito, M. (2020) Real-Time Automatic Surgical Phase Recognition in Laparoscopic Sigmoidectomy Using the Convolutional Neural Network-Based Deep Learning Approach. Surgical Endoscopy, 34, 4924-4931.
https://doi.org/10.1007/s00464-019-07281-0

has been cited by the following article:

• TITLE: Artificial Intelligence and Computer Vision during Surgery: Discussing Laparoscopic Images with ChatGPT4—Preliminary Results

  AUTHORS: Savvas Hirides, Petros Hirides, Kouloufakou Kalliopi, Constantinos Hirides

  KEYWORDS: Artificial Intelligence, Surgery, Image Recognition, Autonomous Surgery

  JOURNAL NAME: Surgical Science, Vol.15 No.3, March 29, 2024

  ABSTRACT: Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come.