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 c hatbot 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.
KeywordsArtificial IntelligenceSurgeryImage RecognitionAutonomous Surgery
Kitaguchi, D., Takeshita, N., Matsuzaki, H., Igaki, T., Hasegawa, H., Kojima, S., Mori, K. and Ito, M. (2022) Real-Time Vascular Anatomical Image Navigation for Laparoscopic Surgery: Experimental Study. Surgical Endoscopy, 36, 6105-6112. https://doi.org/10.1007/s00464-022-09384-7
Bodenstedt, S., Wagner, M., Mayer, B., Stemmer, K., Kenngott, H., Müller-Stich, B., Dillmann, R. and Speidel, S. (2016) Image-Based Laparoscopic Bowel Measurement. The International Journal of Computer Assisted Radiology and Surgery, 11, 407-419. https://doi.org/10.1007/s11548-015-1291-1
Kitaguchi, D., Lee, Y., Hayashi, K., Nakajima, K., Kojima, S., Hasegawa, H., Takeshita, N., Mori, K. and Ito, M. (2022) Development and Validation of a Model for Laparoscopic Colorectal Surgical Instrument Recognition Using Convolutional Neural Network-Based Instance Segmentation and Videos of Laparoscopic Procedures. JAMA Network Open, 5, E2226265. https://doi.org/10.1001/jamanetworkopen.2022.26265
Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M. and Padoy, N. (2017) EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos. IEEE Transactions on Medical Imaging, 36, 86-97. https://doi.org/10.1109/TMI.2016.2593957
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
Shinozuka, K., Turuda, S., Fujinaga, A., Nakanuma, H., Kawamura, M., Matsunobu, Y., Tanaka, Y., Kamiyama, T., Ebe, K., Endo, Y., Etoh, T., Inomata, M. and Tokuyasu, T. (2022) Artificial Intelligence Software Available for Medical Devices: Surgical Phase Recognition in Laparoscopic Cholecystectomy. Surgical Endoscopy, 36, 7444-7452. https://doi.org/10.1007/s00464-022-09160-7
Massey, P.A., Montgomery, C. and Zhang, A.S. (2023) Comparison of ChatGPT-3.5, ChatGPT-4, and Orthopaedic Resident Performance on Orthopaedic Assessment Examinations. Journal of the American Academy of Orthopaedic Surgeons, 31, 1173-1179. https://doi.org/10.5435/JAAOS-D-23-00396
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M. and Elhadad, N. (2015) Intelligible Models for Healthcare: Predicting Pneumonia Risk and Hospital 30-Day Readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, 10-13 August 2015, 1721-1730. https://doi.org/10.1145/2783258.2788613
Cascella, M., Montomoli, J., Bellini, V. and Bignami, E. (2023) Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. Journal of Medical Systems, 47, Article No. 33. https://doi.org/10.1007/s10916-023-01925-4
Robinson, A. and Aggarwal Jr., S. (2023) When Precision Meets Penmanship: ChatGPT and Surgery Documentation. Cureus, 15, E40546. https://doi.org/10.7759/cureus.40546
Cheng, K.M., Sun, Z.J., He, Y.B., Gu, S.Q. and Wu, H.Y. (2023) The Potential Impact of ChatGPT/GPT-4 on Surgery: Will It Topple the Profession of Surgeons? International Journal of Surgery, 109, 1545-1547. https://doi.org/10.1097/JS9.0000000000000388
Rafaqat, W., Chu, D.I. and Kaafarani, H.M. (2023) AI and ChatGPT Meet Surgery: A Word of Caution for Surgeon-Scientists. Annals of Surgery, 278, e943-e944. https://doi.org/10.1097/SLA.0000000000006000
Li, S. (2023) ChatGPT Has Made the Field of Surgery Full of Opportunities and Challenges. International Journal of Surgery, 109, 2537-2538. https://doi.org/10.1097/JS9.0000000000000454
Srivastav, S., Chandrakar, R., Gupta, S., Babhulkar, V., Agrawal, S., Jaiswal, A., Prasad, R. and Wanjari, M.B. (2023) ChatGPT in Radiology: The Advantages and Limitations of Artificial Intelligence for Medical Imaging Diagnosis. Cureus, 15, E41435. https://doi.org/10.7759/cureus.41435
Li, W.B., Zhang, Y.X. and Chen, F.M. (2023) ChatGPT in Colorectal Surgery: A Promising Tool or a Passing Fad? Annals of Biomedical Engineering, 51, 1892-1897. https://doi.org/10.1007/s10439-023-03232-y
Chavez, M.R. (2023) ChatGPT: The Good, the Bad, and the Potential. American Journal of Obstetrics & Gynecology, 229, 357. https://doi.org/10.1016/j.ajog.2023.04.005
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N. and Polosukhin, I. (2017) Attention Is All You Need. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, 4-9 December 2017, 5998-6008.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P. and Amodei, D. (2020) Language Models Are Few-Shot Learners.
Marcus, G. (2020) The Next Decade in AI: Four Steps towards Robust Artificial Intelligence.
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90.
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M. and Sánchez, C.I. (2017) A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005
He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 770-778. https://doi.org/10.1109/CVPR.2016.90
Gruijthuijsen, C., Garcia-Peraza-Herrera, L.C., Borghesan, G., Reynaerts, D., Deprest, J., Ourselin, S., Vercauteren, T. and Vander, Poorten, E. (2022) Robotic Endoscope Control via Autonomous Instrument Tracking. Frontiers in Robotics and AI, 9, Article ID: 832208. https://doi.org/10.3389/frobt.2022.832208
Zhang, J. and Gao, X. (2020) Object Extraction via Deep Learning-Based Marker-Free Tracking Framework of Surgical Instruments for Laparoscope-Holder Robots. The International Journal of Computer Assisted Radiology and Surgery, 15, 1335-1345. https://doi.org/10.1007/s11548-020-02214-y
Li, W., Chiu, P.W.Y. and Li, Z. (2020) An Accelerated Finite-Time Convergent Neural Network for Visual Servoing of a Flexible Surgical Endoscope with Physical and RCM Constraints. The IEEE Transactions on Neural Networks and Learning Systems, 31, 5272-5284. https://doi.org/10.1109/TNNLS.2020.2965553
Kavian, J.A., Wilkey, H.L., Patel, P.A. and Boyd, C.J. (2023) Harvesting the Power of Artificial Intelligence for Surgery: Uses, Implications, and Ethical Considerations. The American Surgeon, 89, 5102-5104.
Au, K. and Yang, W. (2023) Auxiliary Use of ChatGPT in Surgical Diagnosis and Treatment. International Journal of Surgery, 109, 3940-3943.
De Angelis, L., Baglivo, F., Arzilli, G., Privitera, G.P., Ferragina, P., Tozzi, A.E. and Rizzo, C. (2023) ChatGPT and the Rise of Large Language Models: The New AI-Driven Infodemic Threat in Public Health. Frontiers in Public Health, 11, Article ID: 1166120. https://doi.org/10.3389/fpubh.2023.1166120
Hassan, A.M., Nelson, J.A., Coert, J.H., Mehrara, B.J. and Selber, J.C. (2023) Exploring the Potential of Artificial Intelligence in Surgery: Insights from a Conversation with ChatGPT. Annals of Surgical Oncology, 30, 3875-3878. https://doi.org/10.1245/s10434-023-13347-0