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
References
[1]
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
[2]
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
[3]
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
[4]
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
[5]
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
[6]
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
[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
[8]
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
[9]
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
[10]
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
[11]
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
[12]
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
[13]
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
[14]
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
[15]
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
[16]
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
[17]
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.
[18]
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P. and Amodei, D. (2020) Language Models Are Few-Shot Learners.
[19]
Marcus, G. (2020) The Next Decade in AI: Four Steps towards Robust Artificial Intelligence.
[20]
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90.
[21]
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
[22]
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
[23]
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
[24]
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
[25]
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
[26]
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.
[27]
Au, K. and Yang, W. (2023) Auxiliary Use of ChatGPT in Surgical Diagnosis and Treatment. International Journal of Surgery, 109, 3940-3943.
[28]
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
[29]
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
