Document Type : Original Article
Authors
- . Amit Gupta
- . Tanuj Singla
- . Jaine John Chennatt
- . Lena Elizabath David
- . Shaik Sameer Ahmed
- . Deepak Rajput
Abstract
Artificial intelligence (AI) is the future of surgery. Technological advancements are taking place at
an incredible pace, largely due to AI or AI‑backed systems. It is likely that there will be a massive
explosion or “Cambrian explosion” of AI in our everyday life, largely aided by increased funding
and resources spent on research and development. AI has also significantly revolutionized the
medical field. The concept of machine learning and deep learning in AI is the crux of its success. In
surgical practice, AI has numerous applications in the diagnosis of disease, preoperative planning,
intraoperative assistance, surgical training and assessment, and robotics. The potential automation
of surgery is also a possibility in the next few decades. However, at present, augmentation rather
than automation should be the priority. In spite of the allure of AI, it comes with its own price. A robot
lacks the “sixth sense” or intuition that is crucial in the practice of surgery and medicine. Empathy
and human touch are also inimitable characteristics that cannot be replaced by an AI system. Other
limitations include the financial burden and the feasibility of using such technology on a wide scale.
Ethical and legal dilemmas such as those involving privacy laws are other issues that should be
taken under consideration. Despite all these limitations, with the way technology is progressing, it
is inevitable that AI and automation will completely change the way we practice surgery in the near
future. Thus, this narrative review article aims to highlight the various applications and pitfalls of AI
in the field of surgery.
Keywords
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Glob Health J 2020;4:42‑5.
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anastomosis robot (STAR): A vision‑guided robotics system for
laparoscopic suturing. IEEE Trans Biomed Eng 2014;61:1305‑17.
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et al. Waterjet Ablation Therapy for Endoscopic Resection
of prostate tissue trial (WATER) vs WATER II: Comparing
Aquablation therapy for benign prostatic hyperplasia in 30‑80
and 80‑150 mL prostates. BJU Int 2020;125:112‑22.
29. BhojaniN, BidairM, ZornKC, TrainerA, ArtherA, KramolowskyE,
et al. Aquablation for benign prostatic hyperplasia in large
prostates (80‑150 cc): 1‑year results. Urology 2019;129:1‑7.
30. Fagogenis G, Mencattelli M, Machaidze Z, Rosa B, Price K, Wu F,
et al. Autonomous robotic intracardiac catheter navigation using
haptic vision. Sci Robot 2019;4:eaaw1977.
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weforum.org/agenda/2020/08/scientists‑star‑wars‑artificial
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32. Jiang L, Wu Z, Xu X, Zhan Y, Jin X, Wang L, et al. Opportunities
and challenges of artificial intelligence in the medical field:
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strategies. J Int Med Res 2021;49 (3):3000605211000157.
33. DeCamp M, Tilburt JC. Why we cannot trust artificial intelligence
in medicine. Lancet Digit Health 2019;1:e390.
editor. Alan Turing: Life and Legacy of a Great Thinker. Berlin,
Heidelberg: Springer; 2004.
2. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial
intelligence in surgery: Promises and perils. Ann Surg
2018;268:70‑6.
3. Bini SA. Artificial intelligence, machine learning, deep learning,
and cognitive computing: What do these terms mean and how
will they impact health care? J Arthroplasty 2018;33:2358‑61.
4. Haeberle HS, Helm JM, Navarro SM, Karnuta JM, Schaffer JL,
Callaghan JJ, et al. Artificial intelligence and machine learning
in lower extremity arthroplasty: A Review. J Arthroplasty
2019;34:2201‑3.
5. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al.
CheXNet: Radiologist‑Level Pneumonia Detection on Chest
X‑Rays with Deep Learning. ArXiv171105225 Cs Stat; 2017 Dec 25.
Available form: http://arxiv.org/abs/1711.05225. [Last accessed
on 2021 Mar 30].
6. Castellanos‑Ortega A, Suberviola B, García‑Astudillo LA,
Holanda MS, Ortiz F, Llorca J, et al. Impact of the surviving sepsis
campaign protocols on hospital length of stay and mortality
in septic shock patients: Results of a three‑year follow‑up
quasi‑experimental study. Crit Care Med 2010;38:1036‑43.
7. Henry KE, Hager DN, Pronovost PJ, Saria S. A targeted real‑time
early warning score (TREWScore) for septic shock. Sci Transl Med
2015;7:299ra122.
8. Kundu S, Ashinsky BG, Bouhrara M, Dam EB, Demehri S,
Shifat‑E‑Rabbi M, et al. Enabling early detection of osteoarthritis
from presymptomatic cartilage texture maps via transport‑based
learning. Proc Natl Acad Sci U S A 2020;117:24709‑19.
9. Ruiz‑Fernández D, Monsalve Torra A, Soriano‑Payá A,
Marín‑Alonso O, Triana Palencia E. Aid decision algorithms to
estimate the risk in congenital heart surgery. Comput Methods
Programs Biomed 2016;126:118‑27.
10. Saeyeldin A, Zafar MA, Li Y, Tanweer M, Abdelbaky M,
Gryaznov A, et al. Decision‑making algorithm for ascending
aortic aneurysm: Effectiveness in clinical application? J Thorac
Cardiovasc Surg 2019;157:1733‑45.
11. Rezapour A, Hosseinijebeli SS, Faradonbeh SB. Economic
evaluation of E‑health interventions compared with alternative
treatments in older persons’ care: A systematic review. J Educ
Health Promot 2021;10:134.
12. Saji JA, Babu BP, Sebastian SR. Social influence of COVID‑19:
An observational study on the social impact of post‑COVID‑19
lockdown on everyday life in Kerala from a community
perspective. J Educ Health Promot 2020;9:360.
13. Mirnezami R, Ahmed A. Surgery 3.0, artificial intelligence and
the next‑generation surgeon. Br J Surg 2018;105:463‑5.
14. Somashekhar SP, Sepúlveda MJ, Puglielli S, Norden AD,
Shortliffe EH, Rohit Kumar C, et al. Watson for oncology and
breast cancer treatment recommendations: Agreement with an
expert multidisciplinary tumor board. Ann Oncol 2018;29:418‑23.
15. Simon G, DiNardo CD, Takahashi K, Cascone T, Powers C,
Stevens R, et al. Applying artificial intelligence to address the
knowledge gaps in cancer care. Oncologist 2019;24:772‑82.
16. Recognizing Ureter and Uterine Artery in Endoscopic Images
Using a Convolutional Neural Network. Available from:
https://www.computer.org/csdl/proceedings‑article/
cbms/2017/1710a726/12OmNAkWvFp. [Last accessed on 2021
Mar 25].
17. Hashimoto DA, Rosman G, Witkowski ER, Stafford C,
Navarette‑Welton AJ, Rattner DW, et al. Computer vision analysis
of intraoperative video: Automated recognition of operative steps
in laparoscopic sleeve gastrectomy. Ann Surg 2019;270:414‑21.
18. André B, Vercauteren T, Perchant A, Buchner AM, Wallace MB,
AyacheN. Endomicroscopic image retrieval and classification using
invariant visual features. In: Proceedings – 2009 IEEE International
Symposium on Biomedical Imaging: From Nano to Macro. Boston,
Massachusetts ISBI; 2009. Available from: https://mayoclinic.pure.
elsevier.com/en/publications /endomicroscopic‑image‑retrieval‑
and‑classification‑using‑invarian. [Last accessed on 2021 Mar 18].
19. Tian S, Yin XC, Wang ZB, Zhou F, Hao HW. A VidEo‑based
intelligent recognition and decision system for the
phacoemulsification cataract surgery. Comput Math Methods
Med 2015;2015:202934.
20. Li Y, Charalampaki P, Liu Y, Yang GZ, Giannarou S. Context
aware decision support in neurosurgical oncology based on an
efficient classification of endomicroscopic data. Int J Comput
Assist Radiol Surg 2018;13:1187‑99.
21. Hou F, Yang Z, Gu W, Yu Y, Liang Y. Automatic identification
of metastatic lymph nodes in OCT images. In: Optical Coherence
Tomography and Coherence Domain Optical Methods in
Biomedicine XXIII. SPIE BiOS, 2019, San Francisco, California,
United States International Society for Optics and Photonics; 2019.
p. 108673G. Available from: https://www.spiedigitallibrary.
org/conference‑proceedings‑of‑spie/10867/108673G/
Automatic‑identification ‑of‑metastatic‑lymph‑nodes
‑in‑OCT‑images/10.1117/12.2511588.short. [Last accessed on
2021 Mar 16].
22. Hung AJ, Chen J, Gill IS. Automated performance metrics and
machine learning algorithms to measure surgeon performance
and anticipate clinical outcomes in robotic surgery. JAMA Surg
2018;153:770‑1.
23. Predicting Surgical Skill from the First N Seconds of a
Task: Value Over Task Time using the Isogony Principle.
Available from: https://www.springerprofessional.de/
en/predicting‑surgical‑skill‑from‑the‑first‑n‑seconds
‑of‑a‑task‑val/12294456. [Last accessed on 2021 Mar 16].
24. Mohammadi G, Tourdeh M, Ebrahimian A. Effect of
simulation‑based training method on the psychological health
promotion in operating room students during the educational
internship. J Educ Health Promot 2019;8:172.
25. Hardcastle T, Wood A. The utility of virtual reality surgical
simulation in the undergraduate otorhinolaryngology curriculum.
J Laryngol Otol 2018;132:1072‑6.
26. Liu R, Rong Y, Peng Z. A review of medical artificial intelligence.
Glob Health J 2020;4:42‑5.
27. Leonard S, Wu KL, Kim Y, Krieger A, Kim PC. Smart tissue
anastomosis robot (STAR): A vision‑guided robotics system for
laparoscopic suturing. IEEE Trans Biomed Eng 2014;61:1305‑17.
28. Nguyen DD, Barber N, Bidair M, Gilling P, Anderson P, Zorn KC,
et al. Waterjet Ablation Therapy for Endoscopic Resection
of prostate tissue trial (WATER) vs WATER II: Comparing
Aquablation therapy for benign prostatic hyperplasia in 30‑80
and 80‑150 mL prostates. BJU Int 2020;125:112‑22.
29. BhojaniN, BidairM, ZornKC, TrainerA, ArtherA, KramolowskyE,
et al. Aquablation for benign prostatic hyperplasia in large
prostates (80‑150 cc): 1‑year results. Urology 2019;129:1‑7.
30. Fagogenis G, Mencattelli M, Machaidze Z, Rosa B, Price K, Wu F,
et al. Autonomous robotic intracardiac catheter navigation using
haptic vision. Sci Robot 2019;4:eaaw1977.
31. Scientists have Created a Revolutionary Artificial Skin.
World Economic Forum. Available from: https://www.
weforum.org/agenda/2020/08/scientists‑star‑wars‑artificial
‑skin‑accessibility‑feel‑touch/. [Last accessed on 2021 Mar 10].
32. Jiang L, Wu Z, Xu X, Zhan Y, Jin X, Wang L, et al. Opportunities
and challenges of artificial intelligence in the medical field:
Current application, emerging problems, and problem‑solving
strategies. J Int Med Res 2021;49 (3):3000605211000157.
33. DeCamp M, Tilburt JC. Why we cannot trust artificial intelligence
in medicine. Lancet Digit Health 2019;1:e390.
)