A nationwide survey on the perceptions of general surgeons on artificial intelligence

Frank J. Voskens , Julian R. Abbing , Anthony T. Ruys , Jelle P. Ruurda , Ivo A. M. J. Broeders

Artificial Intelligence Surgery ›› 2022, Vol. 2 ›› Issue (1) : 8 -17.

PDF
Artificial Intelligence Surgery ›› 2022, Vol. 2 ›› Issue (1) :8 -17. DOI: 10.20517/ais.2021.10
Original Article

A nationwide survey on the perceptions of general surgeons on artificial intelligence

Author information +
History +
PDF

Abstract

Aim: Artificial intelligence (AI) has the potential to improve perioperative diagnosis and decision making. Despite promising study results, the majority of AI platforms in surgery currently remain in the research setting. Understanding the current knowledge and general attitude of surgeons toward AI applications in their surgical practice is essential and can contribute to the future development and uptake of AI in surgery.

Methods: In March 2021, a web-based survey was conducted among members of the Dutch Association of Surgery. The survey measured opinions on the existing knowledge, expectations, and concerns on AI among surgical residents and surgeons.

Results: A total of 313 respondents completed the survey. Overall, 85% of the respondents agreed that AI could be of value in the surgical field and 61% expected AI to improve their diagnostic ability. The outpatient clinic (35.8%) and operating room (39.6%) were stated as area of interest for the use of AI. Statistically, surgeons working in an academic hospital were more likely to be aware of the possibilities of AI (P = 0.01). The surgeons in this survey were not worried about job replacement, however they raised the greatest concerns on accountability issues (50.5%), loss of autonomy (46.6%), and risk of bias (43.5%).

Conclusion: This survey demonstrates that the majority of the surgeons show a positive and open attitude towards AI. Although various ethical issues and concerns arise, the expectations regarding the implementation of future surgical AI applications are high.

Keywords

Artificial intelligence / surgery / survey

Cite this article

Download citation ▾
Frank J. Voskens, Julian R. Abbing, Anthony T. Ruys, Jelle P. Ruurda, Ivo A. M. J. Broeders. A nationwide survey on the perceptions of general surgeons on artificial intelligence. Artificial Intelligence Surgery, 2022, 2(1): 8-17 DOI:10.20517/ais.2021.10

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Bellman R. An introduction to artificial intelligence: can computers think? Available from: http://www.researchgate.net/publication/239053505_An_Introduction_to_Artificial_Intelligence--Can_Computers_Think [Last accessed on 30 Dec 2021]

[2]

Hashimoto DA,Rus D.Artificial intelligence in surgery: promises and perils.Ann Surg2018;268:70-6 PMCID:PMC5995666

[3]

LeCun Y,Hinton G.Deep learning.Nature2015;521:436-44

[4]

Gupta A,Guan L.Deep learning for object detection and scene perception in self-driving cars: survey, challenges, and open issues.Array2021;10:100057

[5]

Ayala Solares JR,Zhu Y.Deep learning for electronic health records: a comparative review of multiple deep neural architectures.J Biomed Inform2020;101:103337

[6]

Wang D,Gargeya R,Beck AH.Deep learning for identifying metastatic breast cancer.arXiv preprint arXiv2016;1606.05718

[7]

Yu KH,Kohane IS.Artificial intelligence in healthcare.Nat Biomed Eng2018;2:719-31

[8]

Mckinney SM,Godbole V.International evaluation of an AI system for breast cancer screening.Nature2020;577:89-94

[9]

Lakhani P.Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks.Radiology2017;284:574-82

[10]

Liu Y,Norouzi M.Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologists.Arch Pathol Lab Med2019;143:859-68

[11]

Coudray N,Sakellaropoulos T.Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.Nat Med2018;24:1559-67

[12]

Ting DSW,Peng L.Artificial intelligence and deep learning in ophthalmology.Br J Ophthalmol2019;103:167-75 PMCID:PMC6362807

[13]

Gulshan V,Coram M.Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA2016;316:2402-10

[14]

Haenssle HA,Schneiderbauer R.Reader study level-I and level-II GroupsMan against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.Ann Oncol2018;29:1836-42

[15]

Esteva A,Novoa RA.Dermatologist-level classification of skin cancer with deep neural networks.Nature2017;542:115-8 PMCID:PMC8382232

[16]

Mirnezami R.Surgery 3.0, artificial intelligence and the next-generation surgeon.Br J Surg2018;105:463-5

[17]

Zhou XY,Shen M.Application of artificial intelligence in surgery.Front Med2020;14:417-30

[18]

Esmaeilzadeh P.Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives.BMC Med Inform Decis Mak2020;20:170 PMCID:PMC7376886

[19]

Davenport T.The potential for artificial intelligence in healthcare.Future Healthc J2019;6:94-8 PMCID:PMC6616181

[20]

Drysdale E,Chivers C. Implementing AI in healthcare. Available from: http://www.erikdrysdale.com/figures/implementing-ai-in-healthcare.pdf [Last accessed on 30 Dec 2021]

[21]

Scheetz J,McGuinness M.A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology.Sci Rep2021;11:5193 PMCID:PMC7933437

[22]

van Hoek J,Leichtle A.A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over.Eur J Radiol2019;121:108742

[23]

European Society of Radiology (ESR). Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology.Insights Imaging2019;10:105

[24]

Shinners L,Grace S.Exploring healthcare professionals' perceptions of artificial intelligence: validating a questionnaire using the e-Delphi method.Digit Health2021;7:20552076211003433 PMCID:PMC7995296

[25]

Huisman M,Parker W.An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude.Eur Radiol2021;31:7058-66 PMCID:PMC8379099

[26]

Birkhoff DC,Schijven MP.A review on the current applications of artificial intelligence in the operating room.Surg Innov2021;28:611-9 PMCID:PMC8450995

[27]

Epstein NE.Legal and evidenced-based definitions of standard of care: Implications for code of ethics of professional medical societies.Surg Neurol Int2018;9:255 PMCID:PMC6322161

[28]

Price WN 2nd,Cohen IG.Potential liability for physicians using artificial intelligence.JAMA2019;322:1765-6

[29]

Froomkin AM,Pineau J.When AIs outperform doctors: confronting the challenges of a tort-induced over-reliance on machine learning.Ariz L Rev2019;61:33

[30]

Forcier MB,Mullan S.Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers?.J Law Biosci2019;6:317-35 PMCID:PMC6813940

[31]

Pesapane F,Codari M.Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States.Insights Imaging2018;9:745-53 PMCID:PMC6206380

[32]

Asan O,Choudhury A.Artificial intelligence and human trust in healthcare: focus on clinicians.J Med Internet Res2020;22:e15154 PMCID:PMC7334754

[33]

Sarwar S,Faust K.Physician perspectives on integration of artificial intelligence into diagnostic pathology.NPJ Digit Med2019;2:28 PMCID:PMC6550202

[34]

Kawka M,Fang C,Jiao LR.Intraoperative video analysis and machine learning models will change the future of surgical training.Intelligent Surgery2021;

[35]

Padoy N.Machine and deep learning for workflow recognition during surgery.Minim Invasive Ther Allied Technol2019;28:82-90

[36]

Luongo F,Nguyen JH,Hung AJ.Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery.Surgery2021;169:1240-4 PMCID:PMC7994208

[37]

Tokuyasu T,Matsunobu Y.Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy.Surg Endosc2021;35:1651-8 PMCID:PMC7940266

[38]

Madani A,Altieri MS.Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy.Ann Surg2020; PMCID:PMC8186165

[39]

Kelly CJ,Suleyman M,King D.Key challenges for delivering clinical impact with artificial intelligence.BMC Med2019;17:195 PMCID:PMC6821018

[40]

Oh S,Choi SW,Hong J.Physician confidence in artificial intelligence: an online mobile survey.J Med Internet Res2019;21:e12422 PMCID:PMC6452288

[41]

Wong K,Szumacher E.Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology - national survey.J Med Imaging Radiat Sci2021;52:44-8

[42]

Frey CB.The future of employment: how susceptible are jobs to computerisation?.Technol Forecast Soc Change2017;114:254-80

[43]

Krittanawong C.The rise of artificial intelligence and the uncertain future for physicians.Eur J Intern Med2018;48:e13-4

[44]

Staartjes VE,Kernbach JM.Machine learning in neurosurgery: a global survey.Acta Neurochir (Wien)2020;162:3081-91 PMCID:PMC7593280

PDF

62

Accesses

0

Citation

Detail

Sections
Recommended

/