White paper: ethics and trustworthiness of artificial intelligence in clinical surgery

Abstract

This white paper documents the consensus opinion of the Artificial Intelligence Surgery (AIS) task force on Artificial Intelligence (AI) Ethics and the AIS Editorial Board Study Group on Ethics on the ethical considerations and current trustworthiness of artificial intelligence and autonomous actions in surgery. The ethics were divided into 6 topics defined by the Task Force: Reliability of robotic and AI systems; Respect for privacy and sensitive data; Use of complete and representative (i.e., unbiased) data; Transparencies and uncertainties in AI; Fairness: are we exacerbating inequalities in access to healthcare?; Technology as an equalizer in surgical education. Task Force members were asked to research a topic, draft a section, and come up with several potential consensus statements. These were voted on by members of the Task Force and the Study Group, and all proposals that received > 75 % agreement were adopted and included in the White Paper.

Keywords

Artificial intelligence surgery / ethics of artificial intelligence / consensus / white paper

References

[1] Ethics guidelines for trustworthy AI. Available from: https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai. [Last accessed on 15 May 2023]

[2] Top 9 ethical issues in artificial intelligence. Available from: https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/. [Last accessed on 15 May 2023]

[3] Bostrom N. Ethical issues in advanced artificial intelligence. In: Routledge, ed. Machine Ethics and Robot Ethics 2017. Available from: https://www.taylorfrancis.com/chapters/edit/10.4324/9781003074991-7/ethical-issues-advanced-artificial-intelligence-nick-bostrom. [Last accessed on 15 May 2023]

[4] Gumbs AA,Karcz K.White paper: definitions of artificial intelligence and autonomous actions in clinical surgery.Art Int Surg2022;2:93-100

[5] Kessel R.British medical association: 1988, philosophy & practice of medical ethics, B.M. A., London, 94 pp. plus appendices, etc., 9.50 (paper).J Med Philos1989;14:709-10

[6] Holm S. Principles of biomedical ethics, 5th edn. Beauchamp T L, Childress J F. Oxford University Press, 2001, £19.95, pp 454. ISBN 0-19-514332-9. 2002;28:332-332. Available from: https://jme.bmj.com/content/28/5/332.2. [Last accessed on 15 May 2023]

[7] Regulation (EU) 2017/745 of the european parliament and of the council of 5 April 2017. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0745. [Last accessed on 15 May 2023]

[8] Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52021PC0206. [Last accessed on 15 May 2023]

[9] Statement from FDA commissioner scott gottlieb, M.D. on steps toward a new, tailored review framework for artificial intelligence-based medical devices. Available from: https://www.fda.gov/news-events/press-announcements/statement-fda-commissioner-scott-gottlieb-md-steps-toward-new-tailored-review-framework-artificial. [Last accessed on 15 May 2023]

[10] McCulloch P,Campbell WB.Balliol CollaborationNo surgical innovation without evaluation: the IDEAL recommendations.Lancet2009;374:1105-12

[11] Amin F,Mateen A,Khan S.A step toward next-generation advancements in the internet of things technologies.Sensors2022;22:8072 PMCID:PMC9609757

[12] Complete guide to GDPR compliance. Available from: https://gdpr.eu/. [Last accessed on 15 May 2023]

[13] HIPAA & your health rights. Available from: https://www.hhs.gov/programs/hipaa/index.html. [Last accessed on 15 May 2023]

[14] AI ethics (AI code of ethics). Available from: https://www.techtarget.com/whatis/definition/AI-code-of-ethics. [Last accessed on 15 May 2023]

[15] Unethical use of artificial intelligence. Available from: https://lasserouhiainen.com/unethical-use-of-artificial-intelligence/. [Last accessed on 15 May 2023]

[16] Bias in AI and machine learning: some recent examples (OR cases in point). Available from: https://www.lexalytics.com/blog/bias-in-ai-machine-learning/. [Last accessed on 15 May 2023]

[17] Should AI be fair and non-discriminative. Available from: https://ethics-of-ai.mooc.fi/chapter-6/3-discrimination-and-biases. [Last accessed on 15 May 2023]

[18] 3 essential steps for AI ethics. Available from: https://www.sas.com/en_us/insights/articles/analytics/artificial-intelligence-ethics.html. [Last accessed on 15 May 2023]

[19] What are the risks of artificial intelligence? Available from: https://www.linkedin.com/pulse/what-risks-artificial-intelligence-philip-mckeown. [Last accessed on 15 May 2023]

[20] Top 7 challenges in artificial intelligence in 2023. Available from: https://www.upgrad.com/blog/top-challenges-in-artificial-intelligence/. [Last accessed on 15 May 2023]

[21] Is the way you think biased? Here are 3 common mistakes to watch out for. Available from: https://www.weforum.org/agenda/2022/04/cognitive-biases-three-common-types-illustrated/. [Last accessed on 15 May 2023]

[22] Sparrow R. The promise and perils of AI in medicine. Available from: https://www.researchgate.net/publication/344879321_The_promise_and_perils_of_AI_in_medicine. [Last accessed on 15 May 2023]

[23] Amann J,Vayena E,Madai VI.Precise4Q consortiumExplainability for artificial intelligence in healthcare: a multidisciplinary perspective.BMC Med Inform Decis Mak2020;20:310 PMCID:PMC7706019

[24] Rashidian N.Applications of machine learning in surgery: ethical considerations.Art Int Surg2022;2:18-23

[25] Gumbs AA,d’Allemagne B.What is artificial intelligence surgery?.Art Int Surg2021;1:1-10

[26] Boutros C,Ocuin L,Hashimoto DA.Artificial intelligence in hepatopancreaticobiliary surgery - promises and perils.Art Int Surg2022;2:213-23

[27] European Commission. The 2030 agenda for sustainable development and SDGs. Available from: https://commission.europa.eu/strategy-and-policy/international-strategies/sustainable-development-goals/eu-and-united-nations-common-goals-sustainable-future_en. [Last accessed on 18 May 2023]

[28] Stypińska J.AI revolution in healthcare and medicine and the (re-)emergence of inequalities and disadvantages for ageing population.Front Sociol2022;7:1038854 PMCID:PMC9899925

[29] d’Elia A,Rodgers S.Artificial intelligence and health inequities in primary care: a systematic scoping review and framework.Fam Med Community Health2022;10:e001670 PMCID:PMC9716837

[30] Gupta M,Dennehy D.Questioning racial and gender bias in AI-based recommendations: do espoused national cultural values matter?.Inf Syst Front2022;24:1465-81 PMCID:PMC8214712

[31] Elwyn G,Hager A.Coproduction: when users define quality.BMJ Qual Saf2020;29:711-6 PMCID:PMC7467503

[32] Kerasidou A.Ethics of artificial intelligence in global health: Explainability, algorithmic bias and trust.J Oral Biol Craniofac Res2021;11:612-4 PMCID:PMC8449079

[33] Wahl B,Germann S.Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?.BMJ Glob Health2018;3:e000798. PMCID:PMC6135465

[34] Cobianchi L,Loftus TJ.Artificial intelligence and surgery: ethical dilemmas and open issues.J Am Coll Surg2022;235:268-75

[35] Myers SP,Brown JB.Effects of gender bias and stereotypes in surgical training: a randomized clinical trial.JAMA Surg2020;155:552-60 PMCID:PMC7240638

[36] Yuce TK,Glass C.National evaluation of racial/ethnic discrimination in US surgical residency programs.JAMA Surg2020;155:526-8 PMCID:PMC7160741

[37] Ferrari L,De Santi G.Early barriers to career progression of women in surgery and solutions to improve them: a systematic scoping review.Ann Surg2022;276:246-55

[38] Karnick A,Bagautdinov I.Can general surgery interns accurately measure their own technical skills? Analysis of cognitive bias in surgical residents' self-assessments.Surgery2021;170:1353-8

[39] Dill-Macky A,Neumayer LA,Turner AP.The role of implicit bias in surgical resident evaluations.J Surg Educ2022;79:761-8

[40] Voss M,Abel L.Capacity-building partnerships for surgical post-graduate training in low- and middle-income countries: a scoping review of the literature with exploratory thematic synthesis.Health Policy Plan2021;35:1385-412

[41] Mirchi N,Yilmaz R,Winkler-Schwartz A.The virtual operative assistant: an explainable artificial intelligence tool for simulation-based training in surgery and medicine.PLoS One2020;15:e0229596 PMCID:PMC7046231

[42] Anvari M,Barlow K.From telementorship to automation.J Surg Oncol2021;124:246-9

[43] Reiley CE,Yuh DD.Review of methods for objective surgical skill evaluation.Surg Endosc2011;25:356-66

[44] Madani A,Altieri MS.Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy.Ann Surg2022;276:363-9 PMCID:PMC8186165

[45] Asghar MS,Zahid A.Past, present, and future of surgical simulation and perspective of a developing country: A narrative review.J Pak Med Assoc2021;71:2770-6

[46] Martinerie L,Ronot M.Health care simulation in developing countries and low-resource situations.J Contin Educ Health Prof2018;38:205-12

[47] Rangel EL,Castillo-Angeles M.Pregnancy and motherhood during surgical training.JAMA Surg2018;153:644-52 PMCID:PMC5875346

[48] Bissonnette V,Ledwos N,Winkler-Schwartz A,Neurosurgical Simulation & artificial intelligence learning centre .Artificial intelligence distinguishes surgical training levels in a virtual reality spinal task.J Bone Joint Surg Am2019;101:e127 PMCID:PMC7406145

[49] Hasan O,Jessar M,Hashmi P.The need for simulation in surgical education in developing countries. The wind of change. Review article.J Pak Med Assoc69:S62-s68.