Ethics of Artificial Intelligence in Embryo Assessment: Mapping the Terrain

Overview

Journal Hum Reprod

Publisher Oxford University Press

Specialty Reproductive Medicine

Date 2024 Dec 10

PMID 39657965

Authors

Julian J Koplin

Molly Johnston

Amy N S Webb

Andrea Whittaker

Catherine Mills

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) has the potential to standardize and automate important aspects of fertility treatment, improving clinical outcomes. One promising application of AI in the fertility clinic is the use of machine learning (ML) tools to assess embryos for transfer. The successful clinical implementation of these tools in ways that do not erode consumer trust requires an awareness of the ethical issues that these technologies raise, and the development of strategies to manage any ethical concerns. However, to date, there has been little published literature on the ethics of using ML in embryo assessment. This mini-review contributes to this nascent area of discussion by surveying the key ethical concerns raised by ML technologies in healthcare and medicine more generally, and identifying which are germane to the use of ML in the assessment of embryos. We report concerns about the 'dehumanization' of human reproduction, algorithmic bias, responsibility, transparency and explainability, deskilling, and justice.

References

Illingworth P, Venetis C, Gardner D, Nelson S, Berntsen J, Larman M . Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial. Nat Med. 2024; 30(11):3114-3120. PMC: 11564097. DOI: 10.1038/s41591-024-03166-5. View

Luong T, Le N . Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine. J Assist Reprod Genet. 2023; 41(2):239-252. PMC: 10894798. DOI: 10.1007/s10815-023-02973-y. View

Ploug T, Holm S . The right to refuse diagnostics and treatment planning by artificial intelligence. Med Health Care Philos. 2019; 23(1):107-114. DOI: 10.1007/s11019-019-09912-8. View

Ord T . The scourge: moral implications of natural embryo loss. Am J Bioeth. 2008; 8(7):12-9. DOI: 10.1080/15265160802248146. View

Bock G . Jehovah's Witnesses and autonomy: honouring the refusal of blood transfusions. J Med Ethics. 2012; 38(11):652-6. DOI: 10.1136/medethics-2012-100802. View

Dimitriadis I, Zaninovic N, Badiola A, Bormann C . Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online. 2022; 44(3):435-448. DOI: 10.1016/j.rbmo.2021.11.003. View

Obermeyer Z, Powers B, Vogeli C, Mullainathan S . Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019; 366(6464):447-453. DOI: 10.1126/science.aax2342. View

Ferlito B, Segers S, De Proost M, Mertes H . Responsibility Gap(s) Due to the Introduction of AI in Healthcare: An Ubuntu-Inspired Approach. Sci Eng Ethics. 2024; 30(4):34. PMC: 11294411. DOI: 10.1007/s11948-024-00501-4. View

Rudin C . Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2022; 1(5):206-215. PMC: 9122117. DOI: 10.1038/s42256-019-0048-x. View

10.

Afnan M, Liu Y, Conitzer V, Rudin C, Mishra A, Savulescu J . Interpretable, not black-box, artificial intelligence should be used for embryo selection. Hum Reprod Open. 2021; 2021(4):hoab040. PMC: 8687137. DOI: 10.1093/hropen/hoab040. View

11.

Lyell D, Coiera E . Automation bias and verification complexity: a systematic review. J Am Med Inform Assoc. 2016; 24(2):423-431. PMC: 7651899. DOI: 10.1093/jamia/ocw105. View

12.

Bamford T, Easter C, Montgomery S, Smith R, Dhillon-Smith R, Barrie A . A comparison of 12 machine learning models developed to predict ploidy, using a morphokinetic meta-dataset of 8147 embryos. Hum Reprod. 2023; 38(4):569-581. DOI: 10.1093/humrep/dead034. View

13.

Jiang V, Bormann C . Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction. Fertil Steril. 2023; 120(2):228-234. DOI: 10.1016/j.fertnstert.2023.06.025. View

14.

Sandel M . Embryo ethics--the moral logic of stem-cell research. N Engl J Med. 2004; 351(3):207-9. DOI: 10.1056/NEJMp048145. View

15.

Verhaak C, Smeenk J, Evers A, Kremer J, Kraaimaat F, Braat D . Women's emotional adjustment to IVF: a systematic review of 25 years of research. Hum Reprod Update. 2006; 13(1):27-36. DOI: 10.1093/humupd/dml040. View

16.

Storr A, Venetis C, Cooke S, Kilani S, Ledger W . Inter-observer and intra-observer agreement between embryologists during selection of a single Day 5 embryo for transfer: a multicenter study. Hum Reprod. 2016; 32(2):307-314. DOI: 10.1093/humrep/dew330. View

17.

Brock D . The non-identity problem and genetic harms -- the case of wrongful handicaps. Bioethics. 1995; 9(3-4):269-75. DOI: 10.1111/j.1467-8519.1995.tb00361.x. View

18.

Bortolotti L, Harris J . Stem cell research, personhood and sentience. Reprod Biomed Online. 2005; 10 Suppl 1:68-75. DOI: 10.1016/s1472-6483(10)62210-9. View

19.

Segers S, De Proost M . Take five? A coherentist argument why medical AI does not require a new ethical principle. Theor Med Bioeth. 2024; 45(5):387-400. DOI: 10.1007/s11017-024-09676-0. View

20.

Wang F, Kaushal R, Khullar D . Should Health Care Demand Interpretable Artificial Intelligence or Accept "Black Box" Medicine?. Ann Intern Med. 2019; 172(1):59-60. DOI: 10.7326/M19-2548. View