Download PDF
CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 152-160
DOI: 10.1055/s-0042-1742516
Section 4: Clinical Research Informatics
Survey

A Literature Review on Ethics for AI in Biomedical Research and Biobanking

Michaela Kargl
Medical University Graz, Graz, Austria
,
Markus Plass
Medical University Graz, Graz, Austria
,
Heimo Müller
Medical University Graz, Graz, Austria
› Author Affiliations
› Further Information
   Permissions and Reprints

Summary

Background: Artificial Intelligence (AI) is becoming more and more important especially in datacentric fields, such as biomedical research and biobanking. However, AI does not only offer advantages and promising benefits, but brings about also ethical risks and perils. In recent years, there has been growing interest in AI ethics, as reflected by a huge number of (scientific) literature dealing with the topic of AI ethics. The main objectives of this review are: (1) to provide an overview about important (upcoming) AI ethics regulations and international recommendations as well as available AI ethics tools and frameworks relevant to biomedical research, (2) to identify what AI ethics can learn from findings in ethics of traditional biomedical research - in particular looking at ethics in the domain of biobanking, and (3) to provide an overview about the main research questions in the field of AI ethics in biomedical research.

Methods: We adopted a modified thematic review approach focused on understanding AI ethics aspects relevant to biomedical research. For this review, four scientific literature databases at the cross-section of medical, technical, and ethics science literature were queried: PubMed, BMC Medical Ethics, IEEE Xplore, and Google Scholar. In addition, a grey literature search was conducted to identify current trends in legislation and standardization.

Results: More than 2,500 potentially relevant publications were retrieved through the initial search and 57 documents were included in the final review. The review found many documents describing high-level principles of AI ethics, and some publications describing approaches for making AI ethics more actionable and bridging the principles-to-practice gap. Also, some ongoing regulatory and standardization initiatives related to AI ethics were identified. It was found that ethical aspects of AI implementation in biobanks are often like those in biomedical research, for example with regards to handling big data or tackling informed consent. The review revealed current ‘hot’ topics in AI ethics related to biomedical research. Furthermore, several published tools and methods aiming to support practical implementation of AI ethics, as well as tools and frameworks specifically addressing complete and transparent reporting of biomedical studies involving AI are described in the review results.

Conclusions: The review results provide a practically useful overview of research strands as well as regulations, guidelines, and tools regarding AI ethics in biomedical research. Furthermore, the review results show the need for an ethical-mindful and balanced approach to AI in biomedical research, and specifically reveal the need for AI ethics research focused on understanding and resolving practical problems arising from the use of AI in science and society.

Keywords

AI ethics - biomedical research - biobank - literature review


Publication History

Article published online:
04 December 2022

© 2022. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 AI HLEG, European Commission. Ethics guidelines for trustworthy AI; 2019.
    PubMed
  • 2 UNESCO. Recommendation on the ethics of artificial intelligence; 2021. Available from: https://unesdoc.unesco.org/ark:/48223/pf0000377897
    PubMed
  • 3 WHO. Ethics and governance of artificial intelligence for health ethics and governance of artificial intelligence for health 2; 2021. Available from: http://apps.who.int/bookorders
    PubMed
  • 4 Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nature Machine Intelligence 2019;1:389–99.
    PubMed
  • 5 Khan AA, Badshah S, Liang P, Khan B, Waseem M, Niazi M, et al. Ethics of AI: A systematic literature review of principles and challenges The International Conference on Evaluation and Assessment in Software Engineering 2022. p. 383-92.
    PubMed
  • 6 Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: A mapping review. Soc Sci Med 2020 Sep;260:113172.
    PubMed
  • 7 Ienca M, Ferretti A, Hurst S, Puhan M, Lovis C, Vayena E. Considerations for ethics review of big data health research: A scoping review. PLoS One 2018 Oct 11;13(10):e0204937.
    PubMed
  • 8 Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics 2021 Feb 15;22(1):14.
    PubMed
  • 9 European Commission. 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; 2021.
    PubMed
  • 10 Hagendorff T. The ethics of AI ethics: An evaluation of guidelines. Minds and Machines 2020;30:99–120.
    PubMed
  • 11 Floridi L, Cowls J. A unified framework of five principles for AI in society. Harvard Data Science Review 2019;1.
    PubMed
  • 12 Loi M, Heitz C, Christen M. A comparative assessment and synthesis of twenty ethics codes on AI and big data. Institute of Electrical and Electronics Engineers Inc.; 2020. p. 41–6.
    PubMed
  • 13 Floridi L, Cowls C, Beltrametti M, Chatila R, Chazerand P, Dignum V, et al. Ai4people - an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines 2018;28:689–707.
    PubMed
  • 14 OECD. Recommendation of the council on artificial intelligence. oecd/legal/0449;2019. Available from: https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
    PubMed
  • 15 Zhou J, Chen F, Berry A, Reed M, Zhang S, Savage S. A survey on ethical principles of AI and implementations. Institute of Electrical and Electronics Engineers Inc.; 2020. p. 3010–7.
    PubMed
  • 16 Schiff D, Rakova B, A. Ayesh A, Fanti A, Lennon M. Principles to practices for responsible AI: Closing the gap. arXiv preprint arXiv:2006.04707; 2020.
    PubMed
  • 17 Ryan M, Stahl BC. Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications. Journal of Information, Communication and Ethics in Society 2021;19:61–86.
    PubMed
  • 18 Nebeker C, Torous J, Bartlett Ellis RJ. Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC Med 2019 Jul 17;17(1):137.
    PubMed
  • 19 IEEE, 2021, IEEE 7000™ projects | IEEE ethics in action. Available from: https://ethicsinaction.ieee.org/p7000/
    PubMed
  • 20 Hjalmarson M. To ethicize or not to ethicize, in ISOfocus_137, 2019. Available from: https://www.iso.org/isofocus_137.html
    PubMed
  • 21 Müller H, Mayrhofer MT, Veen EBV, Holzinger A. The ten commandments of ethical medical AI. Computer 2021;54:119–23.
    PubMed
  • 22 Jongsma KR, Bredenoord AL. Ethics parallel research: an approach for (early) ethical guidance of biomedical innovation. BMC Med Ethics 2020 Sep 1;21(1):81.
    PubMed
  • 23 Liaw ST, Liyanage H, Kuziemsky C, Terry AL, Schreiber R, Jonnagaddala J, et al. Ethical Use of Electronic Health Record Data and Artificial Intelligence: Recommendations of the Primary Care Informatics Working Group of the International Medical Informatics Association. Yearb Med Inform 2020 Aug;29(1):51-7.
    PubMed
  • 24 Solbakk JH, Holm S, Hofmann B. The Ethics of Research Biobanking. Springer US, USA; 2009.
    PubMed
  • 25 Kozlakidis Z. Biobanks and Biobank-Based Artificial Intelligence (AI) Implementation Through an International Lens. In: Artificial Intelligence and Machine Learning for Digital Pathology. Cham: Springer; 2020. p. 195-203.
    PubMed
  • 26 Lee JE. Artificial intelligence in the future biobanking: Current issues in the biobank and future possibilities of artificial intelligence, Biomedical Journal of Scientific &Technical Research 2018;7(3):5937-9.
    PubMed
  • 27 Kinkorová J, Topoléan O. Biobanks in the era of big data: objectives, challenges, perspectives, and innovations for predictive, preventive, and personalised medicine. EPMA J 2020;11(3):333–41.
    PubMed
  • 28 Jurate S, V. Zivile V, Eugenijus G. ’Mirroring‘ the ethics of biobanking: What analysis of consent documents can tell us? Sci Eng Ethics 2014;20:1079–93.
    PubMed
  • 29 Chauhan C, Gullapalli RR. Ethics of AI in Pathology: Current Paradigms and Emerging Issues. Am J Pathol 2021 Oct;191(10):1673-83.
    PubMed
  • 30 Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 2022 Jan;35(1):23-32.
    PubMed
  • 31 Gille F, Vayena E, Blasimme A. Future-proofing biobanks’ governance. Eur J Hum Genet 2020 Aug;28(8):989-96.
    PubMed
  • 32 Saheb T, Saheb T, Carpenter DO. Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis. Comput Biol Med 2021 Aug;135:104660.
    PubMed
  • 33 Goodman KW. Ethics in Health Informatics. Yearb Med Inform 2020 Aug;29(1):26-31.
    PubMed
  • 34 Blasimme A, Vayena E. The ethics of AI in biomedical research, patient care and public health. Patient Care and Public Health (April 9, 2019). Oxford Handbook of Ethics of Artificial Intelligence. Forthcoming.
    PubMed
  • 35 Morley J, Floridi L, Kinsey L, Elhalal A. From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices, Sci Eng Ethics 2020;26(4):2141-68.
    PubMed
  • 36 European Commission and Directorate-General for Communications Networks, Content and Technology. The Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self assessment. EC Publications Office; 2020.
    PubMed
  • 37 Zicari RV, Brodersen J, Brusseau J, Dudder B, Eichhorn T, Ivanov T, et al. Z-inspection®: A process to assess trustworthy AI. IEEE Trans Technol Soc 2021;2:83–97.
    PubMed
  • 38 Malik A, Patel P, Ehsan L, Guleria S, Hartka T, Adewole S, et al. Ten simple rules for engaging with artificial intelligence in biomedicine. PLoS Comput Biol 2021 Feb 11;17(2):e1008531.
    PubMed
  • 39 IBM. Trusted AI | IBM research teams; 2021. Available from: https://research.ibm.com/teams/trusted-ai#tools
    PubMed
  • 40 Bellamy RK, Dey K, Hind M, Hoffman SC, Houde S, Kannan K, et al. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM J Res Dev 2019;63(4/5): 4:1 - 4:15.
    PubMed
  • 41 Richards J, Piorkowski D, Hind M, Houde S, Mojsilovic A. A methodology for creating AI FactSheets. arXiv preprint arXiv:2006.13796;2020.
    PubMed
  • 42 Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, et al. Model cards for model reporting. ACM 2019.
    PubMed
  • 43 Ibrahim H, Liu X, Rivera SC, Moher D, Chan AW, Sydes MR, et al. Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines. Trials 2021 Jan 6;22(1):11.
    PubMed
  • 44 Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, et al; STARD-AI Steering Committee. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 2021 Jun 28;11(6):e047709.
    PubMed
  • 45 Shelmerdine SC, Arthurs OJ, Denniston A, Sebire NJ. Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health Care Inform 2021 Aug;28(1):e100385.
    PubMed
  • 46 Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. J Med Internet Res 2016 Dec 16;18(12):e323.
    PubMed
  • 47 Matschinske J, Alcaraz N, Benis A, Golebiewski M, Grimm DG, Heumos L, et al. The AIMe registry for artificial intelligence in biomedical research. Nat Methods 2021 Oct;18(10):1128-31.
    PubMed
  • 48 Jotterand F, Bosco C. Artificial Intelligence in Medicine: A Sword of Damocles? J Med Syst 2021 Dec 11;46(1):9.
    PubMed
  • 49 Buruk B, Ekmekci PE, Arda B. A critical perspective on guidelines for responsible and trustworthy artificial intelligence. Med Health Care Philos 2020 Sep;23(3):387-99.
    PubMed
  • 50 Faes L, Liu X, Wagner SK, Fu DJ, Balaskas K, Sim DA, et al. A Clinician’s Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies. Transl Vis Sci Technol 2020 Feb 12;9(2):7.
    PubMed
  • 51 Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet 2019 Apr 20;393(10181):1577-9.
    PubMed
  • 52 CONSORT-AI and SPIRIT-AI Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med 2019 Oct;25(10):1467-8.
    PubMed
  • 53 Liu X, Faes L, Calvert MJ, Denniston AK; CONSORT/SPIRIT-AI Extension Group. Extension of the CONSORT and SPIRIT statements. Lancet 2019 Oct 5;394(10205):1225.
    PubMed
  • 54 Benkler Y. Don’t let industry write the rules for AI. Nature 2019 May;569(7755):161.
    PubMed
  • 55 Greene D, Hoffmann AL, Stark L. Better, nicer, clearer, fairer: A critical assessment of the movement for ethical artificial intelligence and machine learning. Proceedings of the Annual Hawaii International Conference on System Sciences; 2019. p. 2122–31.
    PubMed
  • 56 Mittelstadt, B. AI Ethics–Too principled to fail. arXiv preprint arXiv:1906.06668. 2019.
    PubMed
  • 57 Whittlestone J, Nyrup R, Alexandrova A, Cave S. The role and limits of principles in AI ethics: Towards a focus on tensions. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. ACM; 2019. p. 195-200. Available from: https://doi.org/10.1145/3306618.3314289
    PubMed