Download PDF
CC BY 4.0 · Yearb Med Inform 2024; 33(01): 075-082
DOI: 10.1055/s-0044-1800723
Section 1: Bioinformatics and Translational Informatics
Survey

Searching for Value Sensitive Design in Applied Health AI: A Narrative Review

Yufei Long
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
,
Laurie Novak
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
,
Colin G. Walsh
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
› Author Affiliations
› Further Information
Also available at
   Permissions and Reprints

Summary

Objective: Recent advances in the implementation of healthcare artificial intelligence (AI) have drawn attention toward design methods to address the impacts on workflow. Lesser known than human-centered design, Value Sensitive Design (VSD) is an established framework integrating values into conceptual, technical, and empirical investigations of technology. We sought to study the current state of the literature intersecting elements of VSD with practical applications of healthcare AI.

Methods: Using a modified VSD framework attentive to AI-specific values, we conducted a narrative review informed by PRISMA guidelines and assessed VSD elements across design and implementation case studies.

Results: Our search produced 819 articles that went through multiple rounds of review. Nine studies qualified for full-text review. Most of the studies focused on values for the individual or professional practice such as trust and autonomy. Attention to organizational (e.g., stewardship, employee well-being) and societal (e.g., equity, justice) values was lacking. Studies were primarily from the U.S. and Western Europe.

Conclusion: Future design studies might better incorporate components of VSD by considering larger domains, organizational and societal, in value identification and to bridge to design processes that are not just human-centered but value sensitive. The small number of heterogeneous studies underlines the importance of broader studies of elements of VSD to inform healthcare AI in practice.

Keywords

Value-sensitive design - Artificial Intelligence - Ethical value – Universal Design - Healthcare


Publication History

Article published online:
08 April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019 Jul;8(7):2328–31.
    PubMed
  • 2 Brown LA, Benhamou K, May AM, Mu W, Berk R. Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation. J Clin Psychiatry. 2020 Apr 21;81(3):10951.
    PubMed
  • 3 Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 2021 Apr 10;21(1):125.
    PubMed
  • 4 Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol [Internet]. 2017 Dec 1 [cited 2024 Apr 29];2(4). Available from: https://svn.bmj.com/content/2/4/230
    PubMed
  • 5 Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, et al. Risk Prediction Models for Hospital Readmission: A Systematic Review. JAMA. 2011 Oct 19;306(15):1688–98.
    PubMed
  • 6 Goldstein BA, Navar AM, Pencina MJ, Ioannidis JPA. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017 Jan;24(1):198–208.
    PubMed
  • 7 Turing AM. Computing Machinery and Intelligence. In: Epstein R, Roberts G, Beber G, editors. Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer [Internet]. Dordrecht: Springer Netherlands; 2009 [cited 2024 Apr 29]. p. 23–65. Available from: https://doi.org/10.1007/978-1-4020-6710-5_3
    PubMed
  • 8 Zhang P, Kamel Boulos MN. Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges. Future Internet. 2023 Sep;15(9):286.
    PubMed
  • 9 Wang L, Zhang Z, Wang D, Cao W, Zhou X, Zhang P, et al. Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review. Frontiers in Computer Science [Internet]. 2023 [cited 2024 Jan 9];5. Available from: https://www.frontiersin.org/articles/10.3389/fcomp.2023.1187299
    PubMed
  • 10 Matheny M, Israni ST, Ahmed M, Whicher D. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril.
    PubMed
  • 11 Kiener M. Artificial intelligence in medicine and the disclosure of risks. AI & Soc. 2021 Sep 1;36(3):705–13.
    PubMed
  • 12 Auernhammer J, Zallio M, Domingo L, Leifer L. Facets of Human-Centered Design: The Evolution of Designing by, with, and for People. In: Meinel C, Leifer L, editors. Design Thinking Research : Achieving Real Innovation [Internet]. Cham: Springer International Publishing; 2022 [cited 2024 Feb 14]. p. 227–45. (Understanding Innovation). Available from: https://doi.org/10.1007/978-3-031-09297-8_12
    PubMed
  • 13 Salwei ME, Hoonakker PL, Pulia M, Wiegmann D, Patterson BW, Carayon P. Retrospective analysis of the human-centered design process used to develop a clinical decision support in the emergency department: PE Dx Study Part 2. Human Factors in Healthcare. 2023;4:100055.
    PubMed
  • 14 Chen Y, Clayton EW, Novak LL, Anders S, Malin B. Human-Centered Design to Address Biases in Artificial Intelligence. J Med Internet Res. 2023 Mar 24;25:e43251.
    PubMed
  • 15 Shneiderman B. Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. International Journal of Human–Computer Interaction. 2020 Apr 2;36(6):495–504.
    PubMed
  • 16 Embi PJ. Algorithmovigilance—Advancing Methods to Analyze and Monitor Artificial Intelligence–Driven Health Care for Effectiveness and Equity. JAMA Network Open. 2021 Apr 15;4(4):e214622.
    PubMed
  • 17 Friedman B, Hendry DG. Value Sensitive Design: Shaping Technology with Moral Imagination. MIT Press; 2019. 258 p.
    PubMed
  • 18 The White House [Internet]. 2023 [cited 2024 Feb 20]. OMB Releases Implementation Guidance Following President Biden's Executive Order on Artificial Intelligence | OMB. Available from: https://www.whitehouse.gov/omb/briefing-room/2023/11/01/omb-releases-implementation-guidance-following-president-bidens-executive-order-on-artificial-intelligence/
    PubMed
  • 19 Commissioner O of the. Focus Area: Artificial Intelligence. FDA [Internet]. 2022 Sep 6 [cited 2024 Feb 20]; Available from: https://www.fda.gov/science-research/focus-areas-regulatory-science-report/focus-area-artificial-intelligence
    PubMed
  • 20 Matheny ME, Whicher D, Thadaney Israni S. Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. JAMA. 2020 Feb 11;323(6):509–10.
    PubMed
  • 21 Commission launches AI innovation package to support Artificial Intelligence startups and SMEs | Shaping Europe's digital future [Internet]. 2024 [cited 2024 Feb 20]. Available from: https://digital-strategy.ec.europa.eu/en/news/commission-launches-ai-innovation-package-support-artificial-intelligence-startups-and-smes
    PubMed
  • 22 Umbrello S, van de Poel I. Mapping value sensitive design onto AI for social good principles. AI Ethics. 2021 Aug 1;1(3):283–96.
    PubMed
  • 23 Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71.
    PubMed
  • 24 Hallowell N, Badger S, Sauerbrei A, Nellåker C, Kerasidou A. “I don't think people are ready to trust these algorithms at face value”: trust and the use of machine learning algorithms in the diagnosis of rare disease. BMC Medical Ethics. 2022 Nov 16;23(1):112.
    PubMed
  • 25 Cenci A, Ilskov SJ, Andersen NS, Chiarandini M. The participatory value-sensitive design (VSD) of a mHealth app targeting citizens with dementia in a Danish municipality. AI Ethics. 2023 Apr 13;1–27.
    PubMed
  • 26 Wang H, Gupta S, Singhal A, Muttreja P, Singh S, Sharma P, et al. An Artificial Intelligence Chatbot for Young People's Sexual and Reproductive Health in India (SnehAI): Instrumental Case Study. J Med Internet Res. 2022 Jan 3;24(1):e29969.
    PubMed
  • 27 Götzl C, Hiller S, Rauschenberg C, Schick A, Fechtelpeter J, Fischer Abaigar U, et al. Artificial intelligence-informed mobile mental health apps for young people: a mixed-methods approach on users' and stakeholders' perspectives. Child and Adolescent Psychiatry and Mental Health. 2022 Nov 17;16(1):86.
    PubMed
  • 28 Kerr JI, Naegelin M, Benk M, V Wangenheim F, Meins E, Viganò E, et al. Investigating Employees' Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study. J Med Internet Res. 2023 Apr 13;25:e44131.
    PubMed
  • 29 Cagliero D, Deuitch N, Shah N, Feudtner C, Char D. A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning. J Am Med Inform Assoc. 2023 Apr 19;30(5):819–27.
    PubMed
  • 30 Helenason J, Ekström C, Falk M, Papachristou P. Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care – a mixed method study. Scandinavian Journal of Primary Health Care. 2023;0(0):1–10.
    PubMed
  • 31 Helman S, Terry MA, Pellathy T, Hravnak M, George E, Al-Zaiti S, et al. Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support. Appl Clin Inform. 2023 Aug;14(4):789–802.
    PubMed
  • 32 Fischer A, Rietveld A, Teunissen P, Hoogendoorn M, Bakker P. What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals. BMJ Open. 2023 Oct 1;13(10):e076017.
    PubMed
  • 33 Gibson JJ. The Theory of Affordances. In: The ecological approach to visual perception. Boulder, Colorado: Taylot & Francis; 1979. p. 119–37.
    PubMed