2,840
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

When AI Is Wrong: Addressing Liability Challenges in Women’s Healthcare

ORCID Icon

References

  • Fleming N. How artificial intelligence is changing drug discovery spotlight /631/45 /639/705/117 /631/154 /706/703/559 n/a. Nature. 2018;557(7707):S55–S57. doi:10.1038/d41586-018-05267-x.
  • Shaheen MY. Applications of Artificial Intelligence (AI) in healthcare: a review. Sci Prepr. 2021. Published online doi: 10.14293/S2199-1006.1.SOR-.PPVRY8K.v1.
  • Sim KM. Bilattices and reasoning in artificial intelligence: concepts and foundations. Artif Intell Rev. 2001;15(3):219–40. doi:10.1023/A:1011049617655.
  • Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Futur Healthc J. 2019;6(2):94–98. doi:10.7861/futurehosp.6-2-94.
  • Rong G, Mendez A, Bou Assi E, Zhao B, Sawan M. Artificial intelligence in healthcare: review and prediction case studies. Engineering. 2020;6(3):291–301. doi:10.1016/j.eng.2019.08.015.
  • Tulk Jesso S, Kelliher A, Sanghavi H, Martin T, Henrickson Parker S. Inclusion of clinicians in the development and evaluation of clinical artificial intelligence tools: a systematic literature review. Front Psychol. 2022:13. doi:10.3389/fpsyg.2022.830345.
  • Bates DW, Auerbach A, Schulam P, Wright A, Saria S. Reporting and implementing interventions involving machine learning and artificial intelligence. Ann Intern Med. 2020;172(11_Supplement):S137–S144. doi:10.7326/M19-0872.
  • Schwendicke F, Krois J. Better reporting of studies on artificial intelligence: CONSORT-AI and beyond. J Dent Res. 2021;100(7):677–80. doi:10.1177/0022034521998337.
  • Mun SK, Wong KH, Lo SCB, Li Y, Bayarsaikhan S. Artificial intelligence for the future radiology diagnostic service. Front Mol Biosci. 2021;7:512. doi:10.3389/fmolb.2020.614258.
  • Shah NR. Health care in 2030: will artificial intelligence replace physicians? Ann Intern Med. 2019;170(6):407–08. doi:10.7326/M19-0344.
  • Sanford T, Harmon SA, Turkbey EB, Kesani D, Tuncer S, Madariaga M, Yang C, Sackett J, Mehralivand S, Yan P, et al. Deep-learning-based artificial intelligence for PI-RADS classification to assist multiparametric prostate MRI interpretation: a development study. J Magn Reson Imaging. 2020;52(5):1499–507. doi:10.1002/jmri.27204.
  • Schriger DL, Elder JW, Cooper RJ. Structured clinical decision aids are seldom compared with subjective physician judgment, and are seldom superior. Ann Emerg Med. 2017;70(3):338–344.e3. doi:10.1016/j.annemergmed.2016.12.004.
  • Wears RL, Berg M. Computer technology and clinical work: still waiting for godot. J Am Med Assoc. 2005;293(10):1261–63. doi:10.1001/jama.293.10.1261.
  • Hu Y, Jacob J, Parker GJM, Hawkes DJ, Hurst JR, Stoyanov D. The challenges of deploying artificial intelligence models in a rapidly evolving pandemic. Nat Mach Intell. 2020;2(6):298–300. doi:10.1038/s42256-020-0185-2.
  • Panch T, Mattie H, Celi LA. The “inconvenient truth” about AI in healthcare. Npj Digit Med. 2019;2(1):1–3. doi:10.1038/s41746-019-0155-4.
  • Doyen S, Dadario NB. 12 plagues of AI in healthcare: a practical guide to current issues with using machine learning in a medical context. Front Digit Heal. 2022;74. doi:10.3389/FDGTH.2022.765406.
  • Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-Rivero AI, Etmann C, McCague C, Beer L, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell. 2021;3(3):199–217. doi:10.1038/s42256-021-00307-0.
  • Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369. doi:10.1136/bmj.m1328.
  • Bentley AR, Callier S, Rotimi CN. Diversity and inclusion in genomic research: why the uneven progress? J Community Genet. 2017;8(4):255–66. doi:10.1007/s12687-017-0316-6.
  • Werling DM, Geschwind DH. Sex differences in autism spectrum disorders. Curr Opin Neurol. 2013;26(2):146–53. doi:10.1097/WCO.0b013e32835ee548.
  • Chen IY, Szolovits P, Ghassemi M. Can AI help reduce disparities in general medical and mental health care? AMA J Ethics. 2019;21(2):167–79. doi:10.1001/amajethics.2019.167.
  • Cirillo D, Catuara-Solarz S, Morey C, Guney E, Subirats L, Mellino S, Gigante A, Valencia A, Rementeria MJ, Chadha AS, et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. Npj Digit Med. 2020;3(1):1–11. doi:10.1038/s41746-020-0288-5.
  • Miller IN, Cronin-Golomb A. Gender differences in Parkinson’s disease: clinical characteristics and cognition. Mov Disord. 2010;25(16):2695–703. doi:10.1002/MDS.23388.
  • Regitz-Zagrosek V. Sex and gender differences in health. science & society series on sex and science. EMBO Rep. 2012;13(7):596–603. doi:10.1038/embor.2012.87.
  • Ferretti MT, Iulita MF, Cavedo E, Chiesa PA, Schumacher Dimech A, Santuccione Chadha A, Baracchi F, Girouard H, Misoch S, Giacobini E, et al. Sex differences in Alzheimer disease — the gateway to precision medicine. Nat Rev Neurol. 2018;14(8):457–69. doi:10.1038/s41582-018-0032-9.
  • Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Informatics Assoc. 2020;27(3):491–97. doi:10.1093/jamia/ocz192.
  • Asan O, Bayrak AE, Artificial Intelligence CA. Human trust in healthcare: focus on clinicians. J Med Internet Res. 2020;22(6):e15154. doi:10.2196/15154.
  • Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, Jung K, Heller K, Kale D, Saeed M, et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25(9):1337–40. doi:10.1038/s41591-019-0548-6.
  • Janowitz MF, Solow D. How to read and do proofs. Am Math Mon. 1993;100(2):197. doi:10.2307/2323794.
  • DeSanctis G, Gallupe RB. Decision support systems: concepts and resources for managers. Manage Sci. 1987; 20(4):80–81. [Accessed May 16, 2022]. https://books.google.com/books?hl=en‎&id=9NA6QMcte3cC&oi=fnd&pg=PR9&dq=+%22Decision+Support+Systems%22&ots=DPpsrwMvBa&sig=NgG5F6ya69eWvn6SsgY3SVG9pgw
  • Charu C, Aggarwal JH. Data mining: the textbook. Springer Int Publ. Published online 2015:746. Accessed May 16, 2022. https://books.google.com/books/about/Data_Mining.html?hl=it&id=cfNICAAAQBAJ
  • Bonettini S, Porta F, Ruggiero V. A variable metric forward-backward method with extrapolation. SIAM J Sci Comput. 2016;38(4):A2558–A2584. doi:10.1137/15M1025098.
  • Dixit A, Sahu DR, Gautam P, Som T, Yao JC. An accelerated forward-backward splitting algorithm for solving inclusion problems with applications to regression and link prediction problems. J Nonlinear Var Anal. 2021;5(1):79–101. doi:10.23952/JNVA.5.2021.1.06.
  • He B, Yuan X. Forward–backward-based descent methods for composite variational inequalities. Optimization Methods and Software . 2013;28(4):706–24. doi:10.1080/10556788.2011.645033.
  • Mullen M, Brennan C, Downes T. A hybridized forward backward method applied to electromagnetic wave scattering problems. IEEE Trans Antennas Propag. 2009;57(6):1846–50. doi:10.1109/TAP.2009.2019994.
  • Holliday D, Deraad LL, St-Cyr GJ. Forward-backward method for scattering from imperfect conductors. IEEE Trans Antennas Propag. 1998;46(1):101–07. doi:10.1109/8.655456.
  • What CE. AI-Driven decision making looks like. Harvard Business Review. Published 2019. Accessed 1 June 2022. https://hbr.org/2019/07/what-ai-driven-decision-making-looks-like
  • Phillips-Wren G. AI tools in decision making support systems: a review. Int J Artif Intell Tools. 2012;21(2):2. doi:10.1142/S0218213012400052.
  • Amann J, Vetter D, Blomberg SN, Christensen HC, Coffee M, Gerke S, Gilbert TK, Hagendorff T, Holm S, Livne M, et al. To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems. PLOS Digit Heal. 2022;1(2):e0000016. doi:10.1371/journal.pdig.0000016.
  • Bleher H, Braun M. Diffused responsibility: attributions of responsibility in the use of AI-driven clinical decision support systems. AI Ethics. 2022;1:1. doi:10.1007/s43681-022-00135-x.
  • Hafemeister TL, Gulbrandsen RM. The fiduciary obligation of physicians to “Just Say No” if an “Informed” patient demands services that are not medically indicated. Vol 39.; 2009. Accessed 3 June 2022. http://www.pewinternet.org/pdfs/PIP_Health_
  • Maliha G, Gerke S, Cohen IG, Parikh RB. Artificial intelligence and liability in medicine: balancing safety and innovation. Milbank Q. 2021 April 6;99(3):629–47. Published online. doi:10.1111/1468-0009.12504.
  • Lysaght T, Lim HY, Xafis V, Ngiam KY. AI-assisted decision-making in healthcare: the application of an ethics framework for big data in health and research. Asian Bioeth Rev. 2019;11(3):299–314. doi:10.1007/s41649-019-00096-0.
  • DePaul KS. Journal of health care law DePaul journal of health care law part of the health law and policy commons recommended citation recommended citation Sarah kamensky, artificial intelligence and technology in health care: overview and possible legal implications. 21 DePaul J Heal Care L. 2020. Published online doi: 10.1098/rsta.2017.0360.
  • Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial intelligence in healthcare 1st Bohr, A, Memarzadeh, K. Elsevier; 2020. 295–336. doi: 10.1016/b978-0-12-818438-7.00012-5.
  • Yu R, Alì GS. What’s inside the black box? AI Challenges for lawyers and researchers. In: Legal information management. Vol. 19. Cambridge, UK: Cambridge University Press (CUP); 2019. p. 2–13. doi:10.1017/s1472669619000021.
  • Sperling E. How hardware can bias AI Data: seminconductorengeneering. Published 2019. Accessed 15 May 2022. https://semiengineering.com/where-data-gets-biased/
  • Stobierski T. 8 steps in the data life cycle. Harvard Business School. Published 2021. Accessed 1 June 2022. https://online.hbs.edu/blog/post/data-life-cycle
  • Zhou M, Guo J, Chen N, Ma M, Dong S, Li Y, Fang J, Zhang Y, Zhang Y, Bao J, et al. Effects of message framing and time discounting on health communication for optimum cardiovascular disease and stroke prevention (EMT-OCSP): a protocol for a pragmatic, multicentre, observer-blinded, 12-month randomised controlled study. BMJ Open. 2021;11(3):3. doi:10.1136/bmjopen-2020-043450.
  • Gutbezahl J. 5 types of statistical biases to avoid in your analyses. Harvard Business School - HBS Online. Published 2017. Accessed 30 May 2022. https://online.hbs.edu/blog/post/types-of-statistical-bias
  • Garbin C, Marques MO. Tools to improve reporting, increaseTransparency, and reduce failures in machine learning applications in healthCare. Radiol Artif Intell. 2022;4(2):2. doi:10.1148/RYAI.210127.
  • Raji ID, Smart A, White RN, Mitchell, M, Gebru, T, Hutchinson, B, Smith-Loud, J, Theron, D, Barnes, P, et al. Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. FAT* 2020 - Proc 2020 Conf Fairness, Accountability, Transpar Barcelona Spain. Published online 2020: 33–44. doi:10.1145/3351095.3372873.
  • Johnston AC, Warkentin M. Information privacy compliance in the healthcare industry. Inf Manag & Comput Secur. 2008;16(1):5–19. doi:10.1108/09685220810862715.
  • Johnston MB, Roper L. HIPAA becomes reality: compliance with new privacy, security, and electronic transmission standards. West VA Law Rev. 2000 Accessed August 14, 2021; 103. https://heinonline.org/HOL/Page?handle=hein.journals/wvb103&id=553&div=&collection=
  • Auditability checklist | auditing machine learning algorithms. Accessed 1 June 2022. https://www.auditingalgorithms.net/AuditabilityChecklist.html
  • Mukherjee D, Yurochkin M, Banerjee M, Sun Y. Two simple ways to learn individual fairness metrics from data. 37th Int Conf Mach Learn ICML 2020. 2020;PartF16814: 7054-7064. Virtual Event. Accessed June 3, 2022. https://github.com/
  • Olteanu A, Castillo C, Diaz F, Kıcıman E. Social data: biases, methodological pitfalls, and ethical boundaries. Front Big Data. 2019;2(13):13. doi:10.3389/fdata.2019.00013.
  • Partridge D, Hussain KM. Artificial intelligence and business management. Ablex P, ed. Norwood, N.J.: Ablex Pub. Corp. 1992. Accessed June 7, 2022 https://books.google.com/books?id=3_zkZYwj43sC&pgis=1