Abstract
In an analysis of artificially intelligent systems for medical diagnostics and treatment planning we argue that patients should be able to exercise a right to withdraw from AI diagnostics and treatment planning for reasons related to (1) the physician’s role in the patients’ formation of and acting on personal preferences and values, (2) the bias and opacity problem of AI systems, and (3) rational concerns about the future societal effects of introducing AI systems in the health care sector.
Similar content being viewed by others
Notes
In this paper we use the term ‘AI systems’ to cover both systems based on ‘symbolic AI’ and systems based on machine learning techniques such as deep learning and neural networks.
In this paper we use the term ‘physician’. Diagnostic and treatment decisions are also made by many other types of health care professionals, but we are focusing on medical doctors because they are involved in many of these decisions.
There is a parallel issue raised by AI controlled treatment, e.g. AI controlled surgical robots, but this is outside the scope of this paper.
References
Art. 22 GDPR. 2018. Automated Individual Decision-Making, Including Profiling|General Data Protection Regulation (GDPR). General Data Protection Regulation (GDPR). https://gdpr-info.eu/art-22-gdpr/. Accessed 11 July 2018.
Article 29 Data Protection Working Party. 2018. ARTICLE29 Guidelines on Automated Individual Decision-Making and Profiling for the Purposes of Regulation 2016/679 (wp251rev.01). https://ec.europa.eu/newsroom/article29/item-detail.cfm?item_id=612053. Accessed 20 June 2019
Bedi, G., F. Carrillo, G.A. Cecchi, D.F. Slezak, M. Sigman, N.B. Mota, et al. 2015. Automated Analysis of Free Speech Predicts Psychosis Onset in High-Risk Youths. npj Schizophrenia 1: 15030.
Bird, S., S. Barocas, K. Crawford, F. Diaz, H. Wallach. 2016. Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI. Rochester: Social Science Research Network. Report No.: ID 2846909. https://papers.ssrn.com/abstract=2846909. Accessed 29 May 2018.
Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies, 352. Oxford, New York: Oxford University Press.
Bostrom, N. 2017. Strategic Implications of Openness in AI Development. Global Policy 8 (2): 135–148.
Bozdag, E. 2013. Bias in Algorithmic Filtering and Personalization. Ethics and Information Technology 15 (3): 209–227.
Burrell, J. 2016. How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms. Big Data & Society 3 (1): 2053951715622512.
Cabitza, F., R. Rasoini, and G.F. Gensini. 2017. Unintended Consequences of Machine Learning in Medicine. JAMA 318 (6): 517–518.
Calders, T., and S. Verwer. 2010. Three Naive Bayes Approaches for Discrimination-Free Classification. Data Mining and Knowledge Discovery 21 (2): 277–292.
Dave, Lee R.C.-J. 2014. Hawking: AI Could End Human Race. BBC News. https://www.bbc.co.uk/news/technology-30290540. Accessed 15 Aug 2018.
Dave, Lee R.C.-J. 2016. Stephen Hawking—Will AI Kill or Save? BBC News. https://www.bbc.co.uk/news/technology-37713629. Accessed 15 Aug 2018.
Dilsizian, S.E., and E.L. Siegel. 2014. Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment. Current Cardiology Reports 16 (1): 441.
Dunbar-Jacob, J., and M.K. Mortimer-Stephens. 2001. Treatment Adherence in Chronic Disease. Journal of Clinical Epidemiology 54 (12, Supplement 1): S57–S60.
Esteva, A., B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, et al. 2017. Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature 542 (7639): 115–118.
Friedman, B., and H. Nissenbaum. 1996. Bias in Computer Systems. ACM Transactions on Information Systems 14 (3): 330–347.
Ghani, N.A., S. Hamid, I.A. Targio Hashem, E. Ahmed. 2018. Social Media Big Data Analytics: A Survey. Computers in Human Behavior. http://www.sciencedirect.com/science/article/pii/S074756321830414X. Accessed 15 Feb 2019.
Goddard, K., A. Roudsari, and J.C. Wyatt. 2012. Automation Bias: A Systematic Review of Frequency, Effect Mediators, and Mitigators. Journal of the American Medical Informatics Association 19 (1): 121–127.
Goddard, K., A. Roudsari, and J.C. Wyatt. 2014. Automation Bias: Empirical Results Assessing Influencing Factors. International Journal of Medical Informatics 83 (5): 368–375.
Habermas, J. 2018. Inclusion of the Other: Studies in Political Theory, 341. Hoboken: Wiley.
Hayashi, S., K. Wu, B. Tangsatapornpan. 2018. Competition Policy and the Development of Big Data and Artificial Intelligence. The Roles of Innovation in Competition Law Analysis. Accessed 15 Feb 2019. https://www.elgaronline.com/view/edcoll/9781788972437/9781788972437.00016.xml
Hoff, T. 2011. Deskilling and Adaptation Among Primary Care Physicians Using Two Work Innovations. Health Care Management Review 36 (4): 338–348.
Ipsos, M.O.R.I. 2017. Public Views of Machine Learning. Report Title:92.
Jiang, L., C.C. Yang. 2016. Personalized Recommendation in Online Health Communities with Heterogeneous Network Mining. In 2016 IEEE International Conference on Healthcare Informatics (ICHI), 281–284.
Krämer, N.C., and S. Winter. 2008. Impression Management 2.0: The Relationship of Self-esteem, Extraversion, Self-efficacy, and Self-presentation Within Social Networking Sites. Journal of Media Psychology: Theories, Methods, and Applications 20 (3): 106–116.
Leung, M.K.K., A. Delong, B. Alipanahi, and B.J. Frey. 2016. Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets. Proceedings of the IEEE 104 (1): 176–197.
Mittelstadt, B.D., P. Allo, M. Taddeo, S. Wachter, and L. Floridi. 2016. The Ethics of Algorithms: Mapping the Debate. Big Data & Society. https://doi.org/10.1177/2053951716679679.
Mittelstadt, B.D., and L. Floridi. 2015. The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts. Science and Engineering Ethics 22 (2): 303–341.
Morrell, R.W., D.C. Park, D.P. Kidder, and M. Martin. 1997. Adherence to Antihypertensive Medications Across the Life Span. Gerontologist 37 (5): 609–619.
Müller, V.C., N. Bostrom. 2016. Future Progress in Artificial Intelligence: A Survey of Expert Opinion. In Fundamental Issues of Artificial Intelligence. Cham: Springer, 555–572. (Synthese Library). https://doi.org/10.1007/978-3-319-26485-1_33.
Powles, J., and H. Hodson. 2017. Google DeepMind and Healthcare in an Age of Algorithms. Health and Technology 7 (4): 351–367.
Rajpurkar, P., J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, et al. 2017. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-rays with Deep Learning. [cs, stat]. http://arxiv.org/abs/1711.05225.
Rawls, J. 2005. Political Liberalism, 589. New York: Columbia University Press.
Schermer, B.W. 2011. The Limits of Privacy in Automated Profiling and Data Mining. Computer Law & Security Review 27 (1): 45–52.
Stein, N., and K. Brooks. 2017. A Fully Automated Conversational Artificial Intelligence for Weight Loss: Longitudinal Observational Study Among Overweight and Obese Adults. JMIR Diabetes 2 (2): e28.
Sunstein, C.R., D. Kahneman, D. Schkade, and I. Ritov. 2002. Predictably Incoherent Judgments. Stanford Law Review 54 (6): 1153–1215.
Thaler, R.H. 2000. From Homo Economicus to Homo Sapiens. The Journal of Economic Perspectives 14 (1): 133–141.
Tversky, A., and D. Kahneman. 1986. Rational Choice and the Framing of Decisions. The Journal of Business 59 (4): S251–S278.
Vellido, A. 2019. Societal Issues Concerning the Application of Artificial Intelligence in Medicine. KDD 5 (1): 11–17.
Weng, S.F., J. Reps, J. Kai, J.M. Garibaldi, and N. Qureshi. 2017. Can Machine-Learning Improve Cardiovascular Risk Prediction Using Routine Clinical Data? PLoS ONE 12 (4): e0174944.
Zarsky, T.Z. 2013. Transparent Predictions. University of Illinois Law Review 2013: 1503.
Zarsky, T. 2016. The Trouble with Algorithmic Decisions: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making. Science, Technology and Human Values 41 (1): 118–132.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ploug, T., Holm, S. The right to refuse diagnostics and treatment planning by artificial intelligence. Med Health Care and Philos 23, 107–114 (2020). https://doi.org/10.1007/s11019-019-09912-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11019-019-09912-8