Abstract
The growth of Artificial Intelligence (AI) technologies in health care is driving a growing recognition among policymakers, businesses and researchers that there is a need for policies to address certain potential consequences of AI innovation. In this chapter, we provide insight on several policy implications and challenges relating to the impact of AI on accuracy, fairness and transparency, data privacy and consent, accountability, and workforce disruption. These issues include: monitoring of accuracy; minimizing bias and encouraging transparency, ensuring appropriate use, assessment of who is receiving the information and how it is being used, protecting privacy through data protection requirements, enactment of laws that defines accountabilities, establishment of policies for labour disruption; implementation of professional standards and codes of conduct; adapting educational training for clinicians; and determining what technologies will be insured and funded. Additional complexities arise when AI crosses geographic boundaries. The design, development and implementation of policy and regulation should be in conjunction with a diversity of stakeholders including product developers, researchers, patients, health care providers and policymakers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adler MJ (1991) Desires right & wrong: the ethics of enough. Macmillan Publishing Company
Allen B (2019) The Role of the FDA in ensuring the safety and efficacy of artificial intelligence software and devices. J Am Coll Radiol 16(2):208–210
An Overview of Clinical Applications of Artificial Intelligence (2018) CADTH, Ottawa (CADTH issues in emerging health technologies; issue 174). Retrieved from: https://www.cadth.ca/sites/default/files/pdf/eh0070_overview_clinical_applications_of_AI.pdf
Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25:954–961
Bathaee Y (2018) The artificial intelligence black box and the failure of intent and causation. Harvard J Law Technol 31(2). https://jolt.law.harvard.edu/assets/articlePDFs/v31/The-Artificial-Intelligence-Black-Box-and-the-Failure-of-Intent-and-Causation-Yavar-Bathaee.pdf. Accessed 13 Sep 2019
Calo R (2017) Artificial intelligence policy: a primer and roadmap. https://lawreview.law.ucdavis.edu/issues/51/2/Symposium/51-2_Calo.pdf. Accessed 01 Sept 2019
Caruana R et al (2015) Intelligible models for healthcare. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1721–30. http://people.dbmi.columbia.edu/noemie/papers/15kdd.pdf
Deane M (2018) AI and the future of privacy. https://towardsdatascience.com/ai-and-the-future-of-privacy-3d5f6552a7c4. Accessed 15 Oct 2019
Doern GB, Phidd RW (1992) Canadian public policy: Ideas, structure, process, 2nd edn. Nelson, Toronto, ON
Duhigg C (2012) How company learn your secrets. The New York Times Magazine. https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html. Accessed 12 Oct 2019
Dutton T (2018) An overview of national AI strategies. Retrieved from https://medium.com/politics-ai/an-overview-of-national-ai-strategies-2a70ec6edfdOECD (2019), Artificial Intelligence in Society, OECD Publishing, Paris. https://doi.org/10.1787/eedfee77-en
European Commission (2019) Ethics guidelines for trustworthy AI. (https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai). Accessed 12 Oct 2019
Forcier MB, Gallois H, Mullan S, Joly Y (2019) Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers? J Law Biosci, pp 317–335. https://doi.org/10.1093/jlb/lsz013
Jaremko JL, Azar M, Bromwich R, Lum A, Alicia Cheong LH, Gilbert M et al (2019) Canadian association of radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J 70(2):107–118. https://doi.org/10.1016/j.carj.2019.03.001. Epub 5 Apr 2019
Jenkins WI (1978) Policy analysis: a political and organizational perspective. Martin’s Press, New York, St
Lee NT, Resnick P, Barton G (2019) Algorithmic bias detection mitigation: Best practices and policies to reduce consumer harm. https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/. Accessed 20 Sept 2019
Mesko B (2017) A guide to artificial intelligence in healthcare
Mowat Centre (2019) Governing the future: creating standards for artificial intelligence and algorithms. https://munkschool.utoronto.ca/mowatcentre/governing-the-future-creating-standards-for-artificial-intelligence-and-algorithms/. Accessed 12 Oct 2019
Nuffield Council on Bioethics (2018) Artificial intelligence in healthcare and research. https://nuffieldbioethics.org/wp-content/uploads/Artificial-Intelligence-AI-in-healthcare-and-research.pdf. Accessed 30 Oct 2019
Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453. https://doi.org/10.1126/science.aax2342
OECD (2019) Artificial intelligence in society. OECD Publishing, Paris. https://doi.org/10.1787/eedfee77-en
Pesapane F, Volonté C, Codari, M, Sardanelli F(2018). Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imag 9(5):745–753
Price W, Nicholson II (2017) Artificial intelligence in health care: applications and legal implications. SciTech Lawyer 14 (1). https://repository.law.umich.edu/cgi/viewcontent.cgi?article=2932&context=articles
Reddy S, Allan, S, Coghlan S, Cooper P (2019) A governance model for the application of AI in health Care. J Am Med Inf Assoc, pp 1–7. https://doi.org/10.1093/jamia/ocz192
Reuters T (2018) All John Hancock life insurance policies to include fitness incentives. CBC. https://www.vmmed.com/blog/all-john-hancock-life-insurance-policies-to-include-fitness-incentives/. Accessed 12 Oct 2019
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G et al (2019) Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI J Natl Cancer Inst 111(9):916–922
Scherer MU (2016). Regulating artificial intelligence systems: risks, challenges, competencies and strategies. Harvard J Law Technol 29(2). http://jolt.law.harvard.edu/articles/pdf/v29/29HarvJLTech353.pdf. Accessed 12 Nov 2019
Secretary of State for Health and Social Care (2019) Topol review: preparing the healthcare workforce to deliver the digital future. https://pharmafield.co.uk/healthcare/the-topol-review-preparing-the-healthcare-workforce-to-deliver-the-digital-future/. Accessed 15 Oct 2019
Sharkey A, Sharkey N (2012) Granny and the robots: ethical issues in robot care for the elderly. Ethics Inf Technol 14:27–40
Sullivan HR, Schweikart SJ (2019) Are current tort liability doctrines adequate for addressing injury caused by AI. AMA J Ethics 21(2): E160–166. https://doi.org/10.1001/amajethics.2019.160
SVayena E, Blasimme A, Cohen IG (2018) Machine learning in medicine: addressing ethical challenges. PLoS Med 15(11): e1002689. https://doi.org/10.1371/journal.pmed.1002689
Telegraph (2016, 4 May) Royal free breached UK data law in 1.6m patient deal with Googl’s DeepMind. https://www.theguardian.com/technology/2016/may/04/google-deepmind-access-healthcare-data-patients
Thierer A, O'Sullivan A, Russel R (2017) Artificial intelligence and public policy. Mercatus Research Paper. Available via https://www.mercatus.org/system/files/thierer-artificial-intelligence-policy-mr-mercatus-v1.pdf
Virtue T, Rainey J (2015) HCISPP study guide. Syngress Publishing
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Aggarwal, M., Gingras, C., Deber, R. (2021). Artificial Intelligence in Healthcare from a Policy Perspective. In: Househ, M., Borycki, E., Kushniruk, A. (eds) Multiple Perspectives on Artificial Intelligence in Healthcare. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-67303-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-67303-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67302-4
Online ISBN: 978-3-030-67303-1
eBook Packages: MedicineMedicine (R0)