Donoghue v Stevenson [1932] UKHL 100
O’Connor v The Pennine Acute Hospitals NHS Trust [2015] EWCA Civ 1244 at para. 60 per Jackson LJ.
Computer Associates UK Ltd v Software Incubator Ltd. [2018] EWCA Civ 518
Colin Gee & ors v DePuy International Ltd [2018] EWHC 1208 (QB)
Bolam v Friern Hospital Management Committee [1957] 1 WLR 582.
Bolitho v City and Hackney Health Authority [1998] AC 232.
Cassidy v Ministry of Defence [1951] 2 KB 343
https://www.moorfields.nhs.uk/content/breakthrough-ai-technology-improve-care-patients. Accessed 17 February 2020.
https://www.moorfields.nhs.uk/content/latest-updates-deepmind-health. Accessed 17 February 2020,
Abbott, R. (2018). The reasonable computer: Disrupting the paradigm of tort liability. George Washington Law Review, 86, 1–45.
Google Scholar
Accident Compensation Act 2001.
Accident Compensation Corporation (ACC) website: https://www.acc.co.nz/about-us/how-levies-work/what-your-levies-pay/ Accessed 7 January 2021.
Adamson, A. S., & Smith, A. (2018). Machine learning and health care disparities in dermatology. JAMA dermatology, 154(11), 1247–1248.
Article
Google Scholar
Art. 22 General Data Protection Regulation (Regulation (EU) 2016/679) – Automated individual decision-making, including profiling , available from: https://gdpr-info.eu/art-22-gdpr/ Accessed 17 February 2020
Article 29 Data Protection Working Party. (2018). Guidelines on Automated individual decision-making and Profiling for the purposes of Regulation 2016/679 (wp251rev.01) p. 21, 22. Available from: https://ec.europa.eu/newsroom/article29/item-detail.cfm?item_id=612053 Accessed 17 February 2020
Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., & Utikal, J. S. (2019). Deep neural networks are superior to dermatologists in melanoma image classification. European Journal of Cancer, 119, 11–17. https://doi.org/10.1016/j.ejca.2019.05.023
Article
PubMed
Google Scholar
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N (2015). Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-Day Readmission, KDD ’15, mmxv-, 1721–1730 https://doi.org/https://doi.org/10.1145/2783258.2788613
Chilamkurthy, S., Ghosh, R., Tanamala, S., et al. (2018). Deep learning algorithms for detection of critical findings in head ct scans: A retrospective study. The Lancet, 392(10162), 2388–2396. https://doi.org/10.1016/S0140-6736(18)31645-3
Article
Google Scholar
Consumer Protection Act 1987
Criado-Perez, C. (2019). Invisible women: exposing data bias in a world designed for men. London: Chatto and Windus.
Google Scholar
Department of Health. (2003). Making Amends: A consultation paper setting out proposals for reforming the approach to clinical negligence in the NHS. London: Department of Health.
Google Scholar
Dickson, K., Hinds, K., Burchett, H., Brunton, G., Stansfield, C., Thomas, J. (2016). No-Fault compensation schemes: a rapid realist review. London: EPPI-Centre, Social Science Research Unit, UCL Institute of Education, University College London. ISBN:978–1–907345–96–8.
Dodd, A. (2019). ‘I’m sorry Dave. I’m afraid I can’t do that’: Legal liability in the age of Artificial Intelligence. https://www.fieldfisher.com/en/insights/i%E2%80%99m-sorry,-dave-i%E2%80%99m-afraid-i-can%E2%80%99t-do-that%E2%80%9D-legal. Accessed 17 February 2020.
E.g. Wilsher v Essex Area Health Authority [1988] AC 1074
E.g. Fairchild v Glenhaven Funeral Services Ltd [2003] 1 AC 32
European Commission (2020) Report from the Commission to the European Parliament, the Council and the European Economic and Social Committee: Report on the safety and liability implications of Artificial Intelligence, the Internet of Things and Robotics. COM/2020/64 final. EUR-Lex website. https://ec.europa.eu/info/files/commission-report-safety-and-liability-implications-ai-internet-things-and-robotics_en. Accessed 4 February 2021
Evans, B. J., Pasquale, F. A. (2020). Product Liability Suits for FDA-Regulated AI/ML Software. In: I. Glenn Cohen, Timo Minssen, W. Nicholson Price II, Christopher Robertson, Carmel Shachar (Eds.), The Future of Medical Device Regulation: Innovation and Protection. Cambridge University Press, 2021 forthcoming).
Farrell, A-M., Devaney, S., and Dar, A. (January 20, 2010). No-Fault Compensation Schemes for Medical Injury: A Review. Scottish Government Social Research. https://doi.org/10.2139/ssrn.2221836 (accessed 17 February 2020)
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., & Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342. https://doi.org/10.1038/s41591-018-0107-6
CAS
Article
PubMed
Google Scholar
For a summary of some of the literature see: Flis, V. (2016). No Fault Compensation for Medical Injuries. Medicine, Law & Society, 9(2), 73–84.
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121–127.
Article
Google Scholar
Gupta, N., Gupta, D., Khanna, A., Rebouças Filho, P. P., & de Albuquerque, V. H. C. (2019). Evolutionary algorithms for automatic lung disease detection. Measurement. https://doi.org/10.1016/j.measurement.2019.02.042
Article
Google Scholar
Hardt, M., (2014). How big data is unfair. https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de. Accessed 14 January 2021.
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P., & Larochelle, H. (2017). Brain tumor segmentation with deep neural networks. Medical Image Analysis, 35, 18–31. https://doi.org/10.1016/j.media.2016.05.004
Article
PubMed
Google Scholar
Houssami, N., Kirkpatrick-Jones, G., Noguchi, N., & Lee, C. I. (2019). Artificial intelligence (ai) for the early detection of breast cancer: A scoping review to assess ai’s potential in breast screening practice. Expert Review of Medical Devices, 16(5), 351–362. https://doi.org/10.1080/17434440.2019.1610387%3e
CAS
Article
PubMed
Google Scholar
Howells, G., Twigg-Flesner, C., & Willett, C. (2017). Product liability and digital products. In T. E. Synodinou, P. Jougleux, C. Markou, & T. Prastitou (Eds.), EU internet law Regulation and enforcement (pp. 183–195). Cham: Springer.
Chapter
Google Scholar
Kachalia, A. B., Mello, M. M., Brennan, T. A., & Studdert, D. M. (2008). Beyond negligence: Avoidability and medical injury compensation. Social Science and Medicine, 66, 387–402.
Article
Google Scholar
Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29.
Article
Google Scholar
Komorowski, M., Celi, L. A., Badawi, O., et al. (2018). The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24(11), 1716–1720. https://doi.org/10.1038/s41591-018-0213-5
CAS
Article
PubMed
Google Scholar
Lashbrook, A., (2018). AI-driven dermatology could leave dark skinned patients behind. The Atlantic. https://www.theatlantic.com/health/archive/2018/08/machine-learning-dermatology-skin-color/567619/. Accessed 14 January 2021.
Laurie, G. T., Harmon, S. H. E., & Dove, E. S. (2019). [11th Edition] Mason and McCall Smith’s Law and Medical Ethics. Oxford: OUP.
Book
Google Scholar
Ledford, H., (2019). Millions of black people affected by racial bias in health-care algorithms. https://www.nature.com/articles/d41586-019-03228-6. Accessed 14 January 2021.
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., & Denniston, A. K. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271–e297.
Article
Google Scholar
Marcus, G., & Davis, E. (2019). Reebooting AI: Building artificial intelligence we can trust. New York: Penguin Random House.
Google Scholar
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data and Society, 3(2). https://papers.ssrn.com/abstract=2909885. Accessed 14 January 2021.
Moore, J., & Mello, M. M. (2017). Improving reconciliation following medical injury: A qualitative study of responses to patient safety incidents in New Zealand. BMJ Qual Saf, 26(10), 788–798.
Article
Google Scholar
NHSX. (October 2019). Artificial Intelligence: How to get it right. Putting Policy into practice for safe data-driven innovation in health and care. https://www.nhsx.nhs.uk/assets/NHSX_AI_report.pdf Accessed 17 February 2020
Obermeyer, Z., Powers, B., Vogeli, C., et al. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
CAS
Article
Google Scholar
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring Fairness in Machine Learning to Advance Health Equity. Annals of Internal Medicine, 169(12), 866–872.
Article
Google Scholar
Schneeberger, D., Stöger, K., & Holzinger, A. (2020). The European legal framework for medical AI. International Cross-Domain Conference for Machine Learning and Knowledge Extraction (pp. 209–226). Cham: Springer.
Chapter
Google Scholar
Schönberger, A. (2019). Artificial intelligence in healthcare: A critical analysis of the legal and ethical implications. International Journal of Law and Information Technology, 27(2), 171–203.
Article
Google Scholar
Sim, Y., Chung, M. J., Kotter, E., Yune, S., Kim, M., Do, S., & Choi, B. W. (2019). Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology. https://doi.org/10.1148/radiol.2019182465
Article
PubMed
Google Scholar
Strubell, E., Ganesh, A., McCallum A (2019). Energy and Policy Considerations for Deep Learning in NLP. ArXiv: 1906. 02243 [Cs] http://arxiv.org/abs/1906.02243. Accessed 16 November 2019.
Sullivan, H. R., & Schweikart, S. J. (2019). Are current tort liability doctrines adequate for addressing injury caused by AI? AMA Journal of Ethics, 21(2), 160–166.
Article
Google Scholar
Sumption Lord. (2018). Abolishing personal injuries law. PN, 34(3), 113–121.
Google Scholar
Topol, E. (2019). Deep medicine: how artificial intelligence can make healthcare human again. UK: Hachette.
Google Scholar
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
CAS
Article
Google Scholar
Wallis, K. (2013). New Zealand’s 2005 ‘no-fault’ compensation reforms and medical professional accountability for harm. New Zealand Medical Journal, 126(1371), 33–44.
Google Scholar
Watson, K., & Kottenhagen, R. (2018). Patients’ rights, medical error and harmonisation of compensation mechanisms in europe. European Journal of Health Law, 25, 1–23.
Article
Google Scholar
Whittaker, M., Alper, M., Bennett, C.L., et al. (2019). Disability, Bias and AI. https://ainowinstitute.org/disabilitybiasai-2019.pdf. Accessed 14 January 2021.
Zenor, J. (2018). Endowed by their creator with certain unalienable rights: The future rise of civil rights for artificial intelligence. Savannah Law Review, 5, 115.
Google Scholar