References
1. NHS England. Information Governance Guidance: Artificial Intelligence [Internet]. NHS England - Transformation Directorate; 2022 [cited 2022 Nov 3]. Available from: https://transform.england.nhs.uk/information-governance/guidance/artificial-intelligence/
2. Bainbridge L. Ironies of automation. In: Johannsen G, Rijnsdorp JE, editors. Analysis, Design and Evaluation of Man–Machine Systems [Internet]. Pergamon; 1983 [cited 2023 Feb 22]. p. 129–35. Available from: https://www.sciencedirect.com/science/article/pii/B9780080293486500269
3. Engineering Analysis 22-002 [Internet]. National Highway Traffic Safety Administation, Office of Defects Investigation; 2022 [cited 2022 Nov 3]. Available from: https://static.nhtsa.gov/odi/inv/2022/INOA-EA22002-3184.PDF
4. Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bulletin of the World Health Organization. 2020 Feb;98(4):251–6.
5. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Internal Medicine. 2018 Nov 1;178(11):1544–7.
6. McDermid JA, Jia Y, Porter Z, Habli I. Artificial intelligence explainability: the technical and ethical dimensions. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2021 Aug 16;379(2207):20200363.
7. Chesterman S. Artificial intelligence and the limits of legal personality. ICLQ. 2020;69(4):819–44.
8. Smith H, Fotheringham K. Artificial intelligence in clinical decision-making: Rethinking liability. Medical Law International. 2020 Jun 1;20(2):131–54.
9. Wilsher v Essex Area Health Authority [1987] QB 730 (CA). 1987.
10. Junior v McNicol. Times Law Reports, March 26 1959. 1959.
11. Armitage M, editor. Chapter 10: Persons Professing Some Special Skill. In: Charlesworth & Percy on Negligence. 15th ed. London: Sweet & Maxwell; p. 10–147. (Common Law Library).
12. Burton S, Habli I, Lawton T, McDermid J, Morgan P, Porter Z. Mind the gaps: Assuring the safety of autonomous systems from an engineering, ethical, and legal perspective. Artificial Intelligence. 2020 Feb 1;279:103201.
13. Heywood R. Systemic Negligence and NHS Hospitals: An Underutilised Argument. King’s Law Journal. 2021 Sep 2;32(3):437–65.
14. Morgan, Phillip. Chapter 6: Tort Law and Artificial Intelligence – Vicarious Liability. In: Lim E, Morgan P, editors. The Cambridge Handbook of Private Law and Artificial Intelligence. Cambridge University Press;
15. Abbott R. The Reasonable Robot: Artificial Intelligence and the Law [Internet]. Cambridge: Cambridge University Press; 2020 [cited 2023 Feb 22]. Available from: https://www.cambridge.org/core/books/reasonable-robot/092E62F0087270F1ADD9F62160F23B5A
16. Bjerring JC, Busch J. Artificial Intelligence and Patient-Centered Decision-Making. Philos Technol. 2021 Jun 1;34(2):349–71.
17. Birch J, Creel KA, Jha AK, Plutynski A. Clinical decisions using AI must consider patient values. Nat Med [Internet]. 2022 Jan 31 [cited 2022 Feb 1]; Available from: https://www.nature.com/articles/s41591-021-01624-y
18. Jia Y, Mcdermid JA, Lawton T, Habli I. The Role of Explainability in Assuring Safety of Machine Learning in Healthcare. IEEE Transactions on Emerging Topics in Computing. 2022;1–1.
19. Mittelstadt B, Russell C, Wachter S. Explaining Explanations in AI. In: Proceedings of the Conference on Fairness, Accountability, and Transparency [Internet]. New York, NY, USA: Association for Computing Machinery; 2019 [cited 2023 Feb 22]. p. 279–88. (FAT* ’19). Available from: https://doi.org/10.1145/3287560.3287574
20. Epstein NE. Multidisciplinary in-hospital teams improve patient outcomes: A review. Surgical Neurology International. 2014;5(Suppl 7):S295.
21. Council Directive 85/374/EEC of 25 July 1985 on the approximation of the laws, regulations and administrative provisions of the Member States concerning liability for defective products [Internet]. OJ L Jul 25, 1985. Available from: http://data.europa.eu/eli/dir/1985/374/oj/eng
Acknowledgements
This work was supported by The MPS Foundation Grant Programme. The MPS Foundation was established to undertake research, analysis, education and training to enable healthcare professionals to provide better care for their patients and improve their own wellbeing. To achieve this, it supports and funds research across the world that will make a difference and can be applied in the workplace. The work was also supported by the Engineering and Physical Sciences Research Council (EP/W011239/1).
Conflicts of interest
TL has received an honorarium for a lecture on this topic from Al Sultan United Medical Co and is head of clinical artificial intelligence at Bradford Teaching Hospitals NHS Foundation Trust, and a potential liability sink
All other authors report no conflicts of interest
Authors’ contributions
TL, ZP, IH - conceptualisation, funding acquisition, writing - original draft & review & editing, analysis, visualisation
PM - writing - original draft, analysis, visualisation, writing - review & editing
AC, NH, JI, YJ, VS - analysis, visualisation, writing - review & editing