We are in a stage of transition as artificial intelligence (AI) is increasingly being used in healthcare across the world. Transitions offer opportunities compounded with difficulties. It is universally accepted that regulations and the law can never keep up with the exponential growth of technology. This paper discusses liability issues when AI is deployed in healthcare. Ever-changing, futuristic, user friendly, uncomplicated regulatory requirements promoting compliance and adherence are needed. Regulators have to understand that software itself could be a software as a medical device (SaMD). Benefits of AI could be delayed if slow, expensive clinical trials are mandated. Regulations should distinguish between diagnostic errors, malfunction of technology, or errors due to initial use of inaccurate/inappropriate data as training data sets. The sharing of responsibility and accountability when implementation of an AI-based recommendation causes clinical problems is not clear. Legislation is necessary to allow apportionment of damages consequent to malfunction of an AI-enabled system. Product liability is ascribed to defective equipment and medical devices. However, Watson, the AI-enabled supercomputer, is treated as a consulting physician and not categorised as a product. In India, algorithms cannot be patented. There are no specific laws enacted to deal with AI in healthcare. DISHA or the Digital Information Security in Healthcare Act when implemented in India would hopefully cover some issues. Ultimately, the law is interpreted contextually and perceptions could be different among patients, clinicians and the legal system. This communication is to create the necessary awareness among all stakeholders.
Fomenko A, Lozano A. Artificial intelligence in neurosurgery. UTMJ. 2019;19–21.
3.
Amisha, Malik P, Pathania M, Rathaur V. Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care. 2019;8(7):2328.
4.
Hedges L. The future of AI in healthcare. Software advice. 2020;
5.
Buch VH, Ahmed I, Maruthappu M. Artificial intelligence in medicine: current trends and future possibilities. British Journal of General Practice. 2018;68(668):143–4.
6.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine. 2019;17(1).
7.
Report A. Artificial intelligence for authentic engagement. 18AD;
8.
Topol EJ. Welcoming new guidelines for AI clinical research. Nature Medicine. 2020;26(9):1318–20.
9.
Google’s medical AI was super accurate in a lab. Real life was a different story. MIT Technological Review. 18AD;
10.
Oakden-Rayner L, Dunnmon J, Carniero G, Re C. Hidden Stratification causes clinically meaningful failures in machine learning for medical imaging. 2019;
11.
So why don’t patients trust it? Harvard Business Review. 18AD;
12.
LaRosa E, Danks D. Impacts on Trust of Healthcare AI. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. ACM; 2018.
13.
Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA Journal of Ethics. 2019;21(2):E121-124.
14.
Report A. Artificial intelligence in healthcare. Academy of Medical Royal Colleges. 2019;
15.
Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare Journal. 2019;6(2):94–8.
16.
Asan O, Bayrak AE, Choudhury A. Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. Journal of Medical Internet Research. 2020;22(6):e15154.
17.
USA. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) -discussion paper and request for feedback. 18AD;
18.
Price W, Ii. Artificial intelligence in health care: applications and legal implications. The SciTech Lawyer. 2017;
19.
Narsana B, Lokhandwala M. Artificial intelligence in healthcare: applications and legal implications. 18AD;
20.
Muoio D. Clinical AI’s limitations: some are short-term, others are unavoidable.
21.
Healthcare P. 18AD;
22.
Froomkin AM, Kerr IR, Pineau J. When AIs Outperform Doctors: The Dangers of a Tort-Induced Over-Reliance on Machine Learning and What (Not) to Do About it. SSRN Electronic Journal. 2018;
23.
Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare. Elsevier; 2020. p. 295–336.
24.
Hoffman S. Artificial intelligence in medicine raises legal and ethical concerns. The Conversation. 2019;
25.
Lupton M. Some ethical and legal consequences of the application of artificial intelligence in the field of medicine. Trends in Medicine. 2018;18(4).
26.
Singh S. The medico-legal discussion of the digital and AI revolution in the Indian healthcare industry. 2020;
27.
Tabriz A. Medico-legal perils of artificial intelligence and deep learning. Data Driven Investor. 2019;
28.
Williams D. An ode to Osler: a physician profile. Resident Student Organization. 2017;
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