Clinical negligence, a dynamically and complex piece of law, is unprecedentedly tested in the age of machine learning (ML). Increasing use of ML algorithms in diagnosis, treatment planning, and even surgery raises questions about liability when things go wrong.
This literature review explores the use of the res ipsa loquitur doctrine (“the thing speaks for itself”) for claims of clinical negligence related to ML in medicine. Traditional clinical negligence claims need proof of breach of duty of care, causation, and harm.
It is harder to identify problems when a “black box” ML system has negative results, as even its creators do not understand it. The lack of clarity in ML systems may hide negligent parties and prevent patients harmed by clinical error from getting help.
Current literature highlights the challenge of finding the exact source of an error in an ML system. Is it an algorithm issue, a data failure, or a clinician’s mistake? This uncertainty makes it hard to link breach of duty and harm in clinical error cases.
The doctrine of res ipsa loquitur allows an assumption of negligence when an event unlikely to happen without clinical negligence occurs, the defendant had sole control of the cause, and the plaintiff did not contribute to the harm.
Applying res ipsa loquitur to clinical error using ML is subject to several considerations. Firstly, the courts must determine whether a negative outcome from an ML system would, by the nature of things, be the type of event that would not ordinarily occur without negligence.
Understanding the limits of ML algorithms in healthcare is essential, especially concerning responsibility for clinical negligence.
Consider the clinician’s role in assessing injury causes for clinical negligence claims in the ML era. Using res ipsa loquitur requires fair evaluation for accountability and healthcare advancements.
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