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Liability Determination in AI-driven Medical Practices

Liability Determination in AI-driven Medical Practices

by Rajnandan Gadhi on May 31 2023 1:36 PM
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Highlights:
  • Liability in AI-driven medical practices requires clear accountability between developers, manufacturers, and healthcare professionals
  • Addressing bias in training data is essential to mitigate potential discriminatory outcomes and hold AI organizations liable
  • Regulatory frameworks must evolve to establish guidelines and standards addressing liability concerns
As Artificial Intelligence (AI) and machine learning (ML) become increasingly integrated into medicine, there is a concern that inaccuracies in algorithms could result in patient harm and medical liability. Previous efforts have primarily focused on medical malpractice, but it is important to recognize that the AI ecosystem involves various stakeholders beyond just clinicians. The current liability frameworks are insufficient to promote both the safe implementation of AI in clinical practice and the disruptive innovation it offers.
Firstly, the area of tort liability concerning AI is still in the process of development. As of now, there have not been specific court cases directly addressing liability for AI in healthcare, mainly because the technology itself is relatively new and is still being implemented. Consequently, it is necessary to examine general principles of tort law and their potential application in this context (1 Trusted Source
Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation

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).

Secondly, establishing causation can be particularly difficult in cases involving AI-related torts. Proving the cause of an injury is already challenging in the medical field, where outcomes often have a probabilistic nature rather than being solely deterministic. When AI models, which are frequently non-intuitive and occasionally difficult to comprehend, are introduced into the equation, demonstrating causation is likely to become even more complex.

Factors Affecting AI Liability in Medical Practice

So let us understand a few key points which will help us in determining medical liability when AI technology is used:

Design and Development:

The liability for AI systems in medicine starts with the developers and manufacturers. If a flaw or bias in the AI algorithm leads to harm or incorrect diagnoses, the responsibility may lie with the developers or the organization that deployed the AI system. It is crucial for developers to ensure the accuracy, reliability, and safety of their AI algorithms and validate them through rigorous testing and evaluation.

Training Data and Bias:

Organizations responsible for collecting and curating training data used by AI algorithms can be held liable if the data is biased or incomplete, leading to discriminatory outcomes. To mitigate this, efforts must be made to ensure representative and diverse datasets. For instance, if an AI algorithm for diagnosing skin conditions is trained on data that primarily includes lighter skin tones, it may lead to misdiagnosis or under diagnosis in individuals with darker skin, and the liability may fall on the organization responsible for the biased dataset.

Clinical Decision-Making:

Although AI algorithms provide recommendations in clinical decision-making, the ultimate responsibility for final decisions lies with healthcare professionals. They are accountable for reviewing and validating AI suggestions before making treatment decisions. For instance, an AI algorithm that suggests a medication dosage should not override a healthcare professional's judgment based on individual patient factors and other clinical considerations.

Informed Consent and Patient Communication:

Healthcare providers must inform patients about the involvement of AI technology in their care and communicate any associated risks or limitations. This ensures patient understanding, allows them to make informed decisions, and helps manage expectations. For example, if a patient is undergoing a surgical procedure assisted by a robotic AI system, they should be informed about the role of the AI and potential risks or benefits.

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Regulatory Frameworks:

Governments and regulatory bodies play a crucial role in establishing guidelines and frameworks to address liability concerns in AI-driven medical practices. These frameworks need to evolve alongside technological advancements to ensure accountability and patient safety. For instance, regulatory bodies can establish standards for AI algorithms' validation, data privacy, and informed consent, ensuring that healthcare providers and developers adhere to ethical and legal requirements.

The Need for a Proper Policy Framework

To establish a more balanced liability system, several policy options can be considered. These options include modifying the standard of care, implementing appropriate insurance mechanisms, providing indemnification, developing special or no-fault adjudication systems, and introducing regulation specific to AI. By adopting such liability frameworks, it becomes possible to facilitate the safe and efficient integration of AI and ML in clinical care while also encouraging innovation.

Reference:
  1. Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation - (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452365/)

Source-Medindia


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