AI in Clinical Decision Support

What Are the Liability Issues with AI Clinical Recommendations?

· 4 min read

What Are the Liability Issues with AI Clinical Recommendations?

By Rasit Dinc

Artificial intelligence (AI) is rapidly transforming the healthcare landscape, offering the potential to improve diagnostic accuracy, streamline workflows, and reduce clinician burnout. However, the increasing integration of AI into clinical decision-making also raises significant legal and ethical questions, particularly concerning liability when these systems make mistakes. As AI-powered tools become more sophisticated and autonomous, it is crucial for health professionals to understand the complex liability issues at play.

Under the current legal framework in the United States, the burden of liability for medical errors largely falls on the shoulders of clinicians. The legal standard is that of a “reasonable physician under similar circumstances” [1]. This means that even when an AI system provides a faulty recommendation that leads to patient harm, the physician who follows that recommendation is likely to be held responsible. The law, as it stands, does not recognize the AI as a legal entity that can be held liable, nor does it have a clear mechanism for distributing liability among the various actors involved in the development and deployment of AI systems.

This places clinicians in a difficult position. They are increasingly encouraged to use AI tools to enhance patient care, yet they bear the full legal risk if the technology fails. This is in stark contrast to other high-stakes industries, such as aviation, where liability for automation failures is often distributed among pilots, manufacturers, and maintenance providers [3].

The Nature of AI Errors and the Challenge of Automation Bias

AI systems in healthcare can make several types of errors that can have serious consequences for patients. These include:

These errors are not always easy to detect, especially for busy clinicians who may be dealing with a high volume of patients. This can lead to a phenomenon known as “automation bias,” where clinicians become overly reliant on AI-generated information and are less likely to scrutinize it for errors [2]. Over time, this can erode clinical judgment and increase the risk of medical mistakes.

To address the liability challenges posed by AI in healthcare, there is a growing consensus that legal and ethical frameworks need to be updated. One potential model is the European Union’s AI Liability Directive, which aims to establish a more equitable distribution of liability for AI-related harm [3]. This could involve holding AI developers and manufacturers accountable for flaws in their algorithms, particularly if they fail to meet certain safety and performance standards.

From an ethical perspective, it is also important to consider the moral implications of relying on AI in clinical decision-making. While AI can be a valuable tool, it should not replace the human element of medicine. The fiduciary relationship between a physician and patient is built on trust, empathy, and shared decision-making, and these are qualities that AI cannot replicate [1].

Conclusion

The integration of AI into clinical practice holds immense promise, but it also presents significant liability challenges that must be addressed. The current legal framework, which places the primary burden of liability on clinicians, is inadequate for the age of AI. A more comprehensive approach is needed that distributes responsibility more equitably among all stakeholders, including AI developers, manufacturers, and healthcare organizations. By proactively addressing these liability issues, we can ensure that AI is used in a safe, effective, and ethical manner that ultimately benefits both patients and providers.

References

[1] Cestonaro, C., Delicati, A., Marcante, B., Caenazzo, L., & Tozzo, P. (2023). Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review. Frontiers in Medicine, 10, 1305756. https://pmc.ncbi.nlm.nih.gov/articles/PMC10711067/

[2] Gerke, S., Simon, D. A., & Roman, B. R. (2025). Liability Risks of Ambient Clinical Workflows With Artificial Intelligence for Clinicians, Hospitals, and Manufacturers. JCO Oncology Practice, OP-24-01060. https://ascopubs.org/doi/10.1200/OP-24-01060

[3] Helzer, E., & Lee, S. (2025, June 26). Fault lines in health care AI – Part two: Who’s responsible when AI gets it wrong? Johns Hopkins Carey Business School. https://carey.jhu.edu/articles/fault-lines-health-care-ai-part-two-whos-responsible-when-ai-gets-it-wrong

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