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Editor's E-Note
Large language models (LLMs) are increasingly being used in radiology to assist with reports, decision support, and communicating with patients, among other uses. Although they offer many potential benefits, they are not without risk, however. A recent report highlights some vulnerabilities that radiology departments need to be aware of. Read on to learn more.
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— Dave Yeager, editor |
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LLMs Pose Potential Cybersecurity Threats in Radiology
In a new special report, researchers address the cybersecurity challenges of large language models (LLMs) and the importance of implementing security measures to prevent LLMs from being used maliciously in the health care system. The special report was published recently in Radiology: Artificial Intelligence, a journal of the RSNA.
LLMs, such as OpenAI’s GPT-4 and Google’s Gemini, are a type of AI that can understand and generate human language. LLMs have rapidly emerged as powerful tools across various health care domains, revolutionizing both research and clinical practice. These models are being employed for diverse tasks such as clinical decision support, patient data analysis, drug discovery, and enhancing communication between health care providers and patients by simplifying medical jargon. An increasing number of health care providers are exploring ways to integrate advanced language models into their daily workflows.
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“While integration of LLMs in health care is still in its early stages, their use is expected to expand rapidly,” says lead author Tugba Akinci D’Antonoli, MD, neuroradiology fellow in the department of diagnostic and interventional neuroradiology at University Hospital Basel in Switzerland. “This is a topic that is becoming increasingly relevant and makes it crucial to start understanding the potential vulnerabilities now.”
LLM integration into medical practice offers significant opportunities to improve patient care, but these opportunities are not without risk. LLMs are susceptible to security threats and can be exploited by malicious actors to extract sensitive patient data, manipulate information, or alter outcomes using techniques such as data poisoning or inference attacks.
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