Large language models (LLMs) and other generative artificial intelligence tools are moving into radiology workflows, from simplifying reports and assisting documentation to supporting communication with patients. Their promise comes with distinct risks. Outputs can vary for the same prompt, clinically plausible content can be fabricated, and performance may drift as models or integrations change. Safe use requires discipline around how these systems are evaluated, how patient data are protected and how bias is measured and mitigated. Focusing on these pillars helps departments adopt useful applications while maintaining clinical quality, safeguarding confidentiality and preserving trust among clinicians and patients.
Regulation beyond Accuracy
Conventional software as a medical device frameworks suit deterministic tools with stable versions, but generative AI behaves stochastically and is updated frequently. This creates uncertainty about how to classify, evaluate and monitor applications that summarise charts, draft text or combine text and images in multimodal workflows. A recurring boundary issue is when an LLM-enabled feature qualifies as nondevice clinical decision support versus when it becomes a regulated device, particularly if it produces directive outputs or disease-specific assessments.
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Accuracy metrics alone are insufficient. Safe deployment requires attention to reproducibility, robustness to hallucinations and the human factors that shape clinician performance and cognitive load. Similarity measures for text or images may not capture clinical correctness, so repeatability to the same prompt becomes a performance attribute in its own right. As models extend beyond text, evaluation should confirm that generated content aligns with anatomy and clinical context.
A pragmatic route is to differentiate between general-purpose foundation models and the end-user applications built on them. Regulating the application on its observable outputs, with explicit thresholds for repeatability and hallucination risk, offers a workable path while standards evolve. Ongoing dialogue between radiologists, vendors and regulators is needed to reflect stochastic behaviour, version changes and real-world use.
Protecting Patient Data and Usage Transparency
Generative models are trained on large, sometimes opaque corpora, creating risks of incorporating copyrighted or sensitive material. LLMs can memorise snippets and may reproduce them under adversarial prompts, raising concerns about inadvertent disclosure of protected health information. Image-generation systems carry related risks if training images are reproduced too closely.
Using third-party, cloud-hosted models adds constraints. Without appropriate agreements and safeguards, sending nondeidentified clinical text or images externally may be impermissible. Deidentification itself is challenging, as manual efforts are burdensome and automated approaches are imperfect. Where policy permits, locally deployed open-source models or institutionally controlled secure cloud environments can reduce exposure while supporting permissible use cases.
Vendor transparency is a practical safeguard. Teams should seek clear statements on data handling for inputs, outputs and logs, whether data are retained for product improvement and what protections exist against jailbreaks and breaches. Contracts should align with institutional privacy requirements, with informatics and IT partners involved in procurement and oversight. Federated learning can enable cross-site training without moving raw patient data, broadening diversity while preserving confidentiality. Clear boundaries and monitoring are essential so that initially compliant configurations do not drift into noncompliance as models or integrations change.
Recognising and Mitigating Bias
Bias can undermine safety and equity if training data underrepresent particular populations. In text generation, demographic cues within prompts can influence outputs, embedding disparities into explanations or suggested approaches. Image tools may also reflect or amplify stereotypes, creating indirect harms beyond clinical metrics.
Mitigation starts with diverse, well-described datasets for training and evaluation, with demographic metadata that allow subgroup performance assessment. Evidence should include fairness analyses and subgroup results, not just aggregate metrics. Post-implementation surveillance is necessary because performance can drift after deployment, especially if models or parameters are updated.
Practical techniques include federated learning to expand diversity without centralising data, stress-testing with prompt variations to surface differential responses and reinforcement learning with clinician feedback where model access allows. Transparency again matters: radiology teams should ask about dataset composition, completed bias assessments and plans for ongoing monitoring. Patient-facing uses such as report simplification and education require attention to readability, empathy and multilingual performance to support equitable access. Human factors are integral, since automation bias can lead clinicians to overweight AI suggestions even when subgroup performance varies.
Generative AI can help radiology by reducing documentation burden, supporting patient communication and enabling new forms of data synthesis, but safe adoption hinges on three pillars. Regulation must extend beyond accuracy to address reproducibility, hallucination risk and human factors while focusing evaluation on end-user applications. Privacy protections should limit data exposure through secure deployments, clear vendor policies and governance that prevent drift. Fairness demands diverse datasets, explicit subgroup analyses and ongoing monitoring to detect and correct disparities. Centering practice on these principles allows radiology teams to capture value from generative AI while protecting patients and sustaining clinical trust.
Source: Radiology
Image Credit: iStock
