Artificial intelligence could soon assist patients in interpreting complex medical scan results, enhancing comprehension without sacrificing clinical accuracy, according to a significant study conducted by the University of Sheffield. The research indicates that AI systems, including ChatGPT, could make radiology reports for X-rays, CT, and MRI scans almost twice as easy to understand for patients.
The study revealed that the reading level of these reports could decrease from a university level to one akin to that of a school pupil aged 11 to 13. This newfound clarity could significantly benefit both patients and healthcare systems by alleviating anxiety and confusion, thereby improving health equity for individuals with lower health literacy and those who speak English as a second language. Furthermore, it could allow clinicians to concentrate more on treatment and care decisions instead of deciphering complex jargon.
Researchers evaluated 38 studies published between 2022 and 2025, encompassing more than 12,000 radiology reports that had been simplified using AI tools. These modified reports were then assessed by patients, members of the public, and clinicians to measure both patient understanding and clinical accuracy. Traditionally, radiology reports are crafted with a medical audience in mind, often laden with technical language that can confuse patients. However, with the advent of patient-centered care initiatives—such as the NHS App—and new policies that mandate increased transparency, access to these reports has grown significantly.
Dr. Samer Alabed, the lead author of the study and a Senior Clinical Research Fellow at the University of Sheffield, emphasized the issue with the current format of radiology reports. “The fundamental issue with these reports is they’re not written with patients in mind,” he said. “They are often filled with technical jargon and abbreviations that can easily be misunderstood, leading to unnecessary anxiety, false reassurance, and confusion.” He noted that patients with lower health literacy or those for whom English is a second language are particularly disadvantaged by these reports. This often leads clinicians to spend valuable appointment time explaining terminology instead of focusing on care, suggesting that even minor time savings could yield significant benefits across the NHS.
While most doctors who reviewed the AI-simplified reports found them to be accurate and complete, about one percent contained errors, such as incorrect diagnoses. This highlights the promising nature of AI in this context, while also underscoring the necessity for careful oversight. Notably, none of the 38 studies examined were conducted within the UK or NHS settings, a gap that Dr. Alabed’s team aims to address in future research.
“This research has highlighted several key priorities,” Dr. Alabed stated. “The most important is the need for real-world testing in NHS clinical workflows to properly assess safety, efficiency, and patient outcomes.” He advocates for human oversight models, where clinicians would review and approve AI-generated explanations before they are shared with patients. “Our long-term goal is not to replace clinicians, but to support clearer, kinder, and more equitable communication in healthcare,” he added.
The study reflects the University of Sheffield’s commitment to transforming innovative ideas into impactful solutions, showcasing a collective ambition to enhance patient care through technology. As AI continues to evolve, the integration of these systems into medical contexts holds the potential to not only simplify communication but also foster greater trust and transparency in healthcare systems.
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