Decoding Radiology Reports: Harnessing Large Language Models for Patient Clarity, Simplifying Complex Medical Terminology: The Role of AI in Radiology

In today's digital age and fast-paced world of medical imaging, where access to healthcare information is expanding, ensuring patients understand their medical reports is paramount. As a seasoned leader in medical imaging and a radiologic technologist expert in radiography and computed tomography (CT), I have firsthand witnessed the challenges patients face in comprehending complex radiology reports.

Often brimming with jargon and complex technical medical terminology, these reports can be daunting to interpret, causing confusion and anxiety among patients. However, recent advancements in artificial intelligence, particularly Large Language Models (LLMs), offer promising solutions to this longstanding issue.

Unveiling Radiology Reports: Advocating for Simplification

Reflecting on my experiences, I've encountered numerous patients struggling to decipher their radiology reports. This prompted me to delve deeper into research conducted by Yale University titled Quantitative Evaluation of Large Language Modelsto Streamline Radiology Report Impressions: A Multimodal Retrospective Analysis, which examined the efficacy of various LLMs, including OpenAI's ChatGPT models, Google's Bard (now Gemini), and Microsoft Bing, in simplifying complex medical information. The findings underscored the potential of LLMs to transform the readability of radiology reports, offering a ray of hope for patients grappling with medical terminology.

Deciphering Radiology Reports: An Evaluation of Four Large Language Models (LLMs)

The study conducted a retrospective comparative analysis of four Large Language Models (LLMs) using 750 anonymized radiology reports (refer to Figure 1: Flowchart of Study Design) from the Medical Information Mart for Intensive Care (MIMIC)-IV database.

By employing various prompts and readability assessments, the researchers evaluated the efficacy of LLMs in simplifying report interpretations across a range of imaging modalities, including CT, Radiography, MRI, US, and Diagnostic Mammography (as outlined in Table 1: Breakdown of Imaging Modalities Used), with prompts tailored for patients.

Advancements in Radiology Report Readability: Insights from Large Language Model (LLMs) Study

The study revealed significant strides in the readability of radiology reports through the use of Large Language Models (LLMs), offering promising prospects for patient understanding and engagement in healthcare decision-making.

Employing three distinct prompts to evaluate the efficacy of LLMs in streamlining radiology reports, notable reductions in reading grade levels were observed across various imaging modalities.

Results indicated that all four LLMs markedly enhanced the readability of radiology reports (refer to Table 2: Reading Grade Level and Word Count for Each LLM and Prompt Based on Examination Type) across different prompts and imaging techniques.

Prompt Selection involved three variations: "Simplify this radiology report," "I am a patient. Simplify this radiology report," and "Simplify this radiology report at the 7th-grade level" (see Fig 3 below).

Each prompt was followed by the radiology report impression and queried only once. Leveraging established readability indices like Gunning Fog and Flesch–Kincaid Grade Level, the study underscores the potential of LLMs to enhance communication between healthcare providers and patients.

Furthermore, findings underscored that original radiologist reports exhibited comparable complexities across various imaging modalities, underscoring the uniform advantage of LLM-based simplification techniques. This breakthrough bears significant implications for fostering patient-centered care and promoting transparent communication in healthcare contexts.

Navigating the Future: Enhancing Healthcare Literacy with AI and Patient Portals

In exploring the transformative potential of AI-driven Large Language Models (LLMs), it's clear that meticulous fine-tuning and customization are essential to balance simplification with clinical integrity. Addressing readability metrics and incorporating expert medical oversight are vital steps toward realizing the full benefits of LLMs in healthcare. As we advance toward a future where digital health literacy is crucial, innovative solutions must continue to improve patient comprehension of medical information.

Patient portals play a pivotal role in this evolution, offering convenient access to radiology reports, medical history, test results, and more. However, a recent DrFirst survey reveals challenges in accessing medical records through these portals, despite their importance to 92% of US residents. Nonetheless, adoption rates are rising, with nearly 40% of individuals accessing their records through platforms like Epic's MyChart.

Erum Ahmed's article Patients are using portals more than ever- and high adoption is linked to shorter hospital stays underscores this trend, noting that increased portal usage correlates with shorter hospital stays. Health executives are prioritizing investments in patient portals to enhance communication and engagement.

By harnessing LLMs and fostering collaboration among technology developers, healthcare providers, and patients, we can create a more accessible and patient-centric healthcare ecosystem.

Refinement and Reflection: The Impact of AI-LLMs on Radiology Report Comprehension and Patient Engagement

Questions for Reflection:

1) Have you ever struggled to understand a radiology report? How do you think AI can help bridge the gap in medical communication?

2) What steps can healthcare providers take to ensure AI-driven simplification of medical information is accurate and reliable?

3) How can patients be better educated about interpreting radiology reports and navigating their healthcare journey?

4) How can healthcare organizations integrate LLMs into their workflow to facilitate automatic simplification of radiology reports?

5) What strategies can be employed to enhance patient engagement and digital health literacy in understanding medical information?

6) How might advancements in LLM technology further transform patient-provider communication and healthcare outcomes?

Let's continue the dialogue and strive together towards a future where healthcare information is not just accessible, but truly understandable for all.

Transformative Effects of AI on Radiology Reports: A Patient-Centric Study Overview

In summary, this study highlights the transformative potential of LLMs in simplifying radiology reports for patients. Despite the challenges that lie ahead, these findings instill hope for a future where medical information becomes more accessible and comprehensible. As we delve further into the realm of AI in healthcare, let us endeavor toward a patient-centered approach that emphasizes clarity, understanding, and ultimately, enhanced health outcomes.

Slobodanka

References:

Doshi, R., Amin, K. S., Khosla, P., Bajaj, S., Chheang, S., & Forman, H. P. (2024). Quantitative evaluation of large language models to streamline radiology report impressions: A multimodal retrospective analysis. Radiology, 310(3). https://doi.org/10.1148/radiol.231593

Ahmed, E. (2022, July 5). Patients are using portals more than ever-and high adoption is linked to shorter hospital stays. EMARKETER. https://www.emarketer.com/content/patients-using-portals-more-than-ever-and-high-adoption-linked-shorter-hospital-stays