ABSTRACT
OBJECTIVE: To assess the feasibility of using large language models (LLMs), specifically ChatGPT-4, to generate concise and accurate layperson summaries of musculoskeletal radiology reports. METHODS: Sixty radiology reports, comprising 20 MR shoulder, 20 MR knee, and 20 MR lumbar spine reports, were obtained via PACS. The reports were deidentified and then submitted to ChatGPT-4, with the prompt "Produce an organized and concise layperson summary of the findings of the following radiology report. Target a reading level of 8-9th grade and word count <300 words." Three (two primary and one later added for validation) independent readers evaluated the summaries for completeness and accuracy compared to the original reports. Summaries were rated on a scale of 1 to 3: 1) summaries that were incorrect or incomplete, potentially providing harmful or confusing information; 2) summaries that were mostly correct and complete, unlikely to cause confusion or harm; and 3) summaries that were entirely correct and complete. RESULTS: All 60 responses met the criteria for word count and readability. Mean ratings for accuracy were 2.58 for reader 1, 2.71 for reader 2, and 2.77 for reader 3. Mean ratings for completeness were 2.87 for reader 1 and 2.73 for reader 2 and 2.87 for reader 3. For accuracy, reader 1 identified three summaries as a 1, reader 2 identified one, and reader 3 identified none. For the two primary readers, inter-reader agreement was low for accuracy (kappa 0.33) and completeness (kappa 0.29). There were no statistically significant changes in inter-reader agreement when the third reader's ratings were included in analysis. CONCLUSION: Overall ratings for accuracy and completeness of the AI-generated layperson report summaries were high with only a small minority likely to be confusing or inaccurate. This study illustrates the potential for leveraging generative AI, such as ChatGPT-4, to automate the production of patient-friendly summaries for musculoskeletal MR imaging.
ABSTRACT
OBJECTIVE: Radiology reports have traditionally been written for referring clinical providers. However, as patients increasingly access their radiology reports through online medical records, concerns have been raised about their ability to comprehend these complex documents. The purpose of this study was to assess the readability of lumbar spine MRI reports. MATERIALS AND METHODS: We reviewed 110 lumbar spine MRI reports dictated by 11 fellowship-trained radiologists (eight musculoskeletal radiologists and three neuroradiologists) at a single academic medical center. We evaluated each article for readability using five quantitative readability tests: the Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning Fog Index, Coleman-Liau Index, and the Simple Measure of Gobbledygook. The number of reports with readability at or below eighth-grade level (average reading ability of U.S. adults) and at or below sixth-grade level (level recommended by the National Institutes of Health and the American Medical Association for patient education materials) were determined. RESULTS: The mean readability grade level of the lumbar spine MRI reports was greater than the 12th-grade reading level for all readability scales. Only one report was written at or below eighth-grade level; no reports were written at or below sixth-grade level. CONCLUSION: Lumbar spine MRI reports are written at a level too high for the average patient to comprehend. As patients increasingly read their radiology reports through online portals, consideration should be made of patients' ability to read and comprehend these complex medical documents.
Subject(s)
Comprehension , Health Literacy , Lumbar Vertebrae/diagnostic imaging , Magnetic Resonance Imaging , Spinal Diseases/diagnostic imaging , Adult , HumansABSTRACT
Malnutrition can increase the risk of surgical site infection in both elective spine surgery and total joint arthroplasty. Obesity and diabetes are common comorbid conditions in patients who are malnourished. Despite the relatively high incidence of nutritional disorders among patients undergoing elective orthopaedic surgery, the evaluation and management of malnutrition is not generally well understood by practicing orthopaedic surgeons. Serologic parameters such as total lymphocyte count, albumin level, prealbumin level, and transferrin level have all been used as markers for nutrition status. In addition, anthropometric measurements, such as calf and arm muscle circumference or triceps skinfold, and standardized scoring systems, such as the Rainey-MacDonald nutritional index, the Mini Nutritional Assessment, and institution-specific nutritional scoring tools, are useful to define malnutrition. Preoperative nutrition assessment and optimization of nutritional parameters, including tight glucose control, normalization of serum albumin, and safe weight loss, may reduce the risk of perioperative complications, including infection.
