Improving Readability of Stroke Clinical Trial Consent Forms Using Artificial Intelligence

BACKGROUND: Informed consent forms (ICFs) for clinical trials are often written above the recommended eighth-grade level. We aimed to compare the readability of original ICFs used for National Institutes of Health-funded stroke-related clinical trials with ICFs edited for readability using artificial intelligence. METHODS: tests or the McNemar test (cross-sectional design). We also assessed semantic similarity using the MPNet language model, which produced continuous scores from 0 (no similarity) to 1 (perfect similarity). RESULTS: <0.001). Both the Simple Measure of Gobbledygook and Gunning Fog Index favored the GPT-edited ICFs by 1 to 2 grade levels. The Flesch Reading Ease score favored the GPT-edited ICFs by about 8 points. The mean similarity score was 0.85 (SD=0.04). CONCLUSIONS: GPT-edited ICFs achieved a readability reduction of approximately 2 grade levels compared with the original ICFs while preserving high semantic similarity. Customized GPTs may be a useful tool to improve the readability of clinical trial ICFs.