Bridging the Literacy Gap in Breast Reconstruction Patient Education Using Artificial Intelligence.

BACKGROUND

Artificial intelligence is increasingly integrated into healthcare, demonstrating applications across surgical prediction, intraoperative precision, and patient communication. However, there is limited literature examining the role of artificial intelligence in improving patient comprehension of educational materials for breast reconstruction.

METHODS

30 texts from online education resources for breast reconstruction surgery were selected. Readability of the texts was then evaluated using the Flesch-Kincaid grade level, the Gunning Fog index, and the Coleman-Liau index. ChatGPT 4.0 was instructed to translate the individual texts to the 6th-grade level of English comprehension, and the readability of the translated texts was analyzed. Patient Education Materials Assessment Tool analysis assessed the understandability and actionability of each text.

RESULTS

Shapiro-Wilk test on the paired differences showed that the Flesch-Kincaid grade level and the Gunning Fog index followed normal distributions, whereas the Coleman-Liau index did not. The mean Flesch-Kincaid grade level, the Gunning Fog index, and the Coleman-Liau index for the original texts were 11.2, 13.9, and 11.3. The mean Flesch-Kincaid grade level, the Gunning Fog index, and the Coleman-Liau index for the AI-translated counterparts were 8.6, 10.5, and 9.7 (all p < 0.001). The mean understandability (0.70 SD = 0.06 vs. 0.58 SD = 0.08, p < 0.001) and actionability (0.40, SD = 0.09 vs. 0.37 SD = 0.11, p = 0.057) scores of the artificial intelligence-translations were higher than that of the original texts upon Patient Education Materials Assessment Tool analysis.

CONCLUSION

Artificial intelligence may be a promising tool for enhancing comprehension of breast reconstruction education materials among individuals with limited health literacy.

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