This study, published in the *Journal of Neurosurgery*, attempts to evaluate the effectiveness of ChatGPT in generating readable summaries of neurosurgical literature for patient education. However, despite its innovative aim, the study has several critical shortcomings in methodology, analysis, and conclusions.
Firstly, the selection of abstracts from the “top 5 ranked neurosurgical journals” according to Google Scholar lacks justification and transparency. The relatively small sample size (n=150) does not provide robust statistical power, especially given the complex linguistic and conceptual nature of neurosurgical literature. Additionally, the study’s reliance on readability metrics such as Flesch-Kincaid and SMOG indices fails to capture the depth of understanding required for meaningful patient comprehension. These readability scores, though widely used, do not measure how effectively a layperson understands specialized medical information—a gap that questions the study’s relevance to real-world patient education.
The authors’ main conclusion—that GPT-4 summaries improve readability—lacks novelty, as ChatGPT is inherently designed to simplify the language. Moreover, readability alone does not equate to patient comprehension. A critical shortfall of this study is its failure to assess whether patients interpret the simplified summaries correctly, thus missing a key aspect of effective patient education. Enhancing readability without ensuring true comprehension and accuracy in a medical context presents an incomplete solution that could risk misinterpretation of vital information.
Further weakening the study’s rigor is its simplistic assessment of “scientific accuracy.” Relying on two physicians to rate the accuracy of summaries is insufficient for validating complex neurosurgical information. This approach leaves the study vulnerable to bias and limits the generalizability of its findings. The authors cite Cohen’s kappa to measure interrater reliability, yet provide no substantive discussion on the expertise of these reviewers or the potential variability in their interpretations of scientific accuracy—a serious oversight for a study that aspires to impact patient education.
In conclusion, while this study in the *Journal of Neurosurgery* introduces an interesting concept, it suffers from a lack of methodological rigor and a superficial approach to evaluating AI in patient education. Future research would benefit from a more robust sample, refined metrics that go beyond readability to assess comprehension and accuracy, and a thorough validation process. This would provide a more meaningful and reliable foundation for using AI-generated summaries in patient education, moving beyond readability to truly impactful patient understanding.