Evaluating Artificial Intelligence's Role in Teaching the Reporting and Interpretation of Computed Tomographic Angiography for Preoperative Planning of the Deep Inferior Epigastric Artery Perforator Flap.

JPRAS open 2024 Vol.40() p. 273-285

Lim B, Cevik J, Seth I, Sofiadellis F, Ross RJ, Rozen WM, Cuomo R

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Abstract

[BACKGROUND] Artificial intelligence (AI) has the potential to transform preoperative planning for breast reconstruction by enhancing the efficiency, accuracy, and reliability of radiology reporting through automatic interpretation and perforator identification. Large language models (LLMs) have recently advanced significantly in medicine. This study aimed to evaluate the proficiency of contemporary LLMs in interpreting computed tomography angiography (CTA) scans for deep inferior epigastric perforator (DIEP) flap preoperative planning.

[METHODS] Four prominent LLMs, ChatGPT-4, BARD, Perplexity, and BingAI, answered six questions on CTA scan reporting. A panel of expert plastic surgeons with extensive experience in breast reconstruction assessed the responses using a Likert scale. In contrast, the responses' readability was evaluated using the Flesch Reading Ease score, the Flesch-Kincaid Grade level, and the Coleman-Liau Index. The DISCERN score was utilized to determine the responses' suitability. Statistical significance was identified through a t-test, and P-values < 0.05 were considered significant.

[RESULTS] BingAI provided the most accurate and useful responses to prompts, followed by Perplexity, ChatGPT, and then BARD. BingAI had the greatest Flesh Reading Ease (34.7±5.5) and DISCERN (60.5±3.9) scores. Perplexity had higher Flesch-Kincaid Grade level (20.5±2.7) and Coleman-Liau Index (17.8±1.6) scores than other LLMs.

[CONCLUSION] LLMs exhibit limitations in their capabilities of reporting CTA for preoperative planning of breast reconstruction, yet the rapid advancements in technology hint at a promising future. AI stands poised to enhance the education of CTA reporting and aid preoperative planning. In the future, AI technology could provide automatic CTA interpretation, enhancing the efficiency, accuracy, and reliability of CTA reports.

추출된 의학 개체 (NER)

유형영어 표현한국어 / 풀이UMLS CUI출처등장
해부 breast 유방 dict 3
시술 flap 피판재건술 dict 2
합병증 Deep Inferior scispacy 1
약물 [BACKGROUND] Artificial scispacy 1
약물 [RESULTS] BingAI scispacy 1
약물 ChatGPT scispacy 1
약물 [CONCLUSION] LLMs scispacy 1
질환 BingAI scispacy 1
기타 perforator scispacy 1
기타 BingAI scispacy 1
기타 Flesh Reading scispacy 1

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