ArticleJPRAS open2024
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.
Article in JPRAS open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluating AI-powered instruction on Computed Tomography Angiography (CTA) teaching outcomes: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- A systematic review and meta-analysis on computed tomography angiography mapping for deep inferior epigastric perforator flap breast reconstruction.Frontiers in oncology · 2025Pooled it
- Reconstructing Aesthetics: Aesthetic Surgery Journal and Aesthetic Breast Reconstruction.Aesthetic surgery journal · 2026Article
- [A brief discussion on microsurgery techniques and free perforator flaps for wound repair].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2026Article
- Artificial Intelligence in Breast Reconstruction: A Scoping Review of Pre-, Intra-, and Postoperative Applications.Plastic and reconstructive surgery. Global open · 2026Article
- Plastic and Reconstructive Surgery in the Era of Artificial Intelligence.Indian journal of plastic surgery : official publication of the Association of Plastic Surgeons of India · 2026Article
- The anterior intercostal artery perforator flap in immediate oncoplastic breast reconstruction: current applications and future perspectives.Frontiers in oncology · 2026Review
- Evaluating the Efficacy of Large Language Models in Generating Medical Documentation: A Comparative Study of ChatGPT-4, ChatGPT-4o, and Claude.Aesthetic plastic surgery · 2025Article
- Artificial Intelligence in Microsurgical Planning: A Five-Year Leap in Clinical Translation.Journal of clinical medicine · 2025Review
- Assessment of patient information guides generated by LLMs for common cardiological procedures.Global cardiology science & practice · 2025Article
- The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities.Journal of clinical medicine · 2025Review
- Present and Future of Autologous Breast Reconstruction: Advancing Techniques to Minimize Morbidity and Complications, Enhancing Quality of Life and Patient Satisfaction.Journal of clinical medicine · 2025Article
- Performance of Artificial Intelligence Chatbots in Answering Clinical Questions on Japanese Practical Guidelines for Implant-based Breast Reconstruction.Aesthetic plastic surgery · 2025Article
- Artificial Intelligence in Breast Reconstruction: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Perforator Selection with Computed Tomography Angiography for Unilateral Breast Reconstruction: A Clinical Multicentre Analysis.Medicina (Kaunas, Lithuania) · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.