
AI in Breast Reconstruction: How Accurate Are AI Models in Supporting Surgeons?
2025-07-14
Author: Rajesh
The Rise of AI in Medicine
In the ever-evolving landscape of healthcare, artificial intelligence (AI) is making waves. Its potential to transform medical practices, particularly in enhancing patient care and surgical outcomes, is a hot topic of discussion. Among recent advancements, the performance of AI models like ChatGPT in responding to queries about breast reconstruction has sparked interest. While prior research has looked into ChatGPT's reliability for patient questions, its potential as a decision-support tool for surgeons remains largely uncharted territory.
Understanding the Study
This groundbreaking study focused on evaluating the accuracy of ChatGPT versions 4.0 and 01 in answering standardized plastic surgery exam questions related to breast reconstruction. It explored how these models performed with questions presented in both text and image formats. Researchers posed a total of five questions—one of which had both text and image options—to assess the AI's responses against a standardized key.
Impressive Results Revealed
The findings revealed that ChatGPT version 4.0 scored an impressive average accuracy of 75% on open-ended questions, which climbed to 85% for multiple-choice formats. Interestingly, ChatGPT 01 matched the 75% mark for open-ended questions but also improved to 85% with multiple-choice responses. Notably, ChatGPT 4.0 demonstrated flawless accuracy in addressing text and image-based open-ended questions. However, when it came to multiple-choice questions, adding images did not significantly enhance its performance.
The Future of AI in Surgical Decision-Making
These AI models exhibited varied strengths depending on question formats. Some excelled with structured prompts, while others shone with open-ended queries that included images. The results suggest that while AI holds immense promise as an adjunct tool in breast reconstruction education and surgical decision-making, its effectiveness is contextually dependent. Aligning the strengths of these AI models with the specific needs of clinical practice will be crucial in leveraging their full potential alongside the expertise of seasoned surgeons.