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人工智能生成解剖图像的前景与陷阱:评估 Midjourney 在美容外科中的应用。

The Promise and Pitfalls of AI-Generated Anatomical Images: Evaluating Midjourney for Aesthetic Surgery Applications.

机构信息

Gynecologist, Department of Obstetrics and Gynaecology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Via Olgettina 58-60, 20132, Milan, Italy.

Otolaryngologist, Niguarda Hospital, Milan, Italy.

出版信息

Aesthetic Plast Surg. 2024 May;48(9):1874-1883. doi: 10.1007/s00266-023-03826-w. Epub 2024 Jan 18.

Abstract

BACKGROUNDS

The rapid advancement of generative artificial intelligence (AI) systems, such as Midjourney, has paved the way for their use in medical training, producing computer-generated images. However, despite clear disclosures stating that these images are not intended for medical consultations, their accuracy and realism are yet to be thoroughly examined.

METHODS

A series of requests were addressed to the Midjourney AI tool, a renowned generative artificial intelligence application, with a focus on depicting appropriate systemic anatomy and representing aesthetic surgery operations. Subsequently, a blinded panel of four experts, with years of experience in anatomy and aesthetic surgery, assessed the images based on three parameters: accuracy, anatomical correctness, and visual impact. Each parameter was scored on a scale of 1-5.

RESULTS

All of images produced by Midjourney exhibited significant inaccuracies and lacked correct anatomical representation. While they displayed high visual impact, their unsuitability for medical training and scientific publications became evident.

CONCLUSIONS

The implications of these findings are multifaceted. Primarily, the images' inaccuracies render them ineffective for training, leading to potential misconceptions. Additionally, their lack of anatomical correctness limits their applicability in scientific articles. Although the study focuses on a single AI tool, it underscores the need for collaboration between AI developers and medical professionals. The potential integration of accurate medical databases could refine the precision of such AI tools in the future.

LEVEL OF EVIDENCE III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

摘要

背景

生成式人工智能(如 Midjourney)的快速发展为其在医学培训中的应用铺平了道路,生成计算机生成的图像。然而,尽管明确声明这些图像不用于医疗咨询,但它们的准确性和真实性仍有待彻底检查。

方法

向 Midjourney AI 工具提出了一系列请求,这是一种著名的生成式人工智能应用程序,重点是描绘适当的系统解剖结构并代表美容手术操作。随后,由四名具有解剖学和美容外科多年经验的专家组成的盲评小组根据三个参数评估图像:准确性、解剖正确性和视觉影响。每个参数的得分为 1-5 分。

结果

Midjourney 生成的所有图像都存在明显的不准确之处,缺乏正确的解剖学表示。虽然它们具有很高的视觉冲击力,但它们不适合医学培训和科学出版物是显而易见的。

结论

这些发现的影响是多方面的。首先,图像的不准确性使它们无法用于培训,导致潜在的误解。此外,它们缺乏解剖正确性限制了它们在科学文章中的应用。尽管该研究仅针对单个 AI 工具,但它强调了 AI 开发人员和医学专业人员之间合作的必要性。未来,将准确的医学数据库集成到此类 AI 工具中可能会提高其精度。

证据水平 III:本期刊要求作者为每篇文章分配一个证据水平。有关这些循证医学评级的完整描述,请参阅目录或在线作者指南 www.springer.com/00266

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