"Reports in Medical Illustration (REMIL) in Musculoskeletal Radiology: An Evaluation of Evolving AI Models".

IF 4.7 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
V Umamaheswara Reddy, Nsl Susmitha, Rajesh Botchu
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引用次数: 0

Abstract

Objective: Radiology reports remain predominantly text-based, requiring clinicians and patients to mentally reconstruct imaging findings. Reports in Medical Illustration (REMIL) represent an emerging approach in which artificial intelligence (AI) generates simplified visual summaries directly from report text. This study aimed to evaluate the feasibility, anatomical accuracy, and clinical utility of AI-generated REMIL in musculoskeletal (MSK) radiology.

Methods: Twenty-five MSK imaging cases were selected. Identical report text and standardized prompts were provided to three premium multimodal AI systems-ChatGPT (GPT-4 with DALL-E 3), Perplexity AI, and Google Gemini 3.0 Pro. Each model generated a representative illustration based solely on the report description. Two fellowship-trained musculoskeletal radiologists independently assessed each illustration for anatomical accuracy and clinical usefulness. Errors were categorized as minor or major, and image-generation time was recorded.

Results: Google Gemini 3.0 Pro demonstrated the most consistent performance, producing anatomically accurate illustrations in approximately 40-42% of cases and clinically useful images in 60-65% of cases, whereas ChatGPT and Perplexity AI frequently generated visually plausible images with substantial anatomical inaccuracies. Major errors were observed across all models, particularly in complex cases involving multiple anatomical structures or imaging planes. Simpler cases with a single dominant abnormality were illustrated more accurately by all models.

Conclusion: AI-generated REMIL holds promise as an adjunctive tool for enhancing communication in MSK radiology by providing rapid visual summaries of imaging findings. However, current AI models exhibit inconsistent anatomical accuracy and are not yet reliable for unsupervised clinical use. REMIL should therefore be implemented only with radiologist validation.

“肌肉骨骼放射学医学插图(REMIL)报告:对不断发展的AI模型的评估”。
目的:放射学报告仍然主要以文本为基础,要求临床医生和患者在心理上重建影像学发现。医学插图报告(REMIL)代表了一种新兴的方法,其中人工智能(AI)直接从报告文本生成简化的视觉摘要。本研究旨在评估人工智能生成的REMIL在肌肉骨骼(MSK)放射学中的可行性、解剖学准确性和临床应用。方法:选取25例MSK显像病例。相同的报告文本和标准化提示提供给三个高级多模式人工智能系统- chatgpt (GPT-4与DALL-E 3), Perplexity AI和谷歌Gemini 3.0 Pro。每个模型仅基于报告描述生成一个有代表性的插图。两名训练有素的肌肉骨骼放射科医生独立评估了每个插图的解剖准确性和临床实用性。将错误分为轻微错误和严重错误,并记录图像生成时间。结果:谷歌Gemini 3.0 Pro表现出最一致的性能,在大约40-42%的病例中产生解剖准确的插图,在60-65%的病例中产生临床有用的图像,而ChatGPT和Perplexity AI经常产生视觉上可信的图像,但解剖不准确。在所有模型中都观察到重大错误,特别是在涉及多个解剖结构或成像平面的复杂病例中。所有模型都能更准确地说明单一显性异常的简单病例。结论:人工智能生成的REMIL有望作为辅助工具,通过提供成像结果的快速视觉总结来增强MSK放射学中的交流。然而,目前的人工智能模型表现出不一致的解剖准确性,并且在无监督的临床应用中尚不可靠。因此,REMIL只有在放射科医生验证的情况下才能实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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