Large language models as an academic resource for radiologists stepping into artificial intelligence research

IF 1.5 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Current Problems in Diagnostic Radiology Pub Date : 2024-12-10 DOI:10.1067/j.cpradiol.2024.12.004

Satvik Tripathi , Jay Patel , Liam Mutter , Felix J. Dorfner , Christopher P. Bridge , Dania Daye

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引用次数: 0

Abstract

Background

Radiologists increasingly use artificial intelligence (AI) to enhance diagnostic accuracy and optimize workflows. However, many lack the technical skills to effectively apply machine learning (ML) and deep learning (DL) algorithms, limiting the accessibility of these methods to radiology researchers who could otherwise benefit from them. Large language models (LLMs), such as GPT-4o, may serve as virtual advisors, offering tailored algorithm recommendations for specific research needs. This study evaluates GPT-4o's effectiveness as a recommender system to enhance radiologists' understanding and implementation of AI in research.

Intervention

GPT-4o was used to recommend ML and DL algorithms based on specific details provided by researchers, including dataset characteristics, modality types, data sizes, and research objectives. The model acted as a virtual advisor, guiding researchers in selecting the most appropriate models for their studies.

Methods

The study systematically evaluated GPT-4o's recommendations for clarity, task alignment, model diversity, and baseline selection. Responses were graded to assess the model's ability to meet the needs of radiology researchers.

Results

GPT-4o effectively recommended appropriate ML and DL algorithms for various radiology tasks, including segmentation, classification, and regression in medical imaging. The model suggested a diverse range of established and innovative algorithms, such as U-Net, Random Forest, Attention U-Net, and EfficientNet, aligning well with accepted practices.

Conclusion

GPT-4o shows promise as a valuable tool for radiologists and early career researchers by providing clear and relevant AI and ML algorithm recommendations. Its ability to bridge the knowledge gap in AI implementation could democratize access to advanced technologies, fostering innovation and improving radiology research quality. Further studies should explore integrating LLMs into routine workflows and their role in ongoing professional development.

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大型语言模型作为放射科医师进入人工智能研究的学术资源。

背景：放射科医生越来越多地使用人工智能（AI）来提高诊断准确性和优化工作流程。然而，许多放射科医生缺乏有效应用机器学习（ML）和深度学习（DL）算法的技术技能，这限制了放射科研究人员使用这些方法的机会，而这些研究人员本可以从中受益。大型语言模型（LLM），如 GPT-4o，可以作为虚拟顾问，针对特定研究需求提供量身定制的算法建议。本研究评估了 GPT-4o 作为推荐系统的有效性，以增强放射科医生对研究中人工智能的理解和实施：干预措施：GPT-4o 用于根据研究人员提供的具体细节（包括数据集特征、模式类型、数据大小和研究目标）推荐 ML 和 DL 算法。该模型就像一个虚拟顾问，指导研究人员为其研究选择最合适的模型：该研究系统地评估了 GPT-4o 在清晰度、任务一致性、模型多样性和基线选择方面的建议。结果：GPT-4o 有效地推荐了合适的 MIDI 模型：结果：GPT-4o 为各种放射学任务有效推荐了适当的 ML 和 DL 算法，包括医学影像中的分割、分类和回归。该模型推荐了 U-Net、Random Forest、Attention U-Net 和 EfficientNet 等多种成熟和创新算法，与公认的实践非常吻合：GPT-4o为放射科医生和早期职业研究人员提供了清晰、相关的人工智能和ML算法建议，有望成为有价值的工具。GPT-4o 能够弥合人工智能实施方面的知识鸿沟，从而实现先进技术的普及，促进创新并提高放射学研究质量。进一步的研究应探索将 LLM 纳入常规工作流程及其在持续专业发展中的作用。

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

Current Problems in Diagnostic Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

3.00

自引率

0.00%

发文量

113

审稿时长

46 days

期刊介绍： Current Problems in Diagnostic Radiology covers important and controversial topics in radiology. Each issue presents important viewpoints from leading radiologists. High-quality reproductions of radiographs, CT scans, MR images, and sonograms clearly depict what is being described in each article. Also included are valuable updates relevant to other areas of practice, such as medical-legal issues or archiving systems. With new multi-topic format and image-intensive style, Current Problems in Diagnostic Radiology offers an outstanding, time-saving investigation into current topics most relevant to radiologists.