Tripathi Satvik, Patel Jay, Mutter Liam, Dorfner Felix J, Bridge Christopher P, Daye Dania
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA; Department of Radiology, Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
Curr Probl Diagn Radiol. 2025 May-Jun;54(3):342-348. doi: 10.1067/j.cpradiol.2024.12.004. Epub 2024 Dec 10.
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.
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.
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.
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.
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.
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，许多人缺乏有效应用机器学习（ML）和深度学习（DL）算法的技术技能，这限制了这些方法对本可从中受益的放射学研究人员的可及性。诸如GPT-4o之类的大语言模型（LLM）可充当虚拟顾问，针对特定研究需求提供量身定制的算法建议。本研究评估GPT-4o作为推荐系统在增强放射科医生对AI在研究中的理解和应用方面的有效性。
GPT-4o用于根据研究人员提供的具体细节推荐ML和DL算法，包括数据集特征、模态类型、数据大小和研究目标。该模型充当虚拟顾问，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推荐的清晰度、任务匹配度、模型多样性和基线选择。对回复进行评分以评估该模型满足放射学研究人员需求的能力。
GPT-4o有效地为各种放射学任务推荐了合适的ML和DL算法，包括医学成像中的分割、分类和回归。该模型建议了一系列不同的成熟和创新算法，如U-Net、随机森林、注意力U-Net和EfficientNet，与公认的做法契合良好。
GPT-4o通过提供清晰且相关的AI和ML算法推荐，显示出有望成为放射科医生和早期职业研究人员的宝贵工具。它弥合AI应用中知识差距的能力可使先进技术的获取更加普及，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整合到常规工作流程中及其在持续专业发展中的作用。
