RadOnc-GPT: A Large Language Model for Radiation Oncology Article Swipe

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Zhengliang Liu , P. Wang , Yiwei Li , Jason Holmes , Peng Shu , Lian Zhang , Chenbin Liu , Ninghao Liu , Dajiang Zhu , Xiang Li , Quanzheng Li , Samir H. Patel , Terence T. Sio , Tianming Liu , Wei Liu ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2309.10160

This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on a large dataset of radiation oncology patient records from the Mayo Clinic in Arizona. The model employs instruction tuning on three key tasks - generating radiotherapy treatment regimens, determining optimal radiation modalities, and providing diagnostic descriptions/ICD codes based on patient diagnostic details. Evaluations conducted by comparing RadOnc-GPT outputs to general large language model outputs showed higher ROUGE scores in these three tasks. The study demonstrated the potential of using large language models fine-tuned using domain-specific knowledge like RadOnc-GPT to achieve transformational capabilities in highly specialized healthcare fields such as radiation oncology. However, our model's clinical relevance requires confirmation, and it specializes in only the aforementioned three specific tasks and lacks broader applicability. Furthermore, its evaluation through ROUGE scores might not reflect the true semantic and clinical accuracy - challenges we intend to address in future research.

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Artificial Intelligence

Mathematical Analysis

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Computer science Modalities Radiation oncology Medical physics Key (lock) Language model Domain (mathematical analysis) Transformational leadership Relevance (law) Radiation therapy Medicine Artificial intelligence Psychology Radiology Computer security Sociology Law Social science Social psychology Political science Mathematics Mathematical analysis

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2309.10160
PDF: https://arxiv.org/pdf/2309.10160
OA Status: green
Cited By: 11
Related Works: 10
OpenAlex ID: https://openalex.org/W4386907175

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OpenAlex ID: https://openalex.org/W4386907175

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DOI: https://doi.org/10.48550/arxiv.2309.10160

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Title: RadOnc-GPT: A Large Language Model for Radiation Oncology

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-09-18

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Authors: Zhengliang Liu, P. Wang, Yiwei Li, Jason Holmes, Peng Shu, Lian Zhang, Chenbin Liu, Ninghao Liu, Dajiang Zhu, Xiang Li, Quanzheng Li, Samir H. Patel, Terence T. Sio, Tianming Liu, Wei Liu

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Landing page: https://arxiv.org/abs/2309.10160

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PDF URL: https://arxiv.org/pdf/2309.10160

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Concepts: Computer science, Modalities, Radiation oncology, Medical physics, Key (lock), Language model, Domain (mathematical analysis), Transformational leadership, Relevance (law), Radiation therapy, Medicine, Artificial intelligence, Psychology, Radiology, Computer security, Sociology, Law, Social science, Social psychology, Political science, Mathematics, Mathematical analysis

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Cited by: 11

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Citations by year (recent): 2025: 6, 2024: 4, 2023: 1

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Related works (count): 10

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