RadOnc-GPT: A Large Language Model for Radiation Oncology
This addresses the need for specialized AI tools in radiation oncology, though it is incremental as it adapts existing methods to a new domain with limited tasks.
The paper tackled the problem of applying large language models to radiation oncology by developing RadOnc-GPT, a model fine-tuned on patient records, which achieved higher ROUGE scores in tasks like generating treatment regimens and determining modalities compared to general models.
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.