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This paper presents RadOnc-GPT, a large language model specialized forradiation oncology through advanced tuning methods. RadOnc-GPT was finetuned ona large dataset of radiation oncology patient records from the Mayo Clinic inArizona. The model employs instruction tuning on three key tasks - generatingradiotherapy treatment regimens, determining optimal radiation modalities, andproviding diagnostic descriptions/ICD codes based on patient diagnosticdetails. Evaluations conducted by comparing RadOnc-GPT outputs to general largelanguage model outputs showed higher ROUGE scores in these three tasks. Thestudy demonstrated the potential of using large language models fine-tunedusing domain-specific knowledge like RadOnc-GPT to achieve transformationalcapabilities in highly specialized healthcare fields such as radiationoncology. However, our model's clinical relevance requires confirmation, and itspecializes in only the aforementioned three specific tasks and lacks broaderapplicability. Furthermore, its evaluation through ROUGE scores might notreflect the true semantic and clinical accuracy - challenges we intend toaddress in future research.

RadOnc-GPT: A Large Language Model for Radiation Oncology