ArticleJournal of applied clinical medical physics2025
RTPhy-ChatBot: A RAG-Based intelligent assistant for radiotherapy physics using LLaMA3 and AAPM reports.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Feasibility of retrieval-augmented generation for large language models with Japanese input in radiotherapy.Journal of radiation research · 2026Article
- Intelligent Support for Radiotherapy: A Review of Clinical Applications for Large Language Models.Journal of clinical medicine · 2026Review
- RTPhy-ChatBot: A RAG-Based intelligent assistant for radiotherapy physics using LLaMA3 and AAPM reports.Journal of applied clinical medical physics · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundMedical physics plays a crucial role in radiotherapy, with ongoing technological advancements aimed at improving treatment outcomes. However, the rapid pace of innovation presents challenges for medical physicists, who must continuously acquire and integrate complex information for effective decision-making and communication. PURPOSE: To support efficient knowledge acquisition, we developed RTPhy-ChatBot, an intelligent assistant tailored to radiotherapy physics. The objective was to create a reliable and precise tool to assist medical physicists in their daily work.
methodsThe knowledge base for RTPhy-ChatBot was constructed from publications by the American Association of Physicists in Medicine (AAPM), which were converted into markdown format, segmented, and embedded using the bge-base-en-v1.5 model. RTPhy-ChatBot employed the Meta-LLaMA3-8B-Instruct model for response generation. We compared its performance with several commercial large language models (LLMs) across 20 template questions and evaluated the impact of zero-shot chain-of-thought (CoT) reasoning. In addition to expert scoring by senior medical physicists, we conducted Rouge score analysis against synthesized reference answers.
resultsRTPhy-ChatBot demonstrated strong performance in answering radiotherapy physics questions. Across 20 questions, it achieved an average score of 4.0 ± 0.9, compared to 3.9 ± 1.1 for Gemini-2.0-Flash, 4.0 ± 1.4 for GPT-4o, and 3.8 ± 1.2 for Moonshot-v1. It excelled in questions involving specific quality assurance standards. Rouge analysis yielded scores of 0.5127 (Rouge-1), 0.2119 (Rouge-2), and 0.2748 (Rouge-L), closely matching commercial LLMs.
conclusionsRTPhy-ChatBot proved to be an effective intelligent assistant for radiotherapy physics, delivering accurate, referenced responses grounded in AAPM publications. Despite lacking online access, it matched or exceeded the performance of commercial LLMs in domain-specific tasks. This pilot study highlights the potential of domain-specific assistants in supporting clinical workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.