Harnessing Artificial Intelligence to Code a Decision-Making Aid for the Prostate Cancer Brachytherapy Multidisciplinary Meeting Using Retrieval-Augmented Generation

Introduction We have previously published the first application of artificial intelligence (AI) to streamline the real-world multidisciplinary meeting (MDM) process, finding that ChatGPT (OpenAI) could accurately recommend a treatment option in perfect concordance with the European Association of Urology (EAU) guidelines. ChatGPT now allows users to formally create their own generative pretrained transformer (GPT) tailored to a specific role using retrieval-augmented generation (RAG). In this paper, we seek to utilize this technology for the first time in the prostate cancer multidisciplinary team (MDT) setting, to create a custom decision-making aid GPT for prostate cancer patients being treated with brachytherapy. Methods ChatGPT 4.0 was prompted to create a decision-making custom GPT using RAG to select brachytherapy treatment options for prostate cancer patients. While international guidelines exist, we opted to use our well-established local Royal Surrey guidelines as prior knowledge to the custom GPT. Results The custom GPT created is available on GitHub. Forty patients were discussed, with the AI-generated tool suggesting the same brachytherapy treatment strategy as that of the real-world MDM in all (40/40) cases. Conclusion We believe that this simple study has demonstrated a very practical application of this novel AI technology. This has the capacity to reinvent the paradigm in the preparation of patient cases prior to a cancer board or MDM, as cases can be preselected for the optimal treatment strategy with 100% accuracy prior to discussion by clinicians in the MDM, streamlining the process and facilitating a nuanced discussion by eliminating irrelevant treatment strategies beforehand.

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Publication Details

Journal
Cureus
Published
2026-02-12
DOI
https://doi.org/10.7759/cureus.103457
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Harnessing Artificial Intelligence to Code a Decision-Making Aid for the Prostate Cancer Brachytherapy Multidisciplinary Meeting Using Retrieval-Augmented Generation

Abraham Gabriel, Joseph Gabriel, Stephen Langley, Georgios Antonoglou
Cureus
Artificial Intelligence in Healthcare and Education
article

Harnessing Artificial Intelligence to Code a Decision-Making Aid for the Prostate Cancer Brachytherapy Multidisciplinary Meeting Using Retrieval-Augmented Generation

Abraham Gabriel, Joseph Gabriel, Stephen Langley, Georgios Antonoglou
article en

Abstract

Introduction We have previously published the first application of artificial intelligence (AI) to streamline the real-world multidisciplinary meeting (MDM) process, finding that ChatGPT (OpenAI) could accurately recommend a treatment option in perfect concordance with the European Association of Urology (EAU) guidelines. ChatGPT now allows users to formally create their own generative pretrained transformer (GPT) tailored to a specific role using retrieval-augmented generation (RAG). In this paper, we seek to utilize this technology for the first time in the prostate cancer multidisciplinary team (MDT) setting, to create a custom decision-making aid GPT for prostate cancer patients being treated with brachytherapy. Methods ChatGPT 4.0 was prompted to create a decision-making custom GPT using RAG to select brachytherapy treatment options for prostate cancer patients. While international guidelines exist, we opted to use our well-established local Royal Surrey guidelines as prior knowledge to the custom GPT. Results The custom GPT created is available on GitHub. Forty patients were discussed, with the AI-generated tool suggesting the same brachytherapy treatment strategy as that of the real-world MDM in all (40/40) cases. Conclusion We believe that this simple study has demonstrated a very practical application of this novel AI technology. This has the capacity to reinvent the paradigm in the preparation of patient cases prior to a cancer board or MDM, as cases can be preselected for the optimal treatment strategy with 100% accuracy prior to discussion by clinicians in the MDM, streamlining the process and facilitating a nuanced discussion by eliminating irrelevant treatment strategies beforehand.

CureusVol. 18(2)
Imperial College Healthcare NHS Trust (GB), Royal Surrey County Hospital (GB), Chelsea and Westminster Hospital NHS Foundation Trust (GB), Ashford and St Peter's Hospitals NHS Foundation Trust (GB)
Peace, Justice and strong institutions
Openalex Percentile: Top 59%
Artificial Intelligence in Healthcare and Education
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