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Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases

BACKGROUND: As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support t...

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Autores principales: Lukac, Stefan, Dayan, Davut, Fink, Visnja, Leinert, Elena, Hartkopf, Andreas, Veselinovic, Kristina, Janni, Wolfgang, Rack, Brigitte, Pfister, Kerstin, Heitmeir, Benedikt, Ebner, Florian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579162/
https://www.ncbi.nlm.nih.gov/pubmed/37458761
http://dx.doi.org/10.1007/s00404-023-07130-5
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author Lukac, Stefan
Dayan, Davut
Fink, Visnja
Leinert, Elena
Hartkopf, Andreas
Veselinovic, Kristina
Janni, Wolfgang
Rack, Brigitte
Pfister, Kerstin
Heitmeir, Benedikt
Ebner, Florian
author_facet Lukac, Stefan
Dayan, Davut
Fink, Visnja
Leinert, Elena
Hartkopf, Andreas
Veselinovic, Kristina
Janni, Wolfgang
Rack, Brigitte
Pfister, Kerstin
Heitmeir, Benedikt
Ebner, Florian
author_sort Lukac, Stefan
collection PubMed
description BACKGROUND: As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer? MATERIAL AND METHODS: We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated. RESULTS: Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 2 + (FISH negative) as in need of therapy with an antibody and called endocrine therapy “hormonal treatment”. CONCLUSIONS: Support of artificial intelligence by finding individualized and personalized therapy for our patients in the time of rapidly expanding amount of information is looking for the ways in the clinical routine. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.
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spelling pubmed-105791622023-10-18 Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases Lukac, Stefan Dayan, Davut Fink, Visnja Leinert, Elena Hartkopf, Andreas Veselinovic, Kristina Janni, Wolfgang Rack, Brigitte Pfister, Kerstin Heitmeir, Benedikt Ebner, Florian Arch Gynecol Obstet Gynecologic Oncology BACKGROUND: As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer? MATERIAL AND METHODS: We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated. RESULTS: Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 2 + (FISH negative) as in need of therapy with an antibody and called endocrine therapy “hormonal treatment”. CONCLUSIONS: Support of artificial intelligence by finding individualized and personalized therapy for our patients in the time of rapidly expanding amount of information is looking for the ways in the clinical routine. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer. Springer Berlin Heidelberg 2023-07-17 2023 /pmc/articles/PMC10579162/ /pubmed/37458761 http://dx.doi.org/10.1007/s00404-023-07130-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Gynecologic Oncology
Lukac, Stefan
Dayan, Davut
Fink, Visnja
Leinert, Elena
Hartkopf, Andreas
Veselinovic, Kristina
Janni, Wolfgang
Rack, Brigitte
Pfister, Kerstin
Heitmeir, Benedikt
Ebner, Florian
Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases
title Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases
title_full Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases
title_fullStr Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases
title_full_unstemmed Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases
title_short Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases
title_sort evaluating chatgpt as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases
topic Gynecologic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579162/
https://www.ncbi.nlm.nih.gov/pubmed/37458761
http://dx.doi.org/10.1007/s00404-023-07130-5
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