ArticleJournal of personalized medicine2023
Challenging ChatGPT 3.5 in Senology-An Assessment of Concordance with Breast Cancer Tumor Board Decision Making.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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.
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Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Applications of large language models in tumor boards: a systematic review.Oncology reviews · 2026Pooled it
- Utilizing large language models in breast cancer management: systematic review.Journal of cancer research and clinical oncology · 2024Pooled it
- Large language models could be applied in personalized out-of-hospital management for breast cancer: a prospective randomized single blind study.Scientific reports · 2025Trial
- Development of a ChatGPT-based assistant in multidisciplinary team discussions in oncological urology: the UroMDT Advisor study.International urology and nephrology · 2026Article
- Role prompting modulates linguistic style but not clinical decision structure in GPT-5 tumour board simulation.NPJ digital medicine · 2026Article
- Evaluating Large Reasoning Models Versus Human Multidisciplinary Teams in Lung Cancer Decision-Making: Real-World Study.Journal of medical Internet research · 2026Article
- The role of artificial intelligence in precision medicine for breast cancer.Discover oncology · 2026Review
- AI-enabled clinical decision support in breast cancer care: a blinded multicenter benchmarking study comparing medically specialized with a general-purpose system.Journal of medical systems · 2026Article
- Comparing clinical decision-making between colposcopists and large language models in cervical dysplasia management: a pilot prospective multicenter study.Archives of gynecology and obstetrics · 2026Article
- Challenges in AI Based Tumor Board Case Summarization and Recommendations.Research square · 2026Article
- A review of application of Artificial Intelligence in breast cancer detection and treatment.Discover oncology · 2026Review
- Exploring the Role of AI in Managing Treatment Recommendations for Lymphedema: International, Multidisciplinary, Multiprofessional Survey Study of Trust, Reliability, and Impact on Decision-Making.JMIR medical informatics · 2026Article
- AI-assisted tumor board decision-making in pancreatic oncology.BMC medical informatics and decision making · 2026Article
- Evaluation of Artificial Intelligence as a Decision-Support Tool in Urological Tumor Boards: A Study in Real Clinical Practice.Journal of clinical medicine · 2026Article
- Large Language Models for Cardiovascular Disease, Cancer, and Mental Disorders: A Review of Systematic Reviews.Healthcare (Basel, Switzerland) · 2025Review
- Temporal evolution of large language models (LLMs) in oncology.Journal of translational medicine · 2025Article
- Augmenting Large Language Models With National Comprehensive Cancer Network Guidelines for Improved and Standardized Adjuvant Therapy Recommendations in Postoperative Breast Cancer Cases.JCO clinical cancer informatics · 2025Article
- Review
- Evaluating the Concordance Between ChatGPT and Multidisciplinary Teams in Breast Cancer Treatment Planning: A Study from Bosnia and Herzegovina.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the recent diffusion of access to publicly available large language models (LLMs), common interest in generative artificial-intelligence-based applications for medical purposes has skyrocketed. The increased use of these models by tech-savvy patients for personal health issues calls for a scientific evaluation of whether LLMs provide a satisfactory level of accuracy for treatment decisions. This observational study compares the concordance of treatment recommendations from the popular LLM ChatGPT 3.5 with those of a multidisciplinary tumor board for breast cancer (MTB). The study design builds on previous findings by combining an extended input model with patient profiles reflecting patho- and immunomorphological diversity of primary breast cancer, including primary metastasis and precancerous tumor stages. Overall concordance between the LLM and MTB is reached for half of the patient profiles, including precancerous lesions. In the assessment of invasive breast cancer profiles, the concordance amounts to 58.8%. Nevertheless, as the LLM makes considerably fraudulent decisions at times, we do not identify the current development status of publicly available LLMs to be adequate as a support tool for tumor boards. Gynecological oncologists should familiarize themselves with the capabilities of LLMs in order to understand and utilize their potential while keeping in mind potential risks and limitations.
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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.