Evaluating ChatGPT as an Adjunct for the Multidisciplinary Tumor Board Decision–Making in Primary Breast Cancer Cases
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Abstract
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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In pharmaceutical research, ChatGPT can contribute by sifting through vast volumes of scientific literature, identifying potential drug candidates, and suggesting innovative research directions.
“In the realm of pharmaceutical research, ChatGPT can contribute by sifting through vast volumes of scientific literature, identifying potential drug candidates, and suggesting innovative research directions.”
ChatGPT can assist in drug discovery, research proposal writing, and summarising complex medical research, expediting the research process.
“It can assist in drug discovery, research proposal writing, and summarising complex medical research, expediting the research process [11,24,34,35,36,37,38].”
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MethodAssessing the accuracy of the GPT-4 model in multidisciplinary tumor board decision prediction
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The Diagnostic Ability of GPT-3.5 and GPT-4.0 in Surgery: Comparative Analysis
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Hendriks, M. P., Jager, A., Ebben, K. C. W. J., van Til, J. A., & Siesling, S. (2024) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2024.104267 · Cited by 19 · see citing works
HERNÁNDEZ-FLORES, L. A., López‐Martínez, J. B., la Rosa, J. J. R.-D., Aillaud‐De‐Uriarte, D., Contreras‐Garduño, S., & Cortés‐González, R. (2025) · Journal of Surgical Oncology · doi:10.1002/jso.28121 · Cited by 18 · see citing works
“However, despite their proven utility in multiple studies, some limitations still need to be addressed [13 – 15].”
Li, H., Huang, J., Liu, K., Liu, J., Liu, Q., Zhou, Z., Zong, Z., & Mao, S. (2025) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2025.110096 · Cited by 17 · see citing works
Use of artificial intelligence in breast surgery: a narrative review
Seth, I., Lim, B., Joseph, K., Gracias, D., Xie, Y., Ross, R. J., & Rozen, W. M. (2024) · Gland Surgery · doi:10.21037/gs-23-414 · Cited by 23 · see citing works
“Lukac et al. concluded that while it has potential, its current version is incapable of providing suitable recommendations for patients with primary breast cancer (160).”
ChatGPT in Oncology Diagnosis and Treatment: Applications, Legal and Ethical Challenges
Zhou, Z., Qin, P., Cheng, X., Shao, M., Ren, Z., Zhao, Y., Li, Q., & Liu, L. (2025) · Current Oncology Reports · doi:10.1007/s11912-025-01649-3 · Cited by 17 · see citing works
“Its ability to process a myriad of information could provide a support tool for treatment management in the near future [80].”
Goglia, M., Pace, M., Yusef, M., Gallo, G., Pavone, M., Petrucciani, N., & Aurello, P. (2024) · In Vivo · doi:10.21873/invivo.13534 · Cited by 14 · see citing works
Evaluation of ChatGPT’s Potential in Tailoring Gynecological Cancer Therapies
Krückel, A., Brückner, L. M., Psilopatis, I., Fasching, P. A., Beckmann, M. W., & Emons, J. (2024) · In Vivo · doi:10.21873/invivo.13614 · Cited by 14 · see citing works
“In order to illustrate the performance of the AI, a score was calculated for each AI-R as well as for each tumor board decision following the model of Lukac et al. (see Table IV) (13).”
MethodInfluentialHack, S., Alsleibi, S., Shemesh, S., & Nakache, G. (2025) · SLEEP · doi:10.1093/sleep/zsaf338 · Cited by 15 · see citing works
Piao, Y., Chen, H., Wu, S., Li, X., Li, Z., & Yang, D. (2024) · Digital Health · doi:10.1177/20552076241284771 · Cited by 13 · see citing works
Braun, E.-M., Juhasz‐Böss, I., Solomayer, E., Truhn, D., Keller, C., Heinrich, V., & Braun, B. J. (2023) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-023-07272-6 · Cited by 13 · see citing works
ChatGPT in surgery: a revolutionary innovation?
Bektaş, M., Pereira, J. K., Daams, F. D., & van der Peet, D. L. (2024) · Surgery Today · doi:10.1007/s00595-024-02800-6 · Cited by 13 · see citing works
“One study assessed Chat-GPT’s ability to outline the best treatment plan for patients with a primary diagnosis of early breast cancer by benchmarking its answers with their own treatment recommendations [23].”
BackgroundChatGPT in a gynaecologic oncology multidisciplinary team tumour board: A feasibility study
Levin, G., Gotlieb, W. H., Ramirez, P. T., Meyer, R., & Brezinov, Y. (2024) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.17929 · Cited by 11 · see citing works
Large language models in cancer: potentials, risks, and safeguards
Zitu, M. M., Le, T. D., Duong, T., Haddadan, S., Garcia, M. A., Amorrortu, R. P., Zhao, Y., Rollison, D. E. M., & Thieu, T. (2024) · BJR|Artificial Intelligence · doi:10.1093/bjrai/ubae019 · Cited by 11 · see citing works
“89 For example, GPT-4 shows effectiveness in radiological feature extraction but struggles with low-quality scans and handwritten annotations, resulting from OCR (optical character recognition) errors, highlighting the need for rigorous validation.”
Ghorbian, M., Ghobaei-Arani, M., & Ghorbian, S. (2025) · Methods · doi:10.1016/j.ymeth.2025.04.001 · Cited by 14 · see citing works
Roeschl, T., Hoffmann, M., Hashemi, D., Rarreck, F., Hinrichs, N., Trippel, T. D., Gröschel, M. I., Unbehaun, A., Klein, C., Kempfert, J., et al. (2025) · JMIRx Med · doi:10.2196/74899 · Cited by 13 · see citing works
“Several studies have shown that LLMs can make treatment decisions for patients with oncological and cardiovascular diseases that are in substantial agreement with the respective treatment decisions made by clinical experts on tumor boards [4-7] and heart teams (HTs) [8].”
Real Estate with AI: An agent based on LangChain
Haurum, K. R., Ma, R., & Wen, L. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.08.199 · Cited by 11 · see citing works
Large language models for improving cancer diagnosis and management in primary health care settings
Andrew, A., & Tizzard, E. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100157 · Cited by 11 · see citing works
Applications of Large Language Models (LLMs) in Breast Cancer CarePreprint
Sorin, V., Glicksberg, B. S., Barash, Y., Konen, E., Nadkarni, G. N., & Klang, E. (2023) · medRxiv · doi:10.1101/2023.11.04.23298081 · Cited by 11 · see citing works
“%) evaluated the performance of ChatGPT on actual patient data 13-15 , as opposed to two studies that used data from the internet 16,17 13,15 , and Choi et al. used LLM for information extraction from ultrasound and pathology reports 14 , ( Figure 2 ).”
BackgroundMethodMeyer, B., Kfuri‐Rubens, R., Schmidt, G., Tariq, M., Riedel, C., Recker, F., Riedel, F., Kiechle, M., & Riedel, M. (2025) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.70251 · Cited by 10 · see citing works
Rinderknecht, E., von Winning, D., Kravchuk, A. P., Schäfer, C., Schnabel, M. J., Siepmann, S., Mayr, R., Grassinger, J., Goßler, C., Pöhl, F., et al. (2024) · Current Oncology · doi:10.3390/curroncol31110520 · Cited by 10 · see citing works
“The initial prompts were developed by MM based on previous LLM queries and relevant studies [11–16,18 ,19 ].”
Generative artificial intelligence powered chatbots in urology
Khawaja, Z., Adhoni, M. Z. U., & Byrnes, K. G. (2025) · Current Opinion in Urology · doi:10.1097/mou.0000000000001280 · Cited by 9 · see citing works
ChatGPT-Assisted Decision-Making: An In-Depth Exploration of the Human–AI Interaction
Mustata, M. A., Iliescu, D., Mavriș, E., Jude, C.-R., Bojor, L., Tudorache, P., Cîrdei, I. A., Hrab, D.-E., Aluculesei, A.-C., Răpan, I., et al. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2499165 · Cited by 9 · see citing works
“However, for breast cancer treatments, it showed only 16.05% congruence with a multidisciplinary tumor board’s recommendations (Lukac et al., 2023).”
InfluentialArtificial intelligence as treatment support in breast cancer: current perspectives
Lukac, S., Putz, F., Micheli, G. D., Corti, C., Janni, W. J., Tolaney, S. M., Curigliano, G., Loibl, S., Tarantino, P., & Leone, J. P. (2025) · The Breast · doi:10.1016/j.breast.2025.104564 · Cited by 9 · see citing works
“Lukac et al. [1] re-ported a pilot study conducted on 10 consecutive cases of patients with early BC discussed in MTB.”
Li, Y., Gao, W., Luan, Z., Zhou, Z., & Li, J. (2023) · Cureus · doi:10.7759/cureus.48670 · Cited by 11 · see citing works
“By analyzing mammograms, ChatGPT can identify suspicious regions and offer insights to radiologists, potentially enhancing early diagnosis rates [12,20,21].”
BackgroundAccuracy and Reproducibility of ChatGPT Responses to Breast Cancer Tumor Board Patients
Liao, N., Li, C., Gradishar, W. J., Klimberg, V. S., Roshal, J. A., Yuan, T., Agarwala, S. S., Valero, V. K., Swain, S. M., Margenthaler, J. A., et al. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00001 · Cited by 7 · see citing works
Large language models in thyroid diseases: Opportunities and challenges
Zhang, Y., Li, P., Xu, L., Zhang, X., Ji, H., & Wang, Y. (2025) · EngMedicine · doi:10.1016/j.engmed.2025.100076 · Cited by 7 · see citing works
Development and evaluation of large-language models (LLMs) for oncology: A scoping review
Mehan, N., Desinghe, T. D., & Saha, A. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000980 · Cited by 7 · see citing works
“Other languages include Chinese [53], Korean [72], Spanish [58], and German [42,46].”
Nabieva, N., Brucker, S. Y., & Gmeiner, B. (2024) · Cancers · doi:10.3390/cancers16244163 · Cited by 8 · see citing works
“However, the performance was weak, achieving only 16.05% congruence with the tumor board, probably due to using the older version 3.5 [30].”
InfluentialBüyükceran, E. U., Seyfettin, A., Babatürk, A., Özkan, M. B., Çolak, D., Ünal, İ., Kaymaz, E., Ergün, E., Emer, M. Ö., & Mersin, H. H. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02314-9 · Cited by 6 · see citing works
“Lukac et al., also focusing exclusively on breast cancer, found only 16.05% concordance in their evaluation [11, 14 ] Future research should focus on larger, multicenter prospective studies to assess the feasibility and reliability of LLMs in real-time clinical environments.”
InfluentialTriantafyllidis, A. K., Segkouli, S., Kokkas, S., Alexiadis, A., Lithoxoidou, E. E., Manias, G., Antoniades, A., Votis, K., & Tzovaras, D. (2025) · Healthcare · doi:10.3390/healthcare14010045 · Cited by 6 · see citing works
ChatGPT utilization within the building blocks of the healthcare services: A mixed-methods study
Shojaei, P., Khosravi, M., Jafari, Y., Mahmoudi, A. H., & Hassanipourmahani, H. (2024) · Digital Health · doi:10.1177/20552076241297059 · Cited by 6 · see citing works
Bader, S., Schneider, M. O., Psilopatis, I., Anetsberger, D., Emons, J., & Kehl, S. (2024) · Zeitschrift für Geburtshilfe und Neonatologie · doi:10.1055/a-2411-9516 · Cited by 5 · see citing works
“Die Autoren kamen zu dem Schluss, dass vor allem die bereitgestellten Informationen und deren Präsentation wichtig für die Vorhersage - kraft von KI-Anwendungen ist [11].”
InfluentialAbdullayev, N., Kottlors, J., Habibov, H., Yilmaz, F., Zimmer, C., Hokamp, N. G., Lennartz, S., Valiyev, V., Abbasli, C., Goertz, L., et al. (2025) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2025.110384 · Cited by 5 · see citing works
Yaşar, Y., Demır, M., Cantürk, A., ÖZYILMAZ, S., Turgan, A. H., & Ağaçkaya, Y. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.01.017 · Cited by 5 · see citing works
De la Torre-Trillo, J., Yáñez Castillo, Y., Melgarejo Segura, M. T., Carmona Sánchez, E., Zambudio Munuera, A., Mora-Delgado, J., & López Luque, A. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15062130 · Cited by 5 · see citing works
“Lukac et al. found total agreement rates of 61.4% in primary breast cancer, decreasing to 48% in advanced cases [25], figures very similar to our localized and metastatic prostate subgroups.”
InfluentialLukac, S., Griewing, S., Leinert, E., Dayan, D., Heitmeir, B., Wallwiener, M., Janni, W., Fink, V., & Ebner, F. K. (2024) · Clinics and Practice · doi:10.3390/clinpract15010008 · Cited by 5 · see citing works
“However, these results confirm previous experience that, in everyday clinical practice, ChatGPT can be more supportive for the patient than for doctors [24,34].”
Masur, L., Driller, M. W., Suppiah, H. T., Matzka, M., Sperlich, B., & Düking, P. (2025) · JMIR Formative Research · doi:10.2196/71358 · Cited by 4 · see citing works
A systematic review on the generative AI applications in human medical genetics
Changalidis, A. I., Barbitoff, Y. A., Nasykhova, Y. A., & Glotov, A. S. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2025.1694070 · Cited by 6 · see citing works
“…Lukac et al. (2023) assessed ChatGPT’s ability to contribute to multidisciplinary tumor board decisions for breast cancer patients: it lacked specificity and occasionally misclassified biomarker interpretations, highlighting current limitations in reliability for case-level recommendations [141].”
İsmayılov, R., Altundağ, Ö., & Akçalı, Z. (2025) · Immunotherapy · doi:10.1080/1750743x.2025.2580272 · Cited by 4 · see citing works
Goh, S. S. N., Mariappan, R., Tan, G. S. W., Yao, J., Hew, F. M., Yeo, Y., Ow, S. G. W., Koh, W. Y., Kumarakulasinghe, N. B., Tan, T. H., et al. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00243 · Cited by 4 · see citing works
Lamberti, G., Panzuto, F., Massironi, S., Cives, M., Salvia, A. L., Spada, F., Faggiano, A., Pusceddu, S., Albertelli, M., Tafuto, S., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02274-x · Cited by 4 · see citing works
“%) and the moderate ratings documented in glioma adjuvant therapy decision-making (median overall agreement 5/10) 20,21 .”
Prabhakaran, S., Bell, S. W., Lee, J. C., & Kong, J. C. H. (2026) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2026.111741 · Cited by 4 · see citing works
Applications of large language models in tumor boards: a systematic review
Konzmann, B., Henkel, M. J., Breit, C., & Wetterauer, C. (2026) · Oncology Reviews · doi:10.3389/or.2026.1757059 · Cited by 6 · see citing works
Taşkaldıran, I., Önder, Ç. E., Gökbulut, P., Koç, G., & Kuşkonmaz, Ş. M. (2024) · Digital Health · doi:10.1177/20552076241278692 · Cited by 4 · see citing works
Riedel, M., Meyer, B., Kfuri‐Rubens, R., Riedel, C., Amann, N., Kiechle, M. B., & Riedel, F. (2025) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.15123 · Cited by 4 · see citing works
“21 Accordingly, we found that GPT-4 excelled at conveying general surgical outcomes, but—according to our clinical experts—lacked the necessary depth for detailed and accurate responses.”
Schmidl, B., Hoch, C. C., Walter, R., Wirth, M., Wollenberg, B., & Hussain, T. (2025) · Discover Oncology · doi:10.1007/s12672-025-02798-4 · Cited by 4 · see citing works
Akinniyi, O., Dixon, J., Aina, J., Weaks, F., Saleh, G. A., Rahman, M. M., Meeker, T. J., Trivedi, H., Gichoya, J. W., & Khalifa, F. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109036 · Cited by 3 · see citing works
Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives
Khajeh, M., Farshidmehr, P., Rahimpour, E., Amiri, P., & Shahram, M. A. (2026) · Journal of International Medical Research · doi:10.1177/03000605251412124 · Cited by 4 · see citing works
“51,52 For instance, AI-enabled smart bandages with integrated biosensors can monitor wound moisture, pH, and temperature in real time, triggering alerts when infection is suspected.”
Jiang, H., Yang, C., Zhou, W., Yin, C.-L., Zhou, S., He, R., Ran, G., Wang, W., Wu, M., & Yu, J. (2026) · JMIR Medical Informatics · doi:10.2196/68182 · Cited by 3 · see citing works
“Notably, the recommendations provided by ChatGPT for the clinical management of early-diagnosed breast cancer closely align with decisions made by multidisciplinary teams (MDTs) [13].”
Delourme, S., Redjdal, A., Jacques Bouaud, A., & Brigitte Séroussi, A. (2024) · Methods of Information in Medicine · doi:10.1055/a-2528-4299 · Cited by 3 · see citing works
Kaplan, S. T. (2025) · Oncology Research and Treatment · doi:10.1159/000545231 · Cited by 3 · see citing works
“Although Omni could recommend appropriate treatments based on molecular pro fi les for advanced solid tumors, it sometimes deviated from established clinical guidelines in complex scenarios [11, 12].”
Artificial intelligence as an adjunct tool for breast oncologists – are we there yet?
Schlam, I. M., Menezes, M. C. S., Corti, C., Tan, A., Abuali, I. A., & Tolaney, S. M. (2023) · ESMO Open · doi:10.1016/j.esmoop.2023.101643 · Cited by 3 · see citing works
“However, another recent study by Lukac et al.(13) utilizing ChatGPT as a supportive tool for multidisciplinary tumor board in patients with early breast cancer noted that neoadjuvant treatments were not considered by ChatGPT, HER2 expression interpretation was incorrect, and, as expected, recent studies were not incor…”
ResultClinical Application of Large Language Models for Breast Conditions: A Systematic ReviewPreprint
Cheung, B. H. H., Poon, K. G., Lai, C. F., Lam, K. C., Co, M. T.-H., & Kwong, A. (2024) · medRxiv · doi:10.1101/2024.08.31.24312542 · Cited by 3 · see citing works
“[21, 22] Concordance rate was highest for recommendations on radiotherapy (95%), followed by chemotherapy (94.7%), endocrine therapy (75%), genetic testing (70%).”
BackgroundInfluentialLiao, W., Li, M., Ma, C., Han, Y., Wang, D., Liu, H., Wang, Y., Feng, Z., Wang, H., & Guan, Y. (2026) · Journal of Medical Internet Research · doi:10.2196/83188 · Cited by 2 · see citing works
“These findings are highly consistent with previous studies by Lukac et al [63] and Liu et al [64], both of whom emphasized the importance of AI’s ability to generate dependable treatment recommendations.”
Palzer, J., Genchev, A., Belger, E., Weigle, C. A., Wiemann, B. A., Tessmer, P., Murad, M., Berres, M., Meister, F., Amygdalos, I., et al. (2026) · Cancers · doi:10.3390/cancers18132175 · Cited by 2 · see citing works
“The utility of LLMs as part of an MTB has been addressed in recent studies for various cancer entities [5,6].”
The role of artificial intelligence in precision medicine for breast cancer
Hu, J.-J., Ding, Z.-L., & Yang, Z. (2026) · Discover Oncology · doi:10.1007/s12672-026-05550-8 · Cited by 3 · see citing works
“…in January 2023 involving 10 consecutive BC patients discussed in an MDT. ChatGPT incorrectly identified patients with HER2 1 + and 2+ (FISH-negative) as requiring antibody therapy, used the less precise term “hormone therapy” instead of “endocrine therapy” and provided only generic answers [89].”
Can ChatGPT be used as a scientific source of information on tooth extraction?
Yamamoto, S., Hamada, M., Nishiyama, K., Motoki, A., Fujita, Y., & Uzawa, N. (2025) · Journal of Oral and Maxillofacial Surgery Medicine and Pathology · doi:10.1016/j.ajoms.2025.02.009 · Cited by 2 · see citing works
Roeschl, T., Hoffmann, M., Hashemi, D., Rarreck, F., Hinrichs, N., Trippel, T. D., Gröschel, M. I., Unbehaun, A., Klein, C., Kempfert, J., et al. (2024) · medRxiv · doi:10.1101/2024.11.20.24313385 · Cited by 2 · see citing works
Schmidl, B., Hoch, C. C., Shoykhet, M., Weiser, T., Pigorsch, S. U., Stögbauer, F., Wollenberg, B., Hussain, T., & Wirth, M. (2025) · Research Square · doi:10.21203/rs.3.rs-5648998/v1 · Cited by 2 · see citing works
- Authors
- Lukac, S., Dayan, D., Fink, V., Leinert, E., Hartkopf, A., Veselinovic, K., Janni, W., Rack, B., Pfister, K., Heitmeir, B., & Ebner, F.
- Affiliations
- University Hospital Ulm (DE)
- Corresponding author
- Lukac, Stefan
- ORCID
- Venue
- Archives of Gynecology and Obstetrics
- Published
- 2023-07-17
- Volume
- 308
- Issue
- 6
- Pages
- 1831-1844
- ISSN
- 0003-9128 (ISSN-L)
- 1432-0711
- PMC
- PMC10579162
- OpenAlex
- W4384558920
- Cited by
- 130
- References
- 23
- Citations by year
- 2026: 32 · 2025: 44 · 2024: 45 · 2023: 9
- FWCI
- 5.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Archives of Gynecology and Obstetrics (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Universitätsklinikum Ulm
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- ChatGPT, large language models, multidisciplinary tumor board, primary breast cancer, early breast cancer, treatment decision-making, clinical implementation of AI, HER2-positive breast cancer, endocrine therapy, personalized treatment
- MeSH terms
- Aged, Antibodies, Artificial Intelligence, Breast Neoplasms, Female, Humans, Oncogenes, Pilot Projects
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