Cited by 115Artificial Intelligence in Healthcare and EducationAI in cancer detectionArtificial Intelligence in Healthcare

Applications of large language models in cancer care: current evidence and future perspectives

Open access
Iannantuono, Giovanni Maria, Bracken-Clarke, Dara, Floudas, Charalampos S., Roselli, Mario, Gulley, James L., Karzai, Fatima·2023·Frontiers in Oncology

Abstract

The development of large language models (LLMs) is a recent success in the field of generative artificial intelligence (AI). They are computer models able to perform a wide range of natural language processing tasks, including content generation, question answering, or language translation. In recent months, a growing number of studies aimed to assess their potential applications in the field of medicine, including cancer care. In this mini review, we described the present published evidence for using LLMs in oncology. All the available studies assessed ChatGPT, an advanced language model developed by OpenAI, alone or compared to other LLMs, such as Google Bard, Chatsonic, and Perplexity. Although ChatGPT could provide adequate information on the screening or the management of specific solid tumors, it also demonstrated a significant error rate and a tendency toward providing obsolete data. Therefore, an accurate, expert-driven verification process remains mandatory to avoid the potential for misinformation and incorrect evidence. Overall, although this new generative AI-based technology has the potential to revolutionize the field of medicine, including that of cancer care, it will be necessary to develop rules to guide the application of these tools to maximize benefits and minimize risks.

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  • CAs can meet cancer patients' needs for health education and self-management anytime and anywhere.

    “CAs can meet cancer patients' needs for health education and self-management anytime and anywhere, thus addressing the unique requirements of cancer patients (Iannantuono et al. 2023).”

    From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Iannantuono et al. 2023

    The Introduction (page unavailable) describes chatbots used in oncology for remote patient monitoring and lifestyle coaching, and the Results section describes LLMs answering cancer patients’ information questions. These passages support potential roles in education and aspects of self-management, but do not establish that conversational agents meet patients’ needs anytime and anywhere; the Discussion also notes a consequential error rate. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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References 27 · Cited by 115

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  1. [1]Haug CJ Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med (2023) 388(13): 1201–8. doi: 10.1056/NEJMra2302038 36988595 · DOI · Paper page
  2. [2]Bhinder B Gilvary C Madhukar NS Elemento O. Artificial intelligence in cancer research and precision medicine. Cancer Discovery (2021) 11(4): 900–15. doi: 10.1158/2159-8290.Cd-21-0090 PMC8034385 33811123 · DOI · Paper page
  3. [5]Xu L Sanders L Li K Chow JCL. Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR Cancer (2021) 7(4): e27850. doi: 10.2196/27850 34847056 PMC8669585 · DOI · Paper page
  4. [9]Brown T Mann B Ryder N Subbiah M Kaplan JD Dhariwal P. Language models are few-shot learners. Adv Neural Inf Process Syst (2020) 33: 1877–901. doi: 10.48550/arXiv.2005.14165 · DOI · Paper page
  5. [15]Sorin V Barash Y Konen E Klang E. Large language models for oncological applications. J Cancer Res Clin Oncol (2023) 149(11): 9505–9508. doi: 10.1007/s00432-023-04824-w PMC11796830 37160626 · DOI
  6. [16]Uprety D Zhu D West HJ. Chatgpt-a promising generative Ai tool and its implications for cancer care. Cancer (2023) 129(15): 2284–2289. doi: 10.1002/cncr.34827 37183438 · DOI · Paper page
  7. [18]Yeo YH Samaan JS Ng WH Ting PS Trivedi H Vipani A. Assessing the performance of chatgpt in answering questions regarding cirrhosis and hepatocellular carcinoma. Clin Mol Hepatol (2023) 29(3): 721–732. doi: 10.3350/cmh.2023.0089 PMC10366809 36946005 · DOI
  8. [19]Cao JJ Kwon DH Ghaziani TT Kwo P Tse G Kesselman A. Accuracy of information provided by Chatgpt regarding liver cancer surveillance and diagnosis. AJR Am J Roentgenol (2023) 16: 1–4. doi: 10.2214/ajr.23.29493 37222278 · DOI
  9. [20]Haver HL Ambinder EB Bahl M Oluyemi ET Jeudy J Yi PH. Appropriateness of breast cancer prevention and screening recommendations provided by Chatgpt. Radiology (2023) 307(4): e230424. doi: 10.1148/radiol.230424 37014239 · DOI · Paper page
  10. [21]Moazzam Z Cloyd J Lima HA Pawlik TM. Quality of Chatgpt responses to questions related to pancreatic cancer and its surgical care. Ann Surg Oncol (2023) 22. doi: 10.1245/s10434-023-13777-w 37349615 · DOI
  11. [22]Coskun B Ocakoglu G Yetemen M Kaygisiz O. Can Chatgpt, an artificial intelligence language model, provide accurate and high-quality patient information on prostate cancer? Urology (2023) 4: S0090-4295(23)00570-8. doi: 10.1016/j.urology.2023.05.040 37406864 · DOI
  12. [23]Zhu L Mou W Chen R. Can the Chatgpt and other large language models with internet-connected database solve the questions and concerns of patient with prostate cancer and help democratize medical knowledge? J Transl Med (2023) 21(1): 269. doi: 10.1186/s12967-023-04123-5 37076876 PMC10115367 · DOI
  13. [24]Rahsepar AA Tavakoli N Kim GHJ Hassani C Abtin F Bedayat A. How Ai responds to common lung cancer questions: Chatgpt vs Google Bard. Radiology (2023) 307(5): e230922. doi: 10.1148/radiol.230922 37310252 · DOI
  14. [25]Sorin V Klang E Sklair-Levy M Cohen I Zippel DB Balint Lahat N. Large language model (Chatgpt) as a support tool for breast tumor board. NPJ Breast Cancer (2023) 9(1): 44. doi: 10.1038/s41523-023-00557-8 37253791 PMC10229606 · DOI
  15. [26]Schulte B. Capacity of chatgpt to identify guideline-based treatments for advanced solid tumors. Cureus (2023) 15(4): e37938. doi: 10.7759/cureus.37938 37220429 PMC10200252 · DOI
  16. [27]Haemmerli J Sveikata L Nouri A May A Egervari K Freyschlag C. Chatgpt in glioma adjuvant therapy decision making: ready to assume the role of a doctor in the tumour board? BMJ Health Care Inform (2023) 30(1): e100775. doi: 10.1136/bmjhci-2023-100775 PMC10314415 37399360 · DOI
  17. [28]O'Hern K Yang E Vidal NY. Chatgpt underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int (2023) 12: 168–70. doi: 10.1016/j.jdin.2023.06.002 PMC10316650 37404248 · DOI
  18. [29]Hendler J. Avoiding Another Ai Winter. Available at: https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020002/13rRUyeCkdP (Accessed 27 July 2023).
  19. [33]Luo R Sun L Xia Y Qin T Zhang S Poon H. Biogpt: generative pre-trained transformer for biomedical text generation and mining. Briefings Bioinf (2022) 23(6): bbac409. doi: 10.1093/bib/bbac409 36156661 · DOI · Paper page
  20. [34]Patel SB Lam K. Chatgpt: the future of discharge summaries? Lancet Digit Health (2023) 5(3): e107–e8. doi: 10.1016/s2589-7500(23)00021-3 36754724 · DOI · Paper page

Authors
Iannantuono, G. M., Bracken-Clarke, D., Floudas, C. S., Roselli, M., Gulley, J. L., & Karzai, F.
Affiliations
  • University of Rome Tor Vergata (IT)
  • National Institutes of Health (US)
Corresponding author
Karzai, Fatima
Venue
Frontiers in Oncology
Published
2023-09-04
Volume
13
Pages
1268915-1268915
ISSN
2234-943X (ISSN-L)
OpenAlex
W4386423073
Cited by
115
References
27
Citations by year
2026: 23 · 2025: 47 · 2024: 45
FWCI
1.35
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Oncology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health
  • National Cancer Institute
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
large language models, cancer care, ChatGPT, generative AI, solid tumors, cancer screening, cancer management, misinformation, expert verification, clinical decision support

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