Applications of large language models in cancer care: current evidence and future perspectives
Open accessAbstract
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)
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- 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
- ORCID
- Venue
- Frontiers in Oncology
- Publisher
- Frontiers Media SA
- Published
- 2023-09-04
- Volume
- 13
- Pages
- 1268915-1268915
- ISSN
- 2234-943X (ISSN-L)
- PMC
- PMC10507617
- 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
- gold
- doi.org
- Licence
- 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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