REVIEW PAPER
Generative artificial intelligence in optimizing the quality of cancer care: potential, limitations, and future directions of development for Large Language Models. A narrative literature review
1
Department of Education and Research in Health Sciences, Medical University of Warsaw, Warsaw, Poland
2
Department of Cancer Epidemiology and Primary Prevention, Maria Skłodowska Curie National Research Institute of Oncology, Warsaw, Poland
3
Department of Surgical and Transplantation Nursing and Extracorporeal Therapies, Medical University of Warsaw, Warsaw, Poland
Submission date: 2025-12-04
Final revision date: 2026-01-27
Acceptance date: 2026-02-20
Online publication date: 2026-03-03
Corresponding author
Ilona Cieślak
Department of Education and Research in Health Sciences, Medical University of Warsaw, Litewska 14/16 Street, 00-581 Warsaw, Poland
Department of Education and Research in Health Sciences, Medical University of Warsaw, Litewska 14/16 Street, 00-581 Warsaw, Poland
KEYWORDS
generative artificial intelligenceLarge Language Modelsclinical decision supportpatient education as topiconcology
TOPICS
ABSTRACT
This literature review included scientific articles, published between 2022 and 2025, indexed in PubMed, Scopus, and Proquest. The article investigates the potential of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs), to improve the quality of cancer care. LLMs have demonstrated effectiveness in patient education by simplifying complex medical terminology and tailoring content to the user’s level of understanding. LLMs also assist physicians in clinical decision-making by analyzing medical data and supporting adherence to the latest guidelines. However, expert oversight is still necessary due to the risk of error. For cancer prevention, LLMs promote healthy lifestyle adoption, participation in screening programs, and vaccination. They also play an important role in reducing inequities in access to information. Another key feature of LLMs is their ability to translate complex diagnostic reports into patient-friendly language. LLMs have also shown promise in counteracting cancer-related misinformation. However, the article identifies certain LLMs limitations, such as model hallucination, incomplete personalization, and unresolved legal liability concerns. The article emphasizes ethical dilemmas, particularly those related to patient autonomy and the risk of dehumanizing care. For future progress, the article emphasizes the need to integrate LLMs into e-health systems and to develop specialized models supported by interdisciplinary teams.
REFERENCES (46)
1.
Zheng Y, Wang L, Feng B, Zhao A, Wu Y. Innovating healthcare: the role of ChatGPT in streamlining hospital workflow in the future. Ann Biomed Eng. 2024; 52(4): 750-753. https://doi.org/10.1007/s10439....
2.
Alessandri-Bonetti M, Liu HY, Giorgino R, Nguyen VT, Egro FM. The first months of life of ChatGPT and its impact in healthcare: a bibliometric analysis of the current literature. Ann Biomed Eng. 2024; 52(5): 1107-1110. https://doi.org/10.1007/s10439....
3.
Ferdush J, Begum M, Hossain ST. ChatGPT and clinical decision support: scope, application, and limitations. Ann Biomed Eng. 2024; 52(5): 1119-1124. https://doi.org/10.1007/s10439....
4.
Shaheen R, Salim H. Role of artificial intelligence in healthcare. In: Qidwai M, editor. Intersection of human rights and AI in healthcare. IGI Global Scientific Publishing; 2024. p. 173-200. https://doi.org/10.4018/979-8-....
5.
Farhat R, Malik AK, Sheikh AM, Fatima A. The role of AI in enhancing healthcare access and service quality in resource-limited settings. International Journal of Artificial Intelligence. 2024; 11(2): 70-79. https://doi.org/10.36079/lamin....
6.
Liao C, Chu C, Lien M, Wu Y, Wang T. AI-enhanced healthcare: integrating ChatGPT-4 in ePROs for improved oncology care and decision-making: a pilot evaluation. Current Oncology. 2025; 32(1): 7. https://doi.org/10.3390/curron....
7.
Kola MB. Integration of large language models in clinical decision support: a framework for human-AI collaboration in healthcare. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2024; 10(6): 2352-2363. https://doi.org/10.32628/cseit....
8.
Gupta B, Ta P, Ram K, Sivaprakasam M. Comprehensive modeling and question answering of cancer clinical practice guidelines using LLMs. In: IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Natal, Brazil. New York: IEEE; 2024. p. 1-8. https://doi.org/10.1109/cibcb5....
9.
Carl N, Schramm F, Haggenmüller S, Kather JN, Hetz MJ, Wies C, et al. Large language model use in clinical oncology. NPJ Precision Oncology. 2024; 8(1): 240. https://doi.org/10.1038/s41698....
10.
Aydın S, Karabacak M, Vlachos V, Margetis K. Large language models in patient education: a scoping review of applications in medicine. Frontiers in Medicine. 2024; 11. https://doi.org/10.3389/fmed.2....
11.
Gao M, Varshney AS, Chen S, Goddla V, Gallifant J, Doyle P, et al. The use of large language models to enhance cancer clinical trial educational materials. arXiv. Forthcoming 2024. https://doi.org/10.48550/arxiv....
12.
Zitu M, Le TT, Duong TV, Haddadan S, Garcia MAC, Amorrortu RP, et al. Large language models in cancer: potentials, risks, and safeguards. BJR Artificial Intelligence. 2024; 2(1): ubae019. https://doi.org/10.1093/bjrai/....
13.
Subramanian CR, Yang DA, Khanna R. Enhancing health care communication with large language models—the role, challenges, and future directions. JAMA Netw Open. 2024; 7(3): e240347. https://doi.org/10.1001/jamane....
14.
Ferrara E. Large language models for wearable sensor-based human activity recognition, health monitoring, and behavioral modeling: a survey of early trends, datasets, and challenges. Sensors. 2024; 24(15): 5045. https://doi.org/10.3390/s24155....
15.
Caglayan A, Slusarczyk W, Rabbani RD, Ghose A, Papadopoulos V, Boussios S. Large language models in oncology: revolution or cause for concern?. Current Oncology. 2024; 31(4): 1817-1830. https://doi.org/10.3390/curron....
16.
Deroy A, Maity S. Cancer-answer: empowering cancer care with advanced large language models. arXiv. Forthcoming 2025. https://doi.org/10.48550/arxiv....
17.
Musheyev D, Pan A, Loeb S, Kabarriti AE. How well do artificial intelligence chatbots respond to the top search queries about urological malignancies?. Eur Urol. 2024; 85(1): 13-16. https://doi.org/10.1016/j.euru....
18.
Lim S, Schmälzle R. Artificial intelligence for health message generation: an empirical study using a large language model (LLM) and prompt engineering. Front Commun. 2023; 8: 1129082. https://doi.org/10.3389/fcomm.....
19.
Benary M, Wang XD, Schmidt M, Soll D, Hilfenhaus G, Nassir M, et al. Leveraging large language models for decision support in personalized oncology. JAMA Netw Open. 2023; 6(11): e2343689. https://doi.org/10.1001/jamane....
20.
Saadi N, Raha T, Clément C, Pimentel MA, Rajan R, Kanithi PK. Bridging language barriers in healthcare: a study on Arabic LLMs. arXiv. Forthcoming 2025. https://doi.org/10.48550/arxiv....
21.
Li M, Huang J, Yeung J, Blaes A, Johnson S, Liu H. CancerLLM: a large language model in cancer domain. arXiv. Forthcoming 2024. https://doi.org/10.48550/arxiv....
22.
Siu AHY, Gibson DP, Chiu C, Kwok A, Irwin M, Christie A, et al. ChatGPT as a patient education tool in colorectal cancer-An in depth assessment of efficacy, quality and readability. Colorectal Disease. 2025; 27(1): e17267. https://doi.org/10.1111/codi.1....
23.
Hao Y, Holmes J, Waddle M, Davis B, Yu N, Vickers K, et al. Personalizing prostate cancer education for patients using an HER Integrated LLM agent. arXiv. Forthcoming 2025. https://doi.org/10.48550/arXiv....
24.
Hershenhouse JS, Mokhtar D, Eppler MB, Rodler S, Storino Ramacciotti L, Ganjavi C, et al. Accuracy, readability, and understandability of large language models for prostate cancer information to the public. Prostate Cancer Prostatic Dis. 2025; 28(2): 394-399. https://doi.org/10.1038/s41391....
25.
Thapa S. Adhikari S. Leveraging ChatGPT-Like large language models for Alzheimer's disease: enhancing care, advancing research, and overcoming challenges. In: Bhambri P, Soni R, Tran TA, editors. Smart healthcare systems. Boca Raton: CRC Press; 2024. p. 265-275. https://doi.org/10.1201/978103....
26.
Ferrario A, Sedlakova J, Trachsel M. The role of humanization and robustness of large language models in conversational artificial intelligence for individuals with depression: a critical analysis. JMIR Ment Health. 2024; 11: e56568. https://doi.org/10.2196/56569.
27.
Geantă M, Bădescu D, Chirca N, Nechita OC, Radu CG, Rascu S, et al. The potential impact of large language models on doctor–patient communication: a case study in prostate cancer. Healthcare (Basel). 2024; 12(15): 1548. https://doi.org/10.3390/health....
28.
Wang A, Zhou J, Zhang P, Cao H, Xin H, Xu X, et al. Large language model answers medical questions about standard pathology reports. Front Med (Lausanne). 2024; 11: 1402457. https://doi.org/10.3389/fmed.2....
29.
Güneş YC, Cesur T, Çamur E, Karabekmez LG. Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5th edition. Diagn Interv Radiol. 2025; 31(2): 111-129. https://doi.org/10.4274/dir.20....
30.
Trapp C, Schmidt-Hegemann N, Keilholz M, Brose S, Marschner S, Schönecker S, et al. Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy. Strahlentherapie Und Onkologie. 2025; 201: 333-342. https://doi.org/10.1007/s00066....
31.
Garg A, Gupta S, Vats S, Handa P, Goel N. Prospect of large language models and natural language processing for lung cancer diagnosis: a systematic review. Expert Systems. 2024; 41(11): e13697. https://doi.org/10.1111/exsy.1....
32.
Tsai CY, Cheng PY, Deng JH, Jaw FS, Yii SC. ChatGPT v4 outperforming v3.5 on cancer treatment recommendations in quality, clinical guideline, and expert opinion concordance. Digit Health. 2024; 10: 20552076241269538. https://doi.org/10.1177/205520....
33.
Ho CN, Tian T, Ayers AT, Aaron RE, Phillips VJ, Wolf RM, et al. Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review. BMC Medical Informatics and Decision Making. 2024; 24(1): 357. https://doi.org/10.1186/s12911....
34.
Floyd W, Kleber T, Carpenter DJ, Pasli M, Qazi J, Huang C, et al. Current strengths and weaknesses of ChatGPT as a resource for radiation oncology patients and providers. Int J Radiat Oncol Biol Phys. 204; 118(4): 905-915. https://doi.org/10.1016/j.ijro....
35.
Chen LC, Zack T, Demirci A, Sushil M, Miao B, Kasap C, et al. Assessing large language models for oncology data inference from radiology reports. JCO Clin Cancer Inform. 2024; 8: e2400126. https://doi.org/10.1200/cci.24....
36.
Chow JC, Li K. Ethical considerations in human-centered AI: Advancing oncology chatbots through large language models. JMIR Bioinform Biotechnol. 2024; 5: e64406. https://doi.org/10.2196/64406.
37.
Ong JCL, Chang SYH, William W, Butte AJ, Shah NH, Chew LST, et al. Ethical and regulatory challenges of large language models in medicine. Lancet Digit Health. 2024; 6(6): e428-e432. https://doi.org/10.1016/s2589-....
38.
Naik N, Hameed BM, Shetty DK, Swain D, Shah M, Paul R, et al. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility?. Front Surg. 2022; 9: 862322. https://doi.org/10.3389/fsurg.....
39.
Zhou Z, Qin P, Cheng X, Shao M, Ren Z, Zhao Y, et al. ChatGPT in oncology diagnosis and treatment: applications, legal and ethical challenges. Curr Oncol Rep. 2025; 27(4): 336-354. https://doi.org/10.1007/s11912....
40.
Verlingue L, Boyer C, Olgiati L, Mairesse CB, Morel D, Blay JY. Artificial intelligence in oncology: ensuring safe and effective integration of language models in clinical practice. Lancet Reg Health Eur. 2024; 46: 101064. https://doi.org/10.1016/j.lane....
41.
Kolla L, Parikh RB. Uses and limitations of artificial intelligence for oncology. Cancer. 2024; 130(12): 2101-2107. https://doi.org/10.1002/cncr.3....
42.
Rydzewski NR, Dinakaran D, Zhao SG, Ruppin E, Turkbey B, Citrin DE, et al. Comparative evaluation of LLMs in clinical oncology. NEJM AI. 2024; 1(5). https://doi.org/10.1056/aioa23....
43.
Chung EM, Zhang SC, Nguyen AT, Atkins KM, Sandler HM, Kamrava M. Feasibility and acceptability of ChatGPT generated radiology report summaries for cancer patients. Digit Health. 2023; 9: 20552076231221620. https://doi.org/10.1177/205520....
44.
Sallam M. ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare. 2023; 11(6): 887. https://doi.org/10.3390/health....
45.
Iannantuono GM, Bracken-Clarke D, Floudas CS, Roselli M, Gulley JL, Karzai F. Applications of large language models in cancer care: current evidence and future perspectives. Front Oncol. 2023; 13: 1268915. https://doi.org/10.3389/fonc.2....
46.
Li L, Dinh L, Hu S, Hemphill L. Academic collaboration on large language model studies increases overall but varies across disciplines. arXiv. Forthcoming 2024. https://doi.org/10.48550/arXiv....
| eISSN: | 2354-0265 |
| ISSN: | 2353-6942 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
