Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross‐Sectional Investigation
Open accessPublished version of a preprint
Abstract
Purpose: Caregivers in pediatric oncology need accurate and understandable information about their child's condition, treatment, and side effects. This study assesses the performance of publicly accessible large language model (LLM)‐supported tools in providing valuable and reliable information to caregivers of children with cancer. Methods: In this cross‐sectional study, we evaluated the performance of the four LLM‐supported tools—ChatGPT (GPT‐4), Google Bard (Gemini Pro), Microsoft Bing Chat, and Google SGE—against a set of frequently asked questions (FAQs) derived from the Children's Oncology Group Family Handbook and expert input (In total, 26 FAQs and 104 generated responses). Five pediatric oncology experts assessed the generated LLM responses using measures including accuracy, clarity, inclusivity, completeness, clinical utility, and overall rating. Additionally, the content quality was evaluated including readability, AI disclosure, source credibility, resource matching, and content originality. We used descriptive analysis and statistical tests including Shapiro–Wilk, Levene's, Kruskal–Wallis H ‐tests, and Dunn's post hoc tests for pairwise comparisons. Results: ChatGPT shows high overall performance when evaluated by the experts. Bard also performed well, especially in accuracy and clarity of the responses, whereas Bing Chat and Google SGE had lower overall scores. Regarding the disclosure of responses being generated by AI, it was observed less frequently in ChatGPT responses, which may have affected the clarity of responses, whereas Bard maintained a balance between AI disclosure and response clarity. Google SGE generated the most readable responses whereas ChatGPT answered with the most complexity. LLM tools varied significantly ( p < 0.001) across all expert evaluations except inclusivity. Through our thematic analysis of expert free‐text comments, emotional tone and empathy emerged as a unique theme with mixed feedback on expectations from AI to be empathetic. Conclusion: LLM‐supported tools can enhance caregivers' knowledge of pediatric oncology. Each model has unique strengths and areas for improvement, indicating the need for careful selection based on specific clinical contexts. Further research is required to explore their application in other medical specialties and patient demographics, assessing broader applicability and long‐term impacts.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
LLMs have the potential to provide real-time, individually customized, and up-to-date medical information, with concomitant risks and benefits, to allow for informed, complex, and empathic decision-making among caregivers for oncology patients.
“Of particular interest is the potential of LLMs as providers of real-time, individually customized, and up-to-date medical information, with their concomitant risks and benefits, to allow for informed, complex, and empathic decision-making among caregivers for oncology patients [30].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [30]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877 · see citing works
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
A Comprehensive Overview of Large Language Models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257 · see citing works
Reddy, S. (2024) · Implementation Science · doi:10.1186/s13012-024-01357-9 · Cited by 554 · see citing works
Plantin, L., & Daneback, K. (2009) · BMC Family Practice · doi:10.1186/1471-2296-10-34 · Cited by 403 · see citing works
Leveraging large language models for decision support in personalized oncology
Benary, M., Wang, X. D., Schmidt, M., Soll, D., Hilfenhaus, G., Nassir, M., Sigler, C., Knödler, M., Keller, U., Beule, D., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.43689 · Cited by 306 · see citing works
Sezgın, E. (2023) · Digital Health · doi:10.1177/20552076231186520 · Cited by 279 · see citing works
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
Wang, H., Wang, S., Lin, L., Yang, Y., Wang, S., & Wen, H. (2024) · doi:10.1145/3627673 · Cited by 187 · see citing works
Large Language Models in Education: Vision and Opportunities
Gan, W., Qi, Z., Wu, J., & Lin, J. C. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386291 · Cited by 175 · see citing works
AI-Generated Draft Replies Integrated Into Health Records and Physicians’ Electronic Communication
Tai-Seale, M., Baxter, S. L., Vaida, F., Walker, A., Sitapati, A. M., Osborne, C., Diaz, J., Desai, N., Webb, S., Polston, G., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.6565 · Cited by 158 · see citing works
Large Language Models are Geographically BiasedPreprint
Manvi, R., Khanna, S., Burke, M., Lobell, D. B., & Ermon, S. (2024) · International Conference on Machine Learning · doi:10.48550/arxiv.2402.02680 · Cited by 147 · see citing works
Evaluating Verifiability in Generative Search Engines
Liu, N., Zhang, T., & Liang, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.467 · Cited by 143 · see citing works
Evaluating Verifiability in Generative Search Engines
Liu, N. F., Zhang, T., & Liang, P. (2023) · arXiv · Cited by 143 · see citing works
Quality of Large Language Model Responses to Radiation Oncology Patient Care Questions
Yalamanchili, A., Sengupta, B., Song, J., Lim, S. N., Thomas, T. O., Mittal, B. B., Abazeed, M. E., & Teo, P. T. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.4630 · Cited by 128 · see citing works
Yang, Z., Khatibi, E., Nagesh, N., Abbasian, M., Azimi, I., Jain, R., & Rahmani, A. M. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100465 · Cited by 121 · see citing works
The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
Schulhoff, S., Ilie, M., Balepur, N., Kahadze, K., Liu, A., Si, C., Li, Y., Gupta, A., Han, H., Schulhoff, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2406.06608 · Cited by 118 · see citing works
Moons, P., & Bulck, L. V. (2023) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvad087 · Cited by 115 · see citing works
Applications of large language models in cancer care: current evidence and future perspectives
Iannantuono, G. M., Bracken-Clarke, D., Floudas, C. S., Roselli, M., Gulley, J. L., & Karzai, F. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1268915 · Cited by 115 · see citing works
Potential of large language models in health care: Delphi study
Denecke, K., May, R., LLMHealthGroup, & Rivera Romero, O. (2024) · Journal of Medical Internet Research · doi:10.2196/52399 · Cited by 111 · see citing works
Large Language Model–Based Responses to Patients’ In-Basket Messages
Small, W. R., Wiesenfeld, B. M., Brandfield-Harvey, B., Jonassen, Z., Mandal, S., Stevens, E. R., Major, V. J., Lostraglio, E., Szerencsy, A. C., Jones, S., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.22399 · Cited by 108 · see citing works
Wan, P., Huang, Z., Tang, W., Nie, Y., Pei, D., Deng, S., Chen, J., Zhou, Y., Duan, H., Chen, Q., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03148-7 · Cited by 97 · see citing works
AI in healthcare: navigating opportunities and challenges in digital communication
Sun, G., & Zhou, Y. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1291132 · Cited by 95 · see citing works
Gala, D., & Makaryus, A. N. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20156438 · Cited by 94 · see citing works
ChatGPT vs Google for Queries Related to Dementia and Other Cognitive Decline: Comparison of Results
Hristidis, V., Ruggiano, N., Brown, E. L., Ganta, S. R. R., & Stewart, S. (2023) · Journal of Medical Internet Research · doi:10.2196/48966 · Cited by 93 · see citing works
Thirunavukarasu, A. J., Mahmood, S., Malem, A. H., Foster, W., Sanghera, R., Hassan, R., Zhou, S. X., Wong, S. W., Wong, Y. L., Chong, Y. J., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000341 · Cited by 92 · see citing works
Large language model use in clinical oncology
Carl, N., Schramm, F., Haggenmüller, S., Kather, J. N., Hetz, M. J., Wies, C., Michel, M. S., Wessels, F., & Brinker, T. J. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00733-4 · Cited by 82 · see citing works
Performance of Large Language Models on Medical Oncology Examination Questions
Longwell, J. B., Hirsch, I., Binder, F., Gonzalez Conchas, G. A., Mau, D., Jang, R., Krishnan, R. G., & Grant, R. C. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.17641 · Cited by 81 · see citing works
Ayre, J., Bonner, C., Muscat, D. M., Dunn, A. G., Harrison, E., Dalmazzo, J., Mouwad, D., Aslani, P., Shepherd, H. L., & McCaffery, K. J. (2023) · JMIR Formative Research · doi:10.2196/40645 · Cited by 70 · see citing works
Bangerter, L. R., Griffin, J. M., Harden, K., & Rutten, L. J. F. (2018) · JMIR Aging · doi:10.2196/11237 · Cited by 66 · see citing works
ChatGPT vs. Google: A Comparative Study of Search Performance and User ExperiencePreprint
Xu, R., Feng, Y., & Chen, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4498671 · Cited by 66 · see citing works
Sezgin, E., Chekeni, F., Lee, J., & Keim, S. (2023) · Journal of Medical Internet Research · doi:10.2196/49240 · Cited by 65 · see citing works
The perils and promises of fact-checking with large language models
Quelle, D., & Bovet, A. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1341697 · Cited by 65 · see citing works
Introduction to large language models (LLMs) for dementia care and research
Treder, M. S., Lee, S., & Tsvetanov, K. A. (2024) · Frontiers in Dementia · doi:10.3389/frdem.2024.1385303 · Cited by 63 · see citing works
Medical large language models are susceptible to targeted misinformation attacks
Han, T., Nebelung, S., Khader, F., Wang, T., Müller-Franzes, G., Kuhl, C., Försch, S., Kleesiek, J., Haarburger, C., Bressem, K. K., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01282-7 · Cited by 62 · see citing works
Carmona, K. A., Chittamuru, D., Kravitz, R. L., Ramondt, S., & Ramírez, A. S. (2022) · Journal of Medical Internet Research · doi:10.2196/36322 · Cited by 54 · see citing works
MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
Tang, L., Laban, P., & Durrett, G. (2024) · arXiv · doi:10.48550/arxiv.2404.10774 · Cited by 53 · see citing works
Teaching language models to support answers with verified quotes
Menick, J., Trebacz, M., Mikulik, V., Aslanides, J., Song, F., Chadwick, M., Glaese, M., Young, S., Campbell-Gillingham, L., Irving, G., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.11147 · Cited by 53 · see citing works
Comparison of Large Language Models in Answering Immuno-Oncology Questions: A Cross-Sectional Study
Iannantuono, G. M., Bracken-Clarke, D. M., Karzai, F. H., Choo‐Wosoba, H., Gulley, J. L., & Floudas, C. S. (2024) · The Oncologist · doi:10.1093/oncolo/oyae009 · Cited by 52 · see citing works
Parental Decision-Making for Children With Medical Complexity: An Integrated Literature Review
Jonas, D. F., Scanlon, C. M., & Bogetz, J. F. (2021) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2021.07.029 · Cited by 50 · see citing works
Systematic Biases in LLM Simulations of Debates
Taubenfeld, A., Dover, Y., Reichart, R., & Goldstein, A. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.16 · Cited by 47 · see citing works
Inherent bias in large language models: a random sampling analysis.
Ayoub, N. F., Balakrishnan, K., Ayoub, M. S., Barrett, T. F., David, A. P., & Gray, S. T. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.03.003 · Cited by 47 · see citing works
Neo, E. J. R., Ser, J. S., & Tay, S. S. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1395501 · Cited by 45 · see citing works
Parents’ health information seeking behaviour – does the child’s health status play a role?
Baumann, I., Jaks, R., Robin, D., Juvalta, S., & Dratva, J. (2020) · BMC Family Practice · doi:10.1186/s12875-020-01342-3 · Cited by 39 · see citing works
Machines and empathy in medicine
Topol, E. J. (2023) · The Lancet · doi:10.1016/s0140-6736(23)02292-4 · Cited by 37 · see citing works
KnowNet:Guided Health Information Seeking from LLMs via Knowledge Graph Integration
Yan, Y., Hou, Y., Xiao, Y., Zhang, R., & Wang, Q. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456364 · Cited by 37 · see citing works
Parmanto, B., Aryoyudanta, B., Soekinto, T. W., Setiawan, I. M. A., Wang, Y., Hu, H., Saptono, A., & Choi, Y. K. (2024) · JMIR Formative Research · doi:10.2196/54633 · Cited by 35 · see citing works
Redefining Virtual Assistants in Health Care: The Future With Large Language Models
Sezgin, E. (2023) · Journal of Medical Internet Research · doi:10.2196/53225 · Cited by 32 · see citing works
Lang, S., Yoseph, E., Gonzalez-Suarez, A. D., Kim, R., Fatemi, P., Wagner, K. E., Maldaner, N., Stienen, M. N., & Zygourakis, C. C. (2024) · Neurospine · doi:10.14245/ns.2448098.049 · Cited by 32 · see citing works
Garza, M., Graetz, D. E., Kaye, E. C., Ferrara, G., Rodriguez, M., Vásquez, D. J. S., Aceituno, A. M., Antillón‐Klussmann, F., Gattuso, J. S., Mandrell, B. N., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.660051 · Cited by 31 · see citing works
Haltaufderheide, J., Wäscher, S., Bertlich, B., Vollmann, J., Reinacher‐Schick, A., & Schildmann, J. (2018) · The Oncologist · doi:10.1634/theoncologist.2017-0615 · Cited by 30 · see citing works
Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care
Bitterman, D. S., Downing, A., Maués, J., & Lustberg, M. B. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.23.02439 · Cited by 28 · see citing works
Large Language Models in Oncology: Revolution or Cause for Concern?
Caglayan, A., Slusarczyk, W., Rabbani, R. D., Ghose, A., Papadopoulos, V. P., & Boussios, S. (2024) · Current Oncology · doi:10.3390/curroncol31040137 · Cited by 27 · see citing works
Oeding, J. F., Lu, A. Z., Mazzucco, M., Fu, M. C., Taylor, S. A., Dines, D. M., Warren, R. F., Gulotta, L. V., Dines, J. S., & Kunze, K. N. (2025) · Arthroscopy · doi:10.1016/j.arthro.2024.05.025 · Cited by 27 · see citing works
Ostrowska, M., Kacała, P., Onolememen, D., Vaughan-Lane, K., Joseph, A. S., Ostrowski, A., Pietruszewska, W., Banaszewski, J., & Wróbel, M. (2024) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-024-08643-8 · Cited by 25 · see citing works
Hung, T. K. W., Kuperman, G. J., Sherman, E. J., Ho, A. L., Weng, C., Pfister, D. G., & Mao, J. J. J. (2024) · Journal of Medical Internet Research · doi:10.2196/60695 · Cited by 24 · see citing works
Xue, E., Bracken-Clarke, D. M., Iannantuono, G. M., Choo‐Wosoba, H., Gulley, J. L., & Floudas, C. S. (2024) · Journal of Medical Internet Research · doi:10.2196/54758 · Cited by 23 · see citing works
Gage‐Bouchard, E. A., LaValley, S. A., & Devonish, J. A. (2019) · Cancer Control · doi:10.1177/1073274819841609 · Cited by 18 · see citing works
Kubb, C., & Foran, H. M. (2022) · JMIR Pediatrics and Parenting · doi:10.2196/29618 · Cited by 18 · see citing works
Chua, J. Y. X., Choolani, M. A., Chee, C. Y. I., Yi, H., Chan, Y. H., Lalor, J., Chong, Y. S., & Shorey, S. (2024) · Journal of Medical Internet Research · doi:10.2196/56894 · Cited by 18 · see citing works
What is the impact of artificial intelligence-based chatbots on infodemic management?
Morita, P. P., Lotto, M., Kaur, J., Chumachenko, D., Oetomo, A., Espiritu, K. D., & Hussain, I. Z. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1310437 · Cited by 18 · see citing works
LLM on FHIR - Demystifying Health RecordsPreprint
Schmiedmayer, P., Rao, A., Zagar, P., Ravi, V., Zahedivash, A., Fereydooni, A., & Aalami, O. O. (2024) · arXiv.org · doi:10.48550/arxiv.2402.01711 · Cited by 18 · see citing works
O’Hagan, E. T., McIntyre, D., & Laranjo, L. (2023) · Hypertension · doi:10.1161/hypertensionaha.123.21395 · Cited by 17 · see citing works
Wang, J., Zhen, X., Coyte, P. C., Shao, D., Zhao, N., Chang, L., Feng, Y., & Sun, X. (2023) · Journal of Medical Internet Research · doi:10.2196/46953 · Cited by 11 · see citing works
Chen, Y., Zhang, S., Tang, N., George, D. M., Huang, T., & Tang, J. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1412063 · Cited by 10 · see citing works
Gubanov, M., Pyayt, A., & Karolak, A. (2024) · Proceedings of the 33rd ACM International Conference on Information and Knowledge Management · doi:10.1145/3627673.3680094 · Cited by 10 · see citing works
Understanding of Health-related Decision-making Terminology Among Cancer Caregivers
Moss, K. O., Douglas, S. L., Lipson, A. R., Blackstone, E. C., WILLIAMS, D. A., Aaron, S., & Wills, C. E. (2020) · Western Journal of Nursing Research · doi:10.1177/0193945920965238 · Cited by 9 · see citing works
Excoffier, J.-B., Röhr, T., Figueroa, A., Papaioannou, J.-M., Bressem, K. K., & Ortala, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.01943 · Cited by 9 · see citing works
Melesse, T. G., Chau, J. P. C., Li, W. H. C., & Yimer, M. A. (2023) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2023.102376 · Cited by 6 · see citing works
Chen, L., Zhao, X., Zhang, X., Liu, Y., Cheng, W., Wang, H., Chen, Z., Osaki, T., Matsuda, K., Chen, H., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.03433 · Cited by 3 · see citing works
Are Large Language Models Good Fact Checkers: A Preliminary StudyPreprint
Cao, H., Wei, L., Chen, M., Zhou, W., & Hu, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.17355 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [19]H. Zhou, F. Liu, B. Gu, et al., “A Survey of Large Language Models in Medicine: Progress, Application, and Challenge,” arXiv (2023).
- [26]S. Qi, Y. He, and Z. Yuan, “Can We Catch the Elephant? A Survey of the Evolvement of Hallucination Evaluation on Natural Language Generation,” arXiv (2024).
- [60]J. Anthis, K. Lum, M. Ekstrand, et al., “The Impossibility of Fair LLMs,” arXiv (2024).
Cited by
Sorted by citations
Artificial Intelligence Applications in Pediatric Craniofacial Surgery
Harrison, L. M., Edison, R. L., & Hallac, R. R. (2025) · Diagnostics · doi:10.3390/diagnostics15070829 · Cited by 28 · see citing works
Leveraging ChatGPT to strengthen pediatric healthcare systems: a systematic review
Douma, H., McNamara, C. L., Bakola, M., & Stückler, D. (2025) · European Journal of Pediatrics · doi:10.1007/s00431-025-06320-4 · Cited by 11 · see citing works
Potential of ChatGPT in youth mental health emergency triage: Comparative analysis with clinicians
Thotapalli, S., Yilanli, M., McKay, I. T., Leever, W. J., Youngstrom, E. A., Harvey, K., Lowder, K., Schweitzer, S., Sunderland, E., Jackson, D., et al. (2025) · Psychiatry and Clinical Neurosciences Reports · doi:10.1002/pcn5.70159 · Cited by 13 · see citing works
Self-citationLim, J. S., & Hong, N. (2025) · Health & New Media Research · doi:10.22720/hnmr.2025.00115 · Cited by 6 · see citing works
Sugan, M., Nadkarni, S., Sanaeipoor, S., Patel, S., Natarajamoorthy, S. Y., Rashidi, E., & Miller, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3721270 · Cited by 6 · see citing works
Review of Large Language Models for Patient and Caregiver Support in Cancer Care Delivery
Kouzy, R., Cha, E. E., Rosen, A., & Bitterman, D. S. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00044 · Cited by 8 · see citing works
Chen, J., & Chen, J. (2026) · JMIR Medical Informatics · doi:10.2196/80163 · Cited by 4 · see citing works
Yum, J.-E. I., Naqvi, S. A. A., Zhou, B., & Riaz, I. B. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00134 · Cited by 4 · see citing works
Anwar, S., Yusuf, S., Kada, M. K. R., & Farawansah, F. (2026) · JMIR AI · doi:10.2196/79551 · Cited by 3 · see citing works
Exploring evaluation measures of large language models for family caregiver use: A scoping review
Soojeong, H., Cho, H., Choi, Y. K., & Alexander, G. L. (2026) · Digital Health · doi:10.1177/20552076261425343 · Cited by 3 · see citing works
White, B. M., Aitken, G. L., Zink, J. A., Kheirkhah Rahimabad, P., Kumsa, F. A., Hashtarkhani, S., Rashid, R., Kheirinejad, S., Girdwood, T., Brett, C. L., et al. (2026) · JMIR Cancer · doi:10.2196/82336 · Cited by 2 · see citing works
“Of particular interest is the potential of LLMs as providers of real-time, individually customized, and up-to-date medical information, with their concomitant risks and benefits, to allow for informed, complex, and empathic decision-making among caregivers for oncology patients [30].”
Romanyukha, A. A., Mazloumi, M., Waelheyns, T. D., Mishra, N., Jacobs, J., & Fitousi, N. T. (2025) · Physica Medica · doi:10.1016/j.ejmp.2025.105090 · Cited by 2 · see citing works
Kuhlen, M., Hellmann, F., Pfaehler, E. A. G., André, E., & Redlich, A. (2026) · Biomedicines · doi:10.3390/biomedicines14010146 · Cited by 2 · see citing works
Li, Y., Lv, M., Cheng, L., Xie, Y., Gu, Q., He, H., Chen, Z., Fang, D., & Zhou, X. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.112734 · Cited by 1 · see citing works
Scoping review of AI–chatbots to support family caregivers
Gallagher, V. T., Sheehan, C., Ahmad, A., Ansari, S. M. A., Arp, A., Bell, S. A., Brown, D. E., Cruise, A., Greth, Z., Greer, S. R., et al. (2026) · The Gerontologist · doi:10.1093/geront/gnag028 · Cited by 1 · see citing works
The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review
Yang, E., Ko, S., & Woo, H. (2026) · Journal of Medical Internet Research · doi:10.2196/98184 · Cited by 1 · see citing works
Sanchez, G. A. R., Pérez, A. G., & Borges, C. M. F. (2024) · Seminars in Medical Writing and Education · doi:10.56294/mw2024393
Anwar, S., Yusuf, S., Kada, M. K. R., & Farawansah, F. (2025) · doi:10.2196/preprints.79551
Anil, K., Maramba, I. D. C., Cook, D. P., Cyrus, D., Fakharuzi, Z., Namdar, A. H., West, T. O., & Shirgill, S. (2026) · Healthcare Technology Letters · doi:10.1049/htl2.70102
Wang, P., Li, J., Xue, L., Yang, J., Wang, Z., & Su, W. (2025) · International Conference on Smart Healthcare and Wearable Intelligent Devices (SHWID) · doi:10.1145/3788112.3788138
Kuhlen, M., Brecht, I. B., & Orbach, D. H. (2026) · Pediatric Blood & Cancer · doi:10.1002/1545-5017.70078
Xu, H., Liu, J., & Cheng, Q. (2026) · Applied Sciences · doi:10.3390/app16125827
Yıldırım, A., Turan, A., Gerçek, H., & Çaka, G. (2026) · Revista da Associação Médica Brasileira · doi:10.1590/1806-9282.20251434
Kang, T., Yuan, C., Hu, X., & Huang, W. (2026) · Journal of Medical Internet Research · doi:10.2196/93393
Pediatric Oncology Knowledge Mobilization in Canada: A Environmental Scan
Foulem, C., Drake, E. K., Lui, A., Dias, S., Damoulianos, E., Reid, S., Cossette, P., Duval, M., Efremov, K., Foster, J., et al. (2026) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580261468769
Wang, C., Liu, Z., Jiang, Y., Zhang, T., Wang, H., Xue, T., & Liu, P. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1850996
James, L., Dieleman, C., Evers, S., Ribeiro, M. I., Genga, L., Montagne, B., & Gorp, P. M. E. V. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-27585-1_7
Ríos, M. D. G., Martínez, M. Á. Q., Arias, S. T. C., & Kourilovitch, M. (2026) · Healthcare · doi:10.3390/healthcare14152424
Düzova, M., Düzova, Ü. S., Cihan, E., & Pirinççi, C. Ş. (2026) · Work · doi:10.1177/10519815261491755
Ugwuanyi, R. N. C., Jibrin, Y. A., Oladokun, B. D., Okai, E. D. J., & Abah, B. E. (2026) · Journal of Hospital Librarianship · doi:10.1080/15323269.2026.2749778
- Authors
- Sezgin, E., Jackson, D. I., Kocaballi, A. B., Bibart, M., Zupanec, S., Landier, W., Audino, A., Ranalli, M., & Skeens, M.
- Affiliations
- The Abigail Wexner Research Institute at Nationwide Children's Hospital Columbus Ohio USA
- Nationwide Children's Hospital (US)
- Macquarie University (AU)
- Hospital for Sick Children (CA)
- University of Alabama at Birmingham (US)
- ORCID
- Venue
- Cancer Medicine
- Publisher
- Wiley
- Published
- 2025-01
- Volume
- 14
- Issue
- 1
- Pages
- e70554-e70554
- ISSN
- 2045-7634 (ISSN-L)
- PMC
- PMC11705392
- OpenAlex
- W4406147515
- Cited by
- 30
- References
- 68
- Citations by year
- 2026: 19 · 2025: 10 · 2024: 1
- FWCI
- 6.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancer Medicine (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- large language models, childhood cancer, ChatGPT, Google Bard, Bing Chat, AI-generated health information, information accuracy, AI disclosure, readability, frequently asked questions
- MeSH terms
- Child, Cross-Sectional Studies, Female, Humans, Language, Male, Neoplasms, Caregivers, Information Seeking Behavior
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
