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This paper is in the following e-collection/theme issue:

Patient Education for Cancer (245) Generative Language Models Including ChatGPT (813) Chatbots and Conversational Agents (787) Machine Learning (2230) Formative Evaluation of Digital Health Interventions (3453) Information Seeking, Information Needs (581) Quality/Credibility of eHealth Information and Trust Issues (674) Innovations and Technology in Cancer Care (569)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70176, first published .
Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study

Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study

Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study

Authors of this article:

Sota Nishisako1 Author Orcid Image ;   Takahiro Higashi1, 2 Author Orcid Image ;   Fumihiko Wakao3 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Roca P, Zangri R, Rodriguez-Fernandez G, Sanchez-Pedreño M, García del Valle E. Artificial intelligence in the psychologist’s toolkit: Psypilot as a case study. Frontiers in Psychology 2026;17 View
  2. Dehdab R, Afat S, Mankertz F, Brendel J, Maalouf N, Werner S, Brendlin A, Herrmann J, Nikolaou K, Kloker L, Calukovic B, Benzler K, Zender L, Deinzer C. When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions. Journal of Cancer Research and Clinical Oncology 2026;152(2) View
  3. Resnik D, Hosseini M. Hallucinated citations produced by generative artificial intelligence may constitute research misconduct when citations function as data in scholarly papers. Accountability in Research 2026 View