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Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70176, first published .
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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

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
  4. Košprdić M, Ljajić A, Bašaragin B, Medvecki D, Cassano L, Milošević N. VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering. IEEE Access 2026;14:45129 View
  5. Sevryugina Y, Vargas D. Teaching Research Integrity through Verification of AI-Generated References: An Activity for Upper-Level Chemistry Courses. Journal of Chemical Education 2026;103(5):2610 View
  6. Karni J, Simon C, Hack S. Assistive, not autonomous: Generative artificial intelligence in head and neck cancer care - A scoping review. DIGITAL HEALTH 2026;12 View
  7. Goel R, Mustafa S, Baker T, Bierer B. Large Language Models in Informed Consent — Opportunities, Evidence, and Challenges. NEJM AI 2026 View

Conference Proceedings

  1. Kim Y, Kim M, Kim S, Cho S, Kim J. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. Design and Multi-level Evaluation of MAP-X: a Medically Aligned, Patient-Centered AI Explanation System View