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Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77334, first published .
Medical professionals reviewing patient data on computer screens in a modern clinic

Performance of Large Language Models in Diagnosing Rare Hematologic Diseases and the Impact of Their Diagnostic Outputs on Physicians: Combined Retrospective and Prospective Study

Performance of Large Language Models in Diagnosing Rare Hematologic Diseases and the Impact of Their Diagnostic Outputs on Physicians: Combined Retrospective and Prospective Study

Journals

  1. Pantel J, Hübel K, Fluch-Niebuhr J, Hentrich M, Liedgens P, Hermeneit S, Lipp T, Maoz D, Kirchhoff J, Mücke M, Dalhaus L, Rott U, van Rooij N. Früherkennung seltener Erkrankungen mit künstlicher Intelligenz. Zeitschrift für Allgemeinmedizin 2026;102(2):86 View
  2. Awan S, Khattak M, Khan A, Sathio A, Alsayaydeh J, Bacarra R, Herawan S, Aziz R. Meta-analysis of large language models: benchmarking DeepSeek-R1 against ChatGPT, Gemini, Qwen, and LLaMA. Journal of Big Data 2025;13(1) View
  3. Scott I. Can AI assist in reducing diagnostic error? A narrative review. Diagnosis 2026 View
  4. Li T, Ma Z, Wang B, Fan N, Wang A, Zang L. Diagnostic Performance of Large Language Models for Orthopedic-Related Rare Diseases and Their Impact on Physicians’ Diagnostic Accuracy: 2-Stage Comparative Evaluation Study Based on the Chinese Rare Disease Catalog. Journal of Medical Internet Research 2026;28:e92931 View