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

Natural Language Processing (856) Clinical Informatics (1240) Ontologies, Classifications, and Coding (303) Machine Learning (1931) Development and Evaluation of Research Methods, Instruments and Tools (691) Artificial Intelligence (1961) Tools, Programs and Algorithms (351) Decision Support for Health Professionals (1448) Generative Language Models Including ChatGPT (614) Lung Cancer (53) Immunoinformatics and Pathology informatics (16)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67056, first published .
Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study

Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study

Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study

Authors of this article:

Sanghwan Kim1 Author Orcid Image ;   Sowon Jang2 Author Orcid Image ;   Borham Kim3 Author Orcid Image ;   Leonard Sunwoo2 Author Orcid Image ;   Seok Kim3 Author Orcid Image ;   Jin-Haeng Chung4, 5 Author Orcid Image ;   Sejin Nam1 Author Orcid Image ;   Hyeongmin Cho1 Author Orcid Image ;   Donghyoung Lee1 Author Orcid Image ;   Keehyuck Lee6 Author Orcid Image ;   Sooyoung Yoo3 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Yao G, Zhang W, Zhu Y, Wong U, Zhang Y, Yang C, Shen G, Li Z, Gao H. Comparing the accuracy of large language models and prompt engineering in diagnosing realworld cases. International Journal of Medical Informatics 2025;203:106026 View
  2. Haixian L, Shu P, Zhao L, Chunfeng L, Lun L. Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review. Frontiers in Oncology 2025;15 View
  3. Chu X, Ye Y, Tang S, Han M, Wang G, Lin S, Sun B, Huang Q, Zhang Y, Chu X, Bao K. Personalized Medication for Chronic Diseases Using Multimodal Data‐Driven Chain‐of‐Decisions. Advanced Science 2025 View