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Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72638, first published .
Enhancing Pulmonary Disease Prediction Using Large Language Models With Feature Summarization and Hybrid Retrieval-Augmented Generation: Multicenter Methodological Study Based on Radiology Report

Enhancing Pulmonary Disease Prediction Using Large Language Models With Feature Summarization and Hybrid Retrieval-Augmented Generation: Multicenter Methodological Study Based on Radiology Report

Enhancing Pulmonary Disease Prediction Using Large Language Models With Feature Summarization and Hybrid Retrieval-Augmented Generation: Multicenter Methodological Study Based on Radiology Report

Journals

  1. Anghelescu A, Munteanu C, Anghelescu L, Onose G. ”A Midsummer Night’s Dream” quest for truth: From ChatGPT “hallucinations” to RAG reasoning and ACURAI precision — a scoping review on detection, minimizing, and (almost) complete error elimination and enhancing Large Language Models' re-liability. Balneo and PRM Research Journal 2025;16(Vol 16 No. 3):847 View
  2. Niu Y, Shen L, Liu J, Yang Z, Wang L, Cheng Y. Exploring the feasibility of inferring prostate cancer pathological grade from multiparametric MRI text reports using natural language processing: assessment of four large language models. Abdominal Radiology 2026 View