ArticleCell reports. Medicine2025
Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.
Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The potential of large language models in the field of infertility: a systematic review.Journal of assisted reproduction and genetics · 2025Pooled it
- Large Language Models in Lung Cancer: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Large language models with image processing in automated Cobb angle.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases.Cell reports. Medicine · 2026Article
- Article
- Review
- A narrative review on CT-based evaluation and prediction of lung nodule growth: current status and future directions.Translational lung cancer research · 2026Review
- Preoperative and intraoperative localization of small pulmonary nodules for thoracoscopic surgery: a contemporary narrative review.Journal of robotic surgery · 2026Review
- Diagnostic Performance of a Large Language Model (ChatGPT-4o) in Chronic Rhinosinusitis CT Scan Interpretation.Laryngoscope investigative otolaryngology · 2026Article
- [Expert Consensus on Precision Management of Pulmonary Nodules (2026 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Article
- Stacked CT radiomics, deep learning and clinical feature models for differentiating benign and malignant solitary pulmonary nodules.Scientific reports · 2026Article
- Retrieval‑augmented large language models for depression screening and suicide risk stratification.BMC psychiatry · 2026Article
- Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.Journal of thoracic disease · 2026Review
- [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Construction and validation of a prediction model for malignant pulmonary nodules based on imaging, demographic, and laboratory features.Frontiers in oncology · 2026Article
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- Comparing large language models and search engine responses to common orthodontic questions.PloS one · 2026Article
- Promises and challenges of applying large language models in the healthcare domain.Frontiers in digital health · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tomography (CT) images of patients with lung nodules is unclear. In this study, we evaluate the ability of the latest generative pre-trained transformer (GPT)-4o model to assess changes in malignancy probability, size, and features of lung nodules on longitudinal CT scans from 647 patients (547 from two local centers and 100 from a public dataset). GPT-4o achieves an average accuracy of 0.88 in predicting lung nodule malignancy compared to pathological results and an average intraclass correlation coefficient of 0.91 in measuring nodule size compared with manual measurements by radiologists. Six radiologists' evaluations demonstrate GPT-4o's ability to capture changes in nodule features with a median Likert score of 4.17 (out of 5.00). In summary, GPT-4o could capture dynamic changes in lung nodules across longitudinal follow-up CT images, thus providing high-quality radiological evidence to assist in clinical management.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.