ReviewJournal of thoracic disease2025
Narrative review of the application of artificial intelligence-related technologies in the diagnosis of pulmonary nodules with recommendations for clinical practice and future research.
Review in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Challenges and Limitations in Molecular Testing of Resected Non-Small Cell Lung Cancer Specimens.Current issues in molecular biology · 2026Review
- Advantages of integrating artificial intelligence and spectral CT for lung nodule classification and prognostic judgment: a narrative review.Journal of thoracic disease · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Lung cancer has the highest global incidence and mortality rate among malignancies, primarily due to the complexity and diversity of early lung nodules and tumor changes, in addition to the involvement of numerous other factors, such as delayed screening for early lung nodules and difficulties in treatment. With the continuous optimization of artificial intelligence (AI) models, novel paradigms are emerging in the detection of malignant pulmonary nodules, histopathologic subtyping, and genomic prediction-advances that increasingly inform clinical assessment and therapeutic decision-making. This article reviews Chinese and English research over the past 10 years and explores the current application status of AI in the diagnosis of pulmonary nodules. Methods: Literature available primarily from the PubMed, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI) databases was collected and analyzed to characterize the current state of AI application in the diagnosis of pulmonary nodules across different disciplines. The following keywords were used in the search: "lung cancer", "ground-glass nodules (GGN)", "artificial intelligence", "deep learning", "machine learning", "radiomics", "pathology of lung cancer", and "genomics". The search is limited to English and Chinese publications within the last 10 years, with no restrictions on the age or country of the participants. Key Content and Findings: This study reviews the application of AI in pulmonary nodule diagnosis across imaging, pathology, and genetics. Recently, AI technology has found extensive applications in the healthcare sector. AI significantly enhances the accuracy of early diagnosis and the efficiency of individualized decision-making. However, the standardization of models is insufficient, external validation is scarce, and large-scale prospective studies are still needed to support clinical applications. Conclusions: AI is expected to play a significant role in the diagnosis, radiomics, pathology and genomics of pulmonary nodules in the future. Existing evidence can support the differentiation between benign and malignant pulmonary nodules, improve diagnostic efficiency, and predict pathological types and gene mutations. Thus, AI has a promising application prospect in clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.