ReviewBMC medical research methodology2026
Artificial intelligence and machine learning in non-small cell lung cancer: the current state of the science on multi-omic applications.
Review in BMC medical research methodology, 2026. 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.
- Consolidated Evidence and New Frontiers of Liquid Biopsy in Lung Cancer: A Narrative Review.Cells · 2026Review
- Clinical significance of miRNAs and exosomal miRNAs in non-small cell lung cancer: diagnostic, prognostic, and therapeutic perspectives.Journal of human genetics · 2026Review
- Precision immuno-oncology in NSCLC: integrating ADCs, therapeutic vaccines, and adoptive cell therapies for next-generation systemic treatment.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases and remains a leading cause of cancer-related mortality globally. The biological heterogeneity of NSCLC challenges early detection, accurate staging, and optimal therapy. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the integration of high-dimensional clinical, genomic, and imaging data, transforming cancer care. This narrative review critically examines recent AI/ML applications in NSCLC, emphasizing clinical validation, interpretability, and ethical deployment. We synthesize data from leading studies on imaging, genomics, and multimodal integration, highlighting how deep learning and ensemble methods have begun to outperform traditional diagnostic workflows. Persistent challenges, including dataset diversity, lack of external validation, interpretability, and ethical considerations must be addressed to realize translational impact. We advocate for large-scale, interdisciplinary collaboration to advance AI-powered personalized medicine and improve patient outcomes in NSCLC.
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.