ArticleCancer innovation2025
Artificial Intelligence-Based Methods: The Path Forward in Achieving Equity in Lung Cancer Screening and Evaluation.
Article in Cancer innovation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Implementation of a lung cancer screening program at an urban safety-net hospital: enabling solutions via stakeholder engagement.Frontiers in public health · 2026Article
- Emerging Techniques of Translational Research in Immuno-Oncology: A Focus on Non-Small Cell Lung Cancer.Cancers · 2025Review
- Comparative effectiveness of low-dose CT lung cancer screening among high-risk non-smoking female subgroups and males in China.Frontiers in oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Although lung cancer remains a global threat to public health, evidenced based advances in screening and prevention hold promise for reducing its impact on mortality. An ongoing challenge facing the clinical and research community are the glaring disparities in access to preventive services faced by ethnically and socioeconomically marginalized groups. In this context, novel approaches are needed to improve research methods and thus bolster our ability to improve outcomes. Artificial intelligence (AI) applications such as machine learning and natural language processing hold promise as catalysts in this process, enhancing speed, accuracy and capability. This perspective will highlight the potential of AI methods as essential tool for growth across the lung cancer diagnostic continuum from screening to diagnosis.
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.