Google's lung cancer AI: a promising tool that needs further validation.

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

13 citing papers in PubMed.

1. Radiomics machine learning models for lung cancer early diagnosis in heterogenous multicentre chest CT data: LIBRA study results.European radiology experimental · 2026
   Article
2. CellTypeAI: cell annotation for scRNA-seq using local generative-AI.Bioinformatics (Oxford, England) · 2026
   Article
3. Decoding the enigma of multiple primary lung cancers: from mechanism to bedside-a narrative review.Translational lung cancer research · 2025
   Review
4. Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024
   Review
5. Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes.Cancers · 2023
   Review
6. Squeeze-MNet: Precise Skin Cancer Detection Model for Low Computing IoT Devices Using Transfer Learning.Cancers · 2022
   Article
7. Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov.Diagnostics (Basel, Switzerland) · 2022
   Article
8. Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review.Journal of thoracic disease · 2021
   Review
9. Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists.Radiology. Artificial intelligence · 2021
   Article
10. [Research Progress in Distinguishing Methods of Simultaneous Multiple Primary Lung Cancer and Intrapulmonary Metastasis].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2021
    Review
11. Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?Translational lung cancer research · 2021
    Review
12. Artificial Intelligence and its future potential in lung cancer screening.EXCLI journal · 2020
    Review
13. Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov.Frontiers in oncology · 2020
    Article

Corrections and comments

• Comment on
  End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.2019

Authors and funding

2 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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