ReviewCancers2022
Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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.
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Who cites it
24 citing papers in PubMed.
- Machine Learning for the Interpretation of Serum Protein and Immunofixation Electrophoresis in Multiple Myeloma: A Scoping Review.Diagnostics (Basel, Switzerland) · 2026Review
- Fusion of Clinical and Deep Learning Features for Predicting Pembrolizumab Monotherapy Response in Advanced Non-Small Cell Lung Cancer.Journal of clinical medicine · 2026Article
- Improving risk stratification of pulmonary nodules: an integrated perinodular vascular and radiomic model for clinical decision support.BMC medical imaging · 2026Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.Journal of imaging informatics in medicine · 2025Article
- Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review.Cureus · 2025Review
- Te Oranga Pūkahukahu research programme: intentional steps towards a national equity-focused lung cancer screening programme in Aotearoa New Zealand.Journal of the Royal Society of New Zealand · 2025Article
- The blind spots on chest computed tomography: what do we miss.Journal of thoracic disease · 2024Review
- Article
- Recommendations for the creation of benchmark datasets for reproducible artificial intelligence in radiology.Insights into imaging · 2024Article
- Enhancing Lung Nodule Classification: A Novel CViEBi-CBGWO Approach with Integrated Image Preprocessing.Journal of imaging informatics in medicine · 2024Article
- A survey of recent methods for addressing AI fairness and bias in biomedicine.Journal of biomedical informatics · 2024Review
- Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.European radiology experimental · 2024Article
- Article
- Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results.Frontiers in public health · 2024Article
- Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives.Therapeutic advances in vaccines and immunotherapy · 2024Review
- Article
- Opportunities and challenges for ChatGPT and large language models in biomedicine and health.Briefings in bioinformatics · 2023Article
- Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health.ArXiv · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely.
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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.