ReviewCancers2022
The Role of Artificial Intelligence in Early Cancer Diagnosis.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 3 of them syntheses that pooled it.
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
109 citing papers in PubMed, 3 syntheses or guidelines pooled it, 308 citations in OpenAlex.
- Applications of AI-based deep learning models for detecting dental caries on intraoral images - a systematic review.Evidence-based dentistry · 2025Pooled it
- Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis.Frontiers in big data · 2024Pooled it
- Association of prognostic nutritional index with prognostic outcomes in patients with glioma: a meta-analysis and systematic review.Frontiers in oncology · 2023Pooled it
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Using machine learning algorithms based on laboratory indicators to establish a diagnostic model for lung cancer.BMC cancer · 2026Article
- Reasoning in machine vision by learning fast and slow thinking.Nature communications · 2026Article
- Transformer-Based Feature Extraction and Optimized Deep Neural Network for Gastric Cancer Detection.Journal of imaging informatics in medicine · 2026Article
- Comparing loss of contractual employment pre- and post-diagnosis in patients with rare versus common cancer types: a national registry-based study.Journal of cancer survivorship : research and practice · 2026Article
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- The last human touch: why AI doesn't belong in life's defining moments.BMC medical ethics · 2026Article
- Artificial intelligence in diagnostic imaging: collaborative asset or looming replacement?Oral radiology · 2026Review
- Article
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 2026Review
- A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing.Scientific reports · 2026Article
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- The Role of Artificial Intelligence in Shaping the Doctor-Patient Relationship: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Integrative bioinformatics approaches for early detection biomarkers in ovarian cancer.Annals of medicine and surgery (2012) · 2026Review
- Exploring advancements in the management of penile cancer in the era of artificial intelligence and machine learning: a narrative review.Annals of medicine and surgery (2012) · 2026Article
- Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.
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.