ReviewCurrent oncology (Toronto, Ont.)2023
Artificial Intelligence for Cancer Detection-A Bibliometric Analysis and Avenues for Future Research.
Review in Current oncology (Toronto, Ont.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Trends in artificial intelligence and machine learning for renal cancer.Discover oncology · 2025Article
- Socioeconomic impact of artificial intelligence-driven point-of-care testing devices for liquid biopsy in the OncoCheck system.Cancer metastasis reviews · 2025Review
- Entanglement between pharmacy/pharmaceutical education and cancer? A bibliometric answer.Asian biomedicine : research, reviews and news · 2025Article
- Global Research Trends in Artificial Intelligence and Type 2 Diabetes Mellitus: A Bibliometric Perspective.Cureus · 2025Review
- Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning.Scientific reports · 2024Article
- Development trends and knowledge framework of artificial intelligence (AI) applications in oncology by years: a bibliometric analysis from 1992 to 2022.Discover oncology · 2024Article
- Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis.International ophthalmology · 2024Article
- The global patent landscape of artificial intelligence applications for cancer.Nature biotechnology · 2023Article
- Visualization of the relationship between fungi and cancer from the perspective of bibliometric analysis.Heliyon · 2023Article
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
No grant is acknowledged in the PubMed record.
Abstract
After cardiovascular diseases, cancer is responsible for the most deaths worldwide. Detecting a cancer disease early improves the chances for healing significantly. One group of technologies that is increasingly applied for detecting cancer is artificial intelligence. Artificial intelligence has great potential to support clinicians and medical practitioners as it allows for the early detection of carcinomas. During recent years, research on artificial intelligence for cancer detection grew a lot. Within this article, we conducted a bibliometric study of the existing research dealing with the application of artificial intelligence in cancer detection. We analyzed 6450 articles on that topic that were published between 1986 and 2022. By doing so, we were able to give an overview of this research field, including its key topics, relevant outlets, institutions, and articles. Based on our findings, we developed a future research agenda that can help to advance research on artificial intelligence for cancer detection. In summary, our study is intended to serve as a platform and foundation for researchers that are interested in the potential of artificial intelligence for detecting cancer.
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.