Artificial Intelligence Approach in Prostate Cancer Diagnosis: Bibliometric Analysis
Bibliographic record
Abstract
Background. Prostate cancer is one of the most common male malignancies worldwide that ranks second in cancer-related mortality. Artificial intelligence can reduce subjectivity and improve the efficiency of prostate cancer diagnosis using fewer resources as compared to standard diagnostic scheme. This review aims to highlight the main concepts of prostate cancer diagnosis and artificial intelligence application and to determine achievements, current trends, and potential research directions in this field, using bibliometric analysis. Materials and Methods.The studies on the application of artificial intelligence in the morphological diagnosis of prostate cancer for the past 35 years were searched for in the Scopus database using “artificial intelligence” and “prostate cancer” keywords. The selected studies were systematized using Scopus bibliometric tools and the VOSviewer software. Results. The number of publications in this research field has drastically increased since 2016, with most research carried out in the United States, Canada, and the United Kingdom. They can be divided into three thematic clusters and three qualitative stages in the development of this research field in timeline aspect. Conclusions. Artificial intelligence algorithms are now being actively developed, playing a huge role in the diagnosis of prostate cancer. Further development and improvement of artificial intelligence algorithms have the potential to automate and standardize the diagnosis of prostate cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.041 | 0.193 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".