  Colorectal cancer (CRC) is among the most prevalent cancers in the world. Due
to numerous scholarly papers and broad enquiries about specific use cases for
artificial intelligence (AI) in colorectal cancer, researchers find it
challenging to explore relevant papers on the current knowledge, comprehensive
knowledge, and past methodologies in the literature review. This review
extracts recent AI technology advances for diagnosing colorectal cancer from
January 2010 to March 2022. PubTrends was used to identify and automate the
intellectual structure and comparable papers on the use of AI in colorectal
cancer diagnosis using the most cited papers, keywords, and similar papers.
Papers with quantitative results were represented with a tabular summary, and
other paper contributions were in a sentence summary. Twenty-four (24) out of
the forty-nine (49) top-cited papers were quantitative results, with one (1)
outlier about lung cancer comprehensive screening. The most frequently used
words were: "polyps," "detected", "image," and "colonoscopy." In addition, 83
per cent of the terms frequently used shortly before 2022 were image, polyps,
detected, colonoscopy, and learning. In addition, 16 per cent are preparation,
variant, classification, sample, and surgery. The review showcases 49 of the 50
most cited papers, their notable contributions, objectives, specific AI
methods, results, conclusions, and further recommendations. These papers
highlight the limitations of colonoscopy for therapeutic use. The review
concluded that despite the enormous benefits of using artificial intelligence,
from improving diagnosis, the medical AI programmer still needs to be actively
involved in the diagnosis team for effective results in CRC diagnosis.
