Artificial Intelligence for Cancer Detection—A Bibliometric Analysis and Avenues for Future Research

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.

Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis

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Visualization of the relationship between fungi and cancer from the perspective of bibliometric analysis

“…The achievement of a thorough and comprehensive comprehension of the intricate mechanisms that govern the interaction between fungi and cancer is an essential prerequisite for developing innovative therapeutic approaches to combat this multifarious and enigmatic disease. In this regard, bibliometric analysis emerges as a quantitative method that meticulously examines scientific publications and citation patterns, furnishing valuable insights into the trajectory and impact of research within a particular domain [ 44 , [46] , [47] , [48] , [49] , [50] , [51] , [102] , [103] , [104] , [105] ]. Through the rigorous scrutiny of publication output and citation trends, researchers can effectively evaluate the current state of research pertaining to the interplay between fungi and cancer, identify significant gaps in knowledge, and discern domains that require further exploration and analysis.…”

The Role of Artificial Intelligence in the Diagnosis of Neoplastic Diseases: A Systematic and Bibliometric Review

“…According to Aitana Villaplana [94], one way to represent the proximity of words is to create a new vector with each word as coordinates [95], where the value of the coordinates is the product of the previous vectors [96]. In this way, if a word is repeated frequently in one text (let's say it's repeated 10 times) and not at all in the other (0), it will count as 10*0=0 times in the new vector.…”