Bibliometric analysis of the global scientific production on machine learning applied to different cancer types Ruiz Fresneda, Miguel Ángel Gijón Gijón, Alfonso Morales Álvarez, Pablo Machine learning Cancer Bibliometric analysis Artificial intelligence Public health This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant agreement no. 860627 (CLARIFY Project), from the Spanish Ministry of Science and Innovation under project PID2019-105142RB-C22, and by FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades under the project P20_00286. Funding for open access charge: Universidad de Granada/CBUA. Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments. 2023-09-27T07:55:14Z 2023-09-27T07:55:14Z 2023-08-11 journal article Ruiz-Fresneda, M.A., Gijón, A. & Morales-Álvarez, P. Bibliometric analysis of the global scientific production on machine learning applied to different cancer types. Environ Sci Pollut Res 30, 96125–96137 (2023). [https://doi.org/10.1007/s11356-023-28576-9] https://hdl.handle.net/10481/84681 10.1007/s11356-023-28576-9 eng info:eu-repo/grantAgreement/EC/H2020/860627 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer Nature