Trends in cardiology and oncology artificial intelligence publications

• Published in: American heart journal plus 2022-05, Vol.17, p.100162-100162, Article 100162
• Main Authors: Suero-Abreu, Giselle A, Hamid, Abdulaziz, Akbilgic, Oguz, Brown, Sherry-Ann
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Bibliometric analysis; Deep learning; Machine learning; Natural language processing
• ISSN: 2666-6022; 2666-6022
• DOI: 10.1016/j.ahjo.2022.100162

To determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields. To evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed. Findings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on "machine learning" (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to "artificial intelligence" (n = 4535), "natural language processing" (n = 2608), and "deep learning" (n = 4459). Trends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.