[1Win4] Poster session 1

[1Win4-90] Trend analysis of article titles related to artificial intelligence and electronic records using the medical article database PubMed

〇Nami Kobayashi¹ (1.Shibuya Longevity Health Foundation)

Keywords:AI, Electronic Medical Record, Textmining

To investigate the impact of generative artificial intelligence (AI) on medical records, we analyzed the trend of article titles with hits using “artificial intelligence” and “electronic record” as search terms for articles published in PubMed, a highly reliable database of medical articles, for five years from 2020. Using the software “textmining UserLocal” for analysis of article titles published in every year and Japanese authors’ articles for five years, we compared the tables of word frequencies and co-occurrence network figures. Results were as follows: Terms such as “machine learning”, “natural language processing”, “electronic health record”, and “machine prediction” did not change over the five-year period, but terms such as “robot assist” disappeared in 2024 and instead “large language model” and “primary care” appeared in 2024. While the global impact of generative AI was suggested, robot-assisted surgery and machine learning were still the main topics in Japanese authors’ papers.