Internal Medicine Year in Review 2023.

Artificial Intelligence COVID-19 Internal Medicine Machine Learning Scholarly Information Year in Review

Journal

Internal medicine (Tokyo, Japan)
ISSN: 1349-7235
Titre abrégé: Intern Med
Pays: Japan
ID NLM: 9204241

Informations de publication

Date de publication:
04 Sep 2024
Historique:
medline: 5 9 2024
pubmed: 5 9 2024
entrez: 4 9 2024
Statut: aheadofprint

Résumé

The year 2023 marked a significant change for Internal Medicine, as the number of submissions related to the novel coronavirus infection (COVID-19) declined significantly and interest shifted to other disease fields and research areas. Our journal published its first articles on artificial intelligence (AI) and machine learning (ML), and these articles have shown that AI may be useful for the early detection of potential cardiac diseases, while ML can be used to predict the risk of serious illness in patients hospitalized with COVID-19, providing new possibilities for diagnoses and treatment. In addition to touching on the above, the present article also highlights the status of submissions to the journal (including the number of submissions and acceptance rate) in 2023.

Identifiants

pubmed: 39231657
doi: 10.2169/internalmedicine.4396-24
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Naruaki Ogasawara (N)

Editorial Department, The Japanese Society of Internal Medicine (JSIM), Japan.

Kazuto Matsunaga (K)

Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Japan.

Hajime Isomoto (H)

Department of Multidisciplinary Internal Medicine, Tottori University, Japan.

Wataru Shimizu (W)

Department of Cardiovascular Medicine, Nippon Medical School, Japan.

Classifications MeSH