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Objective: Artificial intelligence (AI) is a turning point in medical advancement. Despite the burgeoning research in this field, there exists a general lack of overview of where AI is being most utilized. This study reviews and describes techniques and trends of AI in the major medical specialties.
Method: A literature search was conducted through PubMed in 2024 using two different search methods. Twenty-nine medical specialties were included, including all 24 major medical board specialties and five additional subspecialties.
Results: There were 143,578 publications involving AI identified with most these (87%) published in the last ten years (124,206) and 52% (74,239) in the last two years. Radiology and Pathology publications were the largest cohorts, 18% (25,319) and 17% (23,828), respectively. Plastic Surgery (1,053), Hepatobiliary (662), and Allergy/Immunology (449) were the least published. There has been a 10,859% growth rate in annual publications across all medical specialties, with Ophthalmology and Preventative Medicine being the fastest-growing areas of research despite Radiology and Pathology being the most researched to date.
Conclusion: This review underscores AI's profound impact on medical research, highlighting its significant growth and utilization across various specialties. AI's influence is most pronounced in Radiology and Pathology, but the substantial increase in publications in Ophthalmology and Preventative Medicine suggests new emerging areas of focus. The ongoing expansion of AI in medicine presents a promising horizon for addressing complex healthcare challenges, fostering a deeper and more comprehensive integration across all specialties.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409705 | PMC |
http://dx.doi.org/10.4293/JSLS.2025.00041 | DOI Listing |
PLoS One
September 2025
Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
Background: Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posing a major public health challenge. Numerous studies have regarded reduced muscle mass as a reliable indicator for identifying pre-sarcopenia.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202.
Retinal ganglion cells (RGCs) are highly compartmentalized neurons whose long axons serve as the sole connection between the eye and the brain. In both injury and disease, RGC degeneration occurs in a similarly compartmentalized manner, with distinct molecular and cellular responses in the axonal and somatodendritic regions. The goal of this study was to establish a microfluidic-based platform to investigate RGC compartmentalization in both health and disease states.
View Article and Find Full Text PDFJ Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.
J Neurooncol
September 2025
Division of Neurosurgery, Department of Brain and Neurosciences, Faculty of Medicine, Tottori University, Tottori, Japan.
Purpose: This study aimed to evaluate the prognostic significance of microvessel density (MVD), assessed by CD34 immunohistochemistry (IHC), and its correlation with radiological features and bevacizumab (BEV) treatment efficacy in newly diagnosed glioblastoma.
Methods: We retrospectively analyzed 41 patients with newly diagnosed glioblastoma. MVD was quantified using CD34 IHC, and patients were stratified into low and high MVD groups according to the cutoff value determined by receiver operating characteristic curve analysis (sensitivity, 76.
Endocrine
September 2025
Section of Endocrinology, Geriatrics and Internal Medicine, Department of Medical Sciences, University of Ferrara, Ferrara, Italy.