The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals

M Salvagno, AD Cassai, S Zorzi, M Zaccarelli… - Plos one, 2024 - journals.plos.org
Natural Language Processing (NLP) is a subset of artificial intelligence that enables
machines to understand and respond to human language through Large Language Models …

Diagnostic evaluation of blunt chest trauma by imaging-based application of artificial intelligence: A review

T Zhao, X Meng, Z Wang, Y Hu, H Fan, J Han… - The American Journal of …, 2024 - Elsevier
Artificial intelligence (AI) is becoming increasingly integral in clinical practice, such as during
imaging tasks associated with the diagnosis and evaluation of blunt chest trauma (BCT) …

Deep learning for pneumothorax detection on chest radiograph: a diagnostic test accuracy systematic review and meta analysis

BD Katzman, M Alabousi, N Islam… - Canadian …, 2024 - journals.sagepub.com
Background: Pneumothorax is a common acute presentation in healthcare settings. A chest
radiograph (CXR) is often necessary to make the diagnosis, and minimizing the time …

Interobserver agreement and performance of concurrent AI assistance for radiographic evaluation of knee osteoarthritis

MW Brejnebøl, A Lenskjold, K Ziegeler, H Ruitenbeek… - Radiology, 2024 - pubs.rsna.org
Background Due to conflicting findings in the literature, there are concerns about a lack of
objectivity in grading knee osteoarthritis (KOA) on radiographs. Purpose To examine how …

Using AI to identify unremarkable chest radiographs for automatic reporting

LL Plesner, FC Müller, MW Brejnebøl, CH Krag… - Radiology, 2024 - pubs.rsna.org
Background Radiology practices have a high volume of unremarkable chest radiographs
and artificial intelligence (AI) could possibly improve workflow by providing an automatic …

Human-AI symbiosis: a path forward to improve chest radiography and the role of radiologists in patient care

WB Gefter, M Prokop, JB Seo, S Raoof, CP Langlotz… - Radiology, 2024 - pubs.rsna.org
To start, we need more rigorous testing of algorithms with prospective, pragmatic, real-world
clinical trials in diverse settings to assure robust generalizability, lack of biases, and a high …

Synthetically enhanced: unveiling synthetic data's potential in medical imaging research

B Khosravi, F Li, T Dapamede, P Rouzrokh… - …, 2024 - thelancet.com
Summary Background Chest X-rays (CXR) are essential for diagnosing a variety of
conditions, but when used on new populations, model generalizability issues limit their …

Added value of artificial intelligence solutions for arterial stenosis detection on head and neck CT angiography: A randomized crossover multi-reader multi-case study

K Li, Y Yang, Y Yang, Q Li, L Jiao, T Chen… - … and Interventional Imaging, 2025 - Elsevier
Purpose The purpose of this study was to investigate the added value of artificial intelligence
(AI) solutions for the detection of arterial stenosis (AS) on head and neck CT angiography …

From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven healthcare research for low-income …

F Krones, B Walker - medRxiv, 2023 - medrxiv.org
This paper critically explores the opportunities and challenges of deploying Artificial
Intelligence (AI) in healthcare. This study has two parallel components:(1) A narrative …

Is radiology's future without medical images?

TC Kwee, C Roest, D Yakar - European Journal of Radiology, 2024 - Elsevier
The visual evaluation of medical images by humans is the cornerstone of current radiology
practice. It enables radiologists and other physicians to diagnose diseases, may yield …