Machine learning applications in stroke medicine: Advancements, challenges, and future prospectives

M Daidone, S Ferrantelli… - Neural Regeneration …, 2024 - journals.lww.com
Stroke is a leading cause of disability and mortality worldwide, necessitating the
development of advanced technologies to improve its diagnosis, treatment, and patient …

AI in orthodontics: Revolutionizing diagnostics and treatment planning—A comprehensive review

N Kazimierczak, W Kazimierczak, Z Serafin… - Journal of Clinical …, 2024 - mdpi.com
The advent of artificial intelligence (AI) in medicine has transformed various medical
specialties, including orthodontics. AI has shown promising results in enhancing the …

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

AA Armoundas, SM Narayan, DK Arnett… - Circulation, 2024 - Am Heart Assoc
A major focus of academia, industry, and global governmental agencies is to develop and
apply artificial intelligence and other advanced analytical tools to transform health care …

The integration of artificial intelligence in robotic surgery: A narrative review

C Zhang, MS Hallbeck, H Salehinejad, C Thiels - Surgery, 2024 - Elsevier
Background The rise of high-definition imaging and robotic surgery has independently been
associated with improved postoperative outcomes. However, steep learning curves and …

Smart cities: The role of Internet of Things and machine learning in realizing a data-centric smart environment

A Ullah, SM Anwar, J Li, L Nadeem, T Mahmood… - Complex & Intelligent …, 2024 - Springer
This paper explores the concept of smart cities and the role of the Internet of Things (IoT)
and machine learning (ML) in realizing a data-centric smart environment. Smart cities …

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

R Shinde, S Patil, K Kotecha, V Potdar… - Transactions on …, 2024 - Wiley Online Library
Healthcare institutions are progressively integrating artificial intelligence (AI) into their
operations. The extraordinary potential of AI is restricted by insufficient medical data for AI …

Deep learning for [18F] fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis

I Häggström, D Leithner, J Alvén… - The Lancet Digital …, 2024 - thelancet.com
Background The rising global cancer burden has led to an increasing demand for imaging
tests such as [18 F] fluorodeoxyglucose ([18 F] FDG)-PET-CT. To aid imaging specialists in …

Vision Transformer–based Decision Support for Neurosurgical Intervention in Acute Traumatic Brain Injury: Automated Surgical Intervention Support Tool

CW Smith, AK Malhotra, C Hammill… - Radiology: Artificial …, 2024 - pubs.rsna.org
Purpose To develop an automated triage tool to predict neurosurgical intervention for
patients with traumatic brain injury (TBI). Materials and Methods A provincial trauma registry …

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy

QD Buchlak, CHM Tang, JCY Seah, A Johnson… - European …, 2024 - Springer
Objectives Non-contrast computed tomography of the brain (NCCTB) is commonly used to
detect intracranial pathology but is subject to interpretation errors. Machine learning can …

A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection

AS Neethi, SK Kannath, AA Kumar, J Mathew… - … Applications of Artificial …, 2024 - Elsevier
Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …