Redefining radiology: a review of artificial intelligence integration in medical imaging

R Najjar - Diagnostics, 2023 - mdpi.com
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making
its foray into radiology, a move that is catalysing transformational shifts in the healthcare …

Generative AI in medicine and healthcare: promises, opportunities and challenges

P Zhang, MN Kamel Boulos - Future Internet, 2023 - mdpi.com
Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI's
ChatGPT, that can be prompted to generate various types of content. In this narrative review …

Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis

SH Park, K Han, HY Jang, JE Park, JG Lee, DW Kim… - Radiology, 2023 - pubs.rsna.org
Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in
practice is critical. Clinical evaluation aims to confirm acceptable AI performance through …

Simplified transfer learning for chest radiography models using less data

AB Sellergren, C Chen, Z Nabulsi, Y Li, A Maschinot… - Radiology, 2022 - pubs.rsna.org
Background Developing deep learning models for radiology requires large data sets and
substantial computational resources. Data set size limitations can be further exacerbated by …

CT-based radiomics and deep learning for BRCA mutation and progression-free survival prediction in ovarian cancer using a multicentric dataset

G Avesani, HE Tran, G Cammarata, F Botta… - Cancers, 2022 - mdpi.com
Simple Summary Ovarian cancer has a heterogeneous response to treatment, and relapse
may vary considerably. Different studies investigated the role of radiomics in ovarian cancer …

Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology

KF Hung, QYH Ai, YY Leung, AWK Yeung - Clinical Oral Investigations, 2022 - Springer
Objectives Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial
radiology (DMFR) are continuously being developed and improved using advanced …

Application of artificial intelligence in orthodontics: current state and future perspectives

J Liu, C Zhang, Z Shan - Healthcare, 2023 - mdpi.com
In recent years, there has been the notable emergency of artificial intelligence (AI) as a
transformative force in multiple domains, including orthodontics. This review aims to provide …

The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review

D Schwabe, K Becker, M Seyferth, A Klaß… - npj Digital …, 2024 - nature.com
The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …

Strategies for implementing machine learning algorithms in the clinical practice of radiology

A Chae, MS Yao, H Sagreiya, AD Goldberg… - Radiology, 2024 - pubs.rsna.org
Despite recent advancements in machine learning (ML) applications in health care, there
have been few benefits and improvements to clinical medicine in the hospital setting. To …

An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning

J Zhang, Y Liu, Y Hua, J Cao - Proceedings of the IEEE/CVF …, 2024 - openaccess.thecvf.com
Abstract Heterogeneous Federated Learning (HtFL) enables collaborative learning on
multiple clients with different model architectures while preserving privacy. Despite recent …