In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …

Importance The efficient and accurate interpretation of radiologic images is paramount.
Objective To evaluate whether a deep learning–based artificial intelligence (AI) engine used …

This statement from the European Society of Thoracic imaging (ESTI) explains and
summarises the essentials for understanding and implementing Artificial intelligence (AI) in …

Introduction The incidence and mortality of lung cancer are highest in Asia compared with
Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 …

Objectives To compare the performance of radiologists in characterizing and diagnosing
pulmonary nodules/masses with and without deep learning (DL)–based computer-aided …

Background Chest radiography remains the most common radiologic examination, and
interpretation of its results can be difficult. Purpose To explore the potential benefit of …

As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear
medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear …

Purpose Computer-aided diagnosis (CAD), which assists in the interpretation of chest
radiographs, is becoming common. However, few studies have evaluated the benefits and …

While many deep-learning-based computer-aided detection systems (CAD) have been
developed and commercialized for abnormality detection in chest radiographs (CXR), their …

Malignant pleural mesothelioma (MPM) is an aggressive primary malignancy of the pleura
that presents unique radiologic challenges with regard to accurate and reproducible …