Implementation of artificial intelligence in thoracic imaging—a what, how, and why guide from the European Society of Thoracic Imaging (ESTI)

Journal article

This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its’ effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. Key Points: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital

Authors: Gleeson, F; Revel, M-P; Biederer, J; Larici, AR; Martini, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: European Radiology
• Volume: 33
• Issue: 7
• Pages: 5077-5086
• Publication date: 2023-02-02
• DOI: 10.1007/s00330-023-09409-2
• EISSN: 1432-1084
• ISSN: 0938-7994
• Language: English
• Keywords: Quality assurance; Pathology; Radiology; Medical physics; Neuroradiology; Neurology; Medicine; Interventional radiology