AI Implementation in Radiology

• Titel: AI Implementation in Radiology
• Titelzusatz: Challenges and Opportunities in Clinical Practice
• Mitwirkende: Ranschaert, Erik [HerausgeberIn]
• Mitwirkende: Rezazade Mehrizi, Mohammad H. [HerausgeberIn]
• Mitwirkende: Grootjans, Willem [HerausgeberIn]
• Mitwirkende: Cook, Tessa S. [HerausgeberIn]
• Verf.angabe: edited by Erik Ranschaert, Mohammad H. Rezazade Mehrizi, Willem Grootjans, Tessa S. Cook
• Ausgabe: 1st ed. 2024.
• Verlagsort: Cham
• Verlagsort: Cham
• Verlag: Springer Nature Switzerland
• Verlag: Imprint: Springer
• E-Jahr: 2024
• Jahr: 2024.
• Jahr: 2024.
• Umfang: 1 Online-Ressource(V, 183 p. 15 illus., 12 illus. in color.)
• Gesamttitel/Reihe: Imaging Informatics for Healthcare Professionals
• ISBN: 978-3-031-68942-0
• DOI: doi:10.1007/978-3-031-68942-0
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• K10plus-PPN: 1910712698
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4630440119
• Lokale URL Inst.: Zum Volltext

Abstract

1 Introduction -- 2 Identification of the Need for Change -- 3 Planning and Goal Setting -- 4 Stakeholder Engagement and communication -- 5 Exploring and Assessing AI Solutions -- 6 Legal and ethical aspects of AI in radiology -- 7 Workflow Integration and Training -- 8 Evaluation, Monitoring, improvement -- 9 The impact of AI on radiology reporting.

Abstract

This book describes change management in the context of implementing AI in medicine and radiology. Why do many medical institutions struggle to use AI in their clinical practice? What are the essential steps for and before an effective implementation of AI in radiology workflow? How can AI implementation trigger enduring improvements in the clinical process? The book shows how change management is crucial to effectively introduce AI to medicine and radiology, transform healthcare delivery and ensure a smooth transition while maximizing the benefits of AI and minimizing potential disruptions. Change management in the context of AI in medicine and radiology involves a systematic approach to identify, plan, implement, and evaluate the integration of AI technologies into healthcare systems. It engages the necessary stakeholders at the appropriate points in the process to ensure that change is implemented properly. By effectively managing the change, healthcare organizations can harness the potential of AI to enhance patient care, improve diagnosis accuracy, and optimize operational efficiency in radiology and other medical specialties. Throughout this change management process, organizations should prioritize ethical considerations, data privacy, and regulatory compliance to ensure that AI technologies are deployed responsibly and in accordance with relevant guidelines and regulations.