Strategizing AI in Healthcare: A Multidimensional Blueprint for Transformative Decision-Making in Clinical Settings

Abstract: The integration of Artificial Intelligence (AI) into healthcare represents a transformative shift, offering opportunities for enhancing patient care, diagnostic accuracy, process optimization and treatment pathways. This research sets out to forge a strategic management decision support framework for leveraging AI within the healthcare sector, aimed at systematically exploring and integrating AI innovations to bolster the patient health outcomes. By creating a comprehensive categorization system, we attempt to navigate the complex array of possible AI applications within the field of healthcare, hence enabling the identification, selection, and advancement of AIdriven initiatives. Through a blend of systematic literature review and expert insights, this study maps possible AI applications across dimensions like ‘medical disciplines’, ‘healthcare processes’, ‘AI research areas’, and ‘user groups’. By reflecting the diverse perspectives, this system transcends mere classification and stands as a cornerstone for identifying, selecting, and developing AI-driven medical use cases to guide strategic implementations of AI within clinical settings. This multidimensional system offers a blueprint for healthcare entities to strategically navigate the AI landscape, enabling them to make informed decisions about technology adoption and change management processes, ultimately leading to improved patient care and operational efficiency.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Strategizing AI in Healthcare: A Multidimensional Blueprint for Transformative Decision-Making in Clinical Settings ; volume:10 ; number:4 ; year:2024 ; pages:595-599 ; extent:5 ; Current directions in biomedical engineering ; 10, Heft 4 (2024), 595-599 (gesamt 5)

Contributors, Places and Time

• Creator: Simon, Martina ; Kamin, Stefan ; Hamper, Andreas ; Wittenberg, Thomas ; Schmitt-Rüth, Stephanie

Further information

• DOI: 10.1515/cdbme-2024-2146
• URN: urn:nbn:de:101:1-2412181747533.060118511475
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:22 AM CEST