Panagiotis Korfiatis PhD , Timothy L. Kline PhD , Holly M. Meyer MS , Sana Khalid MS , Timothy Leiner MD , Brenna T. Loufek MS , Daniel Blezek PhD , David E. Vidal JD , Robert P. Hartman MD , Lori J. Joppa MBA , Andrew D. Missert PhD , Theodora A. Potretzke MD , Jerome P. Taubel , Jason A. Tjelta BS , Matthew R. Callstrom MD , Eric E. Williamson MD
{"title":"Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations","authors":"Panagiotis Korfiatis PhD , Timothy L. Kline PhD , Holly M. Meyer MS , Sana Khalid MS , Timothy Leiner MD , Brenna T. Loufek MS , Daniel Blezek PhD , David E. Vidal JD , Robert P. Hartman MD , Lori J. Joppa MBA , Andrew D. Missert PhD , Theodora A. Potretzke MD , Jerome P. Taubel , Jason A. Tjelta BS , Matthew R. Callstrom MD , Eric E. Williamson MD","doi":"10.1016/j.mcpdig.2024.100188","DOIUrl":null,"url":null,"abstract":"<div><div>Integration of AI-enabled algorithms into the radiology workflow presents a complex array of challenges that span operational, technical, clinical, and regulatory domains. Successfully overcoming these hurdles requires a multifaceted approach, including strategic planning, educational initiatives, and careful consideration of the practical implications for radiologists' workloads. Institutions must navigate these challenges with a clear understanding of the potential benefits and limitations of both vended and in-house developed AI tools.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100188"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mayo Clinic Proceedings. Digital health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949761224001214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Integration of AI-enabled algorithms into the radiology workflow presents a complex array of challenges that span operational, technical, clinical, and regulatory domains. Successfully overcoming these hurdles requires a multifaceted approach, including strategic planning, educational initiatives, and careful consideration of the practical implications for radiologists' workloads. Institutions must navigate these challenges with a clear understanding of the potential benefits and limitations of both vended and in-house developed AI tools.
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