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Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice.
Zhang, David Y; Venkat, Arsha; Khasawneh, Hamdi; Sali, Rasoul; Zhang, Valerio; Pei, Zhiheng.
Afiliación
  • Zhang DY; Department of Computation, NovinoAI, Fort Lauderdale, Florida; Department of Veterans Affairs New York Harbor Healthcare System, New York, New York. Electronic address: dyzhang01@gmail.com.
  • Venkat A; School of Medicine, New York Medical College, New York, New York.
  • Khasawneh H; King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan.
  • Sali R; Department of Computation, NovinoAI, Fort Lauderdale, Florida; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Zhang V; Department of Computation, NovinoAI, Fort Lauderdale, Florida.
  • Pei Z; Department of Veterans Affairs New York Harbor Healthcare System, New York, New York; Department of Pathology, New York University School of Medicine, New York, New York; Department of Medicine, New York University School of Medicine, New York, New York. Electronic address: Zhiheng.Pei@nyulangone.or
Lab Invest ; 104(9): 102111, 2024 09.
Article en En | MEDLINE | ID: mdl-39053633
ABSTRACT
The advent of affordable technology has significantly influenced the practice of digital pathology, leading to its growing adoption within the pathology community. This review article aimed to outline the latest developments in digital pathology, the cutting-edge advancements in artificial intelligence (AI) applications within this field, and the pertinent United States regulatory frameworks. The content is based on a thorough analysis of original research articles and official United States Federal guidelines. Findings from our review indicate that several Food and Drug Administration-approved digital scanners and image management systems are establishing a solid foundation for the seamless integration of advanced technologies into everyday pathology workflows, which may reduce device and operational costs in the future. AI is particularly transforming the way morphologic diagnoses are automated, notably in cancers like prostate and colorectal, within screening initiatives, albeit challenges such as data privacy issues and algorithmic biases remain. The regulatory environment, shaped by standards from the Food and Drug Administration, Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments, and College of American Pathologists, is evolving to accommodate these innovations while ensuring safety and reliability. Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments have issued policies to allow pathologists to review and render diagnoses using digital pathology remotely. Moreover, the introduction of new digital pathology Current Procedural Terminology codes designed to complement existing pathology Current Procedural Terminology codes is facilitating reimbursement processes. Overall, these advancements are heralding a new era in pathology that promises enhanced diagnostic precision and efficiency through digital and AI technologies, potentially improving patient care as well as bolstering educational and research activities.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Patología / Inteligencia Artificial / Tecnología Digital Límite: Humans Idioma: En Revista: Lab Invest Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Patología / Inteligencia Artificial / Tecnología Digital Límite: Humans Idioma: En Revista: Lab Invest Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos