introduction-an-integrated-pathology-image-management-artificial

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

The development of electronic reporting systems (LIS), image management systems (IMS), and AI models has revolutionized pathology practices. By ...

NovinoAI

New York Harbor Healthcare System

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.

Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice.

Histopathology, which is the gold-standard for prostate cancer diagnosis, faces significant challenges. With prostate cancer ranking among the most common cancers in the United States and worldwide, pathologists experience an increased number for prostate biopsies. At the same time, precise pathological assessment and classification are necessary for risk stratification and treatment decisions in prostate cancer care, adding to the challenge to pathologists. Recent advancement in digital pathology makes artificial intelligence and learning tools adopted in histopathology feasible. In this review, we introduce the concept of AI and its various techniques in the field of histopathology. We summarize the clinical applications of AI pathology for prostate cancer, including pathological diagnosis, grading, prognosis evaluation, and treatment options. We also discuss how AI applications can be integrated into the routine pathology workflow. With these rapid advancements, it is evident that AI applications in prostate cancer go beyond the initial goal of being tools for diagnosis and grading. Instead, pathologists can provide additional information to improve long-term patient outcomes by assessing detailed histopathologic features at pixel level using digital pathology and AI. Our review not only provides a comprehensive summary of the existing research but also offers insights for future advancements.

Article	Year
Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice. Laboratory investigation; a journal of technical methods and pathology\| PubMed ID: 39053633	2024
Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer. American journal of clinical and experimental urology\| PubMed ID: 39308594	2024

David Y. Zhang

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