Pathology Reporting Evolution: Digital, AI, Standards

Juan Carlos Mendez^*
*Correspondence: Juan Carlos Mendez, Department of Genitourinary Diagnostic Pathology, Universidad Médica de Nueva Castilla, Valverde, Spain, Email:

Introduction

This paper offers a comprehensive guide for integrating digital pathology into routine diagnostic workflows, emphasizing the practical aspects and challenges involved. It discusses infrastructure requirements, workflow changes, and quality assurance, highlighting the benefits for reporting efficiency and accessibility within a modern pathology department [1].

This review explores the evolving role of artificial intelligence in digital pathology, detailing its applications in image analysis, disease diagnosis, and report generation. It covers various AI techniques and discusses both the potential benefits for efficiency and accuracy in reporting, as well as the current limitations and future challenges [2].

This systematic review investigates how structured reporting influences the accuracy and completeness of pathology diagnoses. It synthesizes evidence demonstrating that standardized templates can reduce reporting errors, ensure inclusion of critical data elements, and ultimately improve patient care by providing clearer, more consistent information to clinicians [3].

This article reviews the evolution and benefits of synoptic reporting in cancer pathology, emphasizing its role in standardizing critical data elements for prognostic and therapeutic decision-making. It discusses how these structured reports improve data quality, facilitate cancer staging, and enhance communication between pathologists and oncologists [4].

This review delves into the obstacles and advantages of adopting digital pathology for primary diagnoses, focusing on how these factors impact reporting practices. It highlights technical infrastructure needs, pathologist training, regulatory hurdles, and the potential for improved efficiency and remote access in pathology reporting [5].

This guideline from ASCO and CAP provides recommendations for molecular pathology reporting, specifically for targeted therapies in cancer. It outlines essential data elements, standardized terminology, and the need for clear interpretation of genomic findings to guide clinical decision-making and ensure comprehensive and accurate reports for oncologists [6].

This review explores the potential of blockchain technology in pathology, including its implications for data security, interoperability, and integrity in reporting. It discusses how blockchain could enhance traceability of pathology data, secure patient information, and facilitate more trustworthy and auditable diagnostic reports, improving overall trust in the reporting ecosystem [7].

This article provides an overview of quality assurance measures in anatomic pathology, emphasizing their direct impact on the reliability and accuracy of pathology reporting. It covers various strategies like inter-observer variability checks, error tracking, and guideline adherence, all critical for maintaining high standards in diagnostic reporting and patient safety [8].

This paper examines the current state and hurdles of telepathology, particularly in geographically isolated regions, and its influence on pathology reporting. It details issues like connectivity, image quality, and regulatory frameworks, while also highlighting telepathology's potential to extend expert diagnostic reporting services to underserved areas, improving access to care [9].

This qualitative study investigates the factors hindering and promoting the adoption of standardized reporting practices in diagnostic pathology. It reveals insights into pathologist resistance, IT system compatibility, and training needs, underscoring the importance of clear guidelines and clinician buy-in to improve the consistency and utility of pathology reports [10].

Description

This paper offers a comprehensive guide for integrating digital pathology into routine diagnostic workflows, emphasizing the practical aspects and challenges involved. It discusses infrastructure requirements, workflow changes, and quality assurance, highlighting the benefits for reporting efficiency and accessibility within a modern pathology department [1]. This review further delves into the obstacles and advantages of adopting digital pathology for primary diagnoses, specifically focusing on how these factors impact reporting practices, highlighting technical infrastructure needs, pathologist training, regulatory hurdles, and the potential for improved efficiency and remote access in pathology reporting [5].

This review explores the evolving role of Artificial Intelligence (AI) in digital pathology, detailing its applications in image analysis, disease diagnosis, and report generation. It covers various AI techniques and discusses both the potential benefits for efficiency and accuracy in reporting, as well as the current limitations and future challenges [2]. Here's the thing, blockchain technology also presents a compelling avenue for pathology, exploring its implications for data security, interoperability, and integrity in reporting. What this really means is blockchain could enhance traceability of pathology data, secure patient information, and facilitate more trustworthy and auditable diagnostic reports, ultimately improving overall trust in the reporting ecosystem [7].

This systematic review investigates how structured reporting influences the accuracy and completeness of pathology diagnoses. It synthesizes evidence demonstrating that standardized templates can reduce reporting errors, ensure inclusion of critical data elements, and ultimately improve patient care by providing clearer, more consistent information to clinicians [3]. This concept extends to synoptic reporting in cancer pathology, where its evolution and benefits are reviewed, emphasizing its role in standardizing critical data elements for prognostic and therapeutic decision-making. These structured reports improve data quality, facilitate cancer staging, and enhance communication between pathologists and oncologists [4]. Implementing standardized reporting in diagnostic pathology, however, faces factors hindering and promoting adoption, as revealed by a qualitative study. It surfaces insights into pathologist resistance, IT system compatibility, and training needs, underscoring the importance of clear guidelines and clinician buy-in to improve the consistency and utility of pathology reports [10].

This guideline from ASCO and CAP provides recommendations for molecular pathology reporting, specifically for targeted therapies in cancer. It outlines essential data elements, standardized terminology, and the need for clear interpretation of genomic findings to guide clinical decision-making and ensure comprehensive and accurate reports for oncologists [6]. What this really means is quality assurance measures in anatomic pathology are paramount, directly impacting the reliability and accuracy of reporting. This covers various strategies like inter-observer variability checks, error tracking, and guideline adherence, all critical for maintaining high standards in diagnostic reporting and patient safety [8]. On another note, telepathology addresses the current state and hurdles, particularly in geographically isolated regions, and its influence on pathology reporting. It details issues like connectivity, image quality, and regulatory frameworks, while highlighting its potential to extend expert diagnostic reporting services to underserved areas, improving access to care [9].

Conclusion

The modern landscape of pathology reporting is undergoing significant transformation, with a strong focus on enhancing efficiency, accuracy, and accessibility. Digital pathology is central to this evolution, offering a comprehensive guide for integration into routine diagnostic workflows, addressing infrastructure, workflow shifts, and quality assurance for better reporting efficiency. Challenges and opportunities exist in adopting digital pathology for primary diagnoses, encompassing technical needs, pathologist training, and regulatory hurdles, alongside the promise of improved remote access. Artificial Intelligence (AI) plays an evolving role, detailing applications in image analysis and diagnosis, highlighting both its benefits for accuracy and current limitations. Complementing these technological shifts, structured and synoptic reporting are critical for improving diagnostic accuracy and completeness. Standardized templates reduce errors, ensure crucial data inclusion, and facilitate cancer staging and communication, particularly in cancer pathology where it standardizes critical elements for prognostic decisions. Quality assurance measures are vital in anatomic pathology to ensure report reliability, incorporating strategies like inter-observer checks and guideline adherence. Molecular pathology reporting also has specific guidelines for targeted therapies in cancer, emphasizing standardized terminology and clear interpretation of genomic findings. Telepathology extends expert diagnostic services to remote areas, though it faces hurdles like connectivity and image quality. The adoption of standardized practices faces barriers like pathologist resistance and IT compatibility, underscoring the need for clear guidelines. Emerging technologies like blockchain are also explored for enhancing data security and integrity in reporting, improving overall trustworthiness.

Acknowledgement

None

Conflict of Interest

None

References

Hina MH, Deepak D, Lea DW. "Digital pathology: a practical guide to implementation".Histopathology 81 (2022):593-608.

Indexed at, Google Scholar, Crossref

Daisuke K, Masao H, Hirokazu N. "Artificial intelligence in digital pathology: a survey of the current landscape".Mod Pathol 34 (2021):1-19.

Indexed at, Google Scholar, Crossref

Patrizia P, Valentina V, Manuela G. "Impact of structured reporting on diagnostic accuracy and completeness in pathology: a systematic review".Virchows Arch 477 (2020):3-15.

Indexed at, Google Scholar, Crossref

Alexander AM, Jonathan D, Ian AC. "Synoptic Reporting in Cancer Pathology: Current State and Future Directions".Arch Pathol Lab Med 143 (2019):805-812.

Indexed at, Google Scholar, Crossref

Mohammed NA, Jamil M, Ritesh S. "Challenges and opportunities in implementing digital pathology for primary diagnosis: a review".J Clin Pathol 74 (2021):440-449.

Indexed at, Google Scholar, Crossref

Rina N, Nagla AH, Christian P. "Molecular pathology reporting for targeted therapy: an ASCO/CAP guideline".J Clin Oncol 39 (2021):3144-3168.

Indexed at, Google Scholar, Crossref

Mohammed A, Abdullah S, B. R. "Blockchain technology in pathology: A comprehensive review of its applications and future potential".J Pathol Inform 14 (2023):100236.

Indexed at, Google Scholar, Crossref

Michael BC, Panagiotis P, Saul S. "Quality assurance in anatomic pathology: an overview of current practices and future directions".Semin Diagn Pathol 39 (2022):342-351.

Indexed at, Google Scholar, Crossref

Martha M, Yee FL, Raina H. "Current status and challenges of telepathology in remote areas".J Clin Pathol 73 (2020):280-285.

Indexed at, Google Scholar, Crossref

Sarah E, David WS, Andrew MD. "Implementing standardized reporting in diagnostic pathology: a qualitative study of barriers and facilitators".J Clin Pathol 76 (2023):41-47.

Indexed at, Google Scholar, Crossref