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

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.

A multimodal generative AI copilot for human pathology.

Computational pathology1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders3,4. However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots5 tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref. 6). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making.

[The environmental impact of digital technology and artificial intelligence, in the era of digital pathology]. / Impacts environnementaux du numérique et de l'intelligence artificielle, à l'heure de la pathologie digitale.

While digitization and artificial intelligence represent the future of our specialty, future is also constrained by global warming and overstepping of planetary limits, threatening human health and the functioning of the healthcare system. The report by the Délégation ministérielle du numérique en santé and the French government's ecological planning of the healthcare system confirm the need to control the environmental impact of digital technology. Indeed, despite the promises of dematerialization, digital technology is a very material industry, generating greenhouse gas emissions, problematic consumption of water and mineral resources, and social impacts. The digital sector is impacting at every stage: (i) manufacture of equipment; (ii) use; and (iii) end-of-life of equipment, which, when recycled, can only be recycled to a very limited extent. This is a fast-growing sector, and the digitization of our specialty is part of its acceleration and its impact. Understanding the consequences of digitalization and artificial intelligence, and phenomena such as the rebound effect, is an essential prerequisite for the implementation of a sober, responsible, and sustainable digital pathology. The aim of this update is to help pathologists better understand the environmental impact of digital technology. As healthcare professionals, we have a responsibility to combine technological advances with an awareness of their impact, within a systemic vision of human health.

The digital revolution in pathology: Towards a smarter approach to research and treatment.

Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

[Introduction to renal pathology]. / Einführung in die Nierenpathologie.

In recent decades, nephropathology has developed worldwide as a subspeciality of pathology, which requires special methodological and technical equipment to process the material and specific clinical and pathological expertise to interpret the findings. These special requirements mean that nephropathology is not available at all pathology institutes, but is carried out on a large scale in a few highly specialised centres. The history of nephropathology, or in a narrower sense the specialised histopathological examination of kidney biopsies, began in 1958 with the first use or performance of a kidney biopsy [1]. It thus replaced the practice of urinalysis, which had been common since the Middle Ages, as a diagnostic tool for kidney diseases. Specialised techniques such as immunofluorescence or immunohistology but also electron microscopy are required to assess specific renal changes, for which the examination of renal biopsies is one of the few remaining routine applications today. In Germany and German-speaking countries, the discipline developed thanks to the work of outstanding people in the field of pathology who were primarily involved in this discipline and had the necessary technical and human resources in their laboratories to ensure that these biopsies could be analysed.

[Artificial intelligence for pathology-how, where, and why?] / Künstliche Intelligenz in der Pathologie ­ wie, wo und warum?

Artificial intelligence promises many innovations and simplifications in pathology, but also raises just as many questions and uncertainties. In this article, we provide a brief overview of the current status, the goals already achieved by existing algorithms, and the remaining challenges.

Trends in authorship in the Journal of Cutaneous Pathology, 1981-2020.

BACKGROUND: The increase in authors per scientific article in many different medical and scientific disciplines has raised concerns over ethical authorship. Trends in authorship in dermatopathology are unknown. METHODS: Cross-sectional study of a random sample of 200 articles from the Journal of Cutaneous Pathology (1981-2020). RESULTS: The number of authors per article increased by an estimated 96% between 1981 and 2020 (2.7-5.3), while the relative citation ratio decreased by an estimated 56% during the same period (1.19-0.52). Higher author counts were not associated with higher relative citation ratios (p = 0.2349) or analytic study designs (p = 0.2987). Higher relative citation ratios were associated with analytic study designs (p = 0.0374). CONCLUSIONS: There has been significant growth in authorship credit at the journal without a corresponding increase in research impact or study rigor. Remedial measures to stem authorship inflation and promote more impactful studies may be necessary.

Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models.

Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.

Whole Slide Images in Artificial Intelligence Applications in Digital Pathology: Challenges and Pitfalls.

The use of digitized data in pathology research is rapidly increasing. The whole slide image (WSI) is an indispensable part of the visual examination of slides in digital pathology and artificial intelligence applications; therefore, the acquisition of WSI with the highest quality is essential. Unlike the conventional routine of pathology, the digital conversion of tissue slides and the differences in its use pose difficulties for pathologists. We categorized these challenges into three groups: before, during, and after the WSI acquisition. The problems before WSI acquisition are usually related to the quality of the glass slide and reflect all existing problems in the analytical process in pathology laboratories. WSI acquisition problems are dependent on the device used to produce the final image file. They may be related to the parts of the device that create an optical image or the hardware and software that enable digitization. Post-WSI acquisition issues are related to the final image file itself, which is the final form of this data, or the software and hardware that will use this file. Because of the digital nature of the data, most of the difficulties are related to the capabilities of the hardware or software. Being aware of the challenges and pitfalls of using digital pathology and AI will make pathologists' integration to the new technologies easier in their daily practice or research.

Digital pathology and artificial intelligence in translational medicine and clinical practice.

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Trends in cytopathology fellowship positions and vacancies over the past decade.

INTRODUCTION: Cytopathology is one of the most sought-after fellowships within pathology, with a lower fellowship vacancy rate compared with most other subspecialties. The Accreditation Council for Graduate Medical Education (ACGME) actively tracks annual program data for cytopathology fellowship programs, and evaluating this longitudinal data looking at trends in programs and positions over the past 10 years could provide insights into the future of cytopathology and its training programs. METHODS: Data obtained from the ACGME was examined in detail for all ACGME-accredited cytopathology fellowship programs over the past decade (2011-2021). Additional responses from program directors (PDs) from a 2021 American Society of Cytopathology (ASC) survey are also included. RESULTS: The total number of ACGME-approved cytopathology training programs and cytopathology fellowship positions remained relatively constant over the past 10 years, but the vacancy rate and number of programs with 1-2 unfilled spots has gradually but steadily risen over the past 6 years. In a 2021 ASC PD survey with 66% response rate, 53% of PDs reported having recruitment problems at least occasionally and 46% reported an increase in unexpected fellowship openings. CONCLUSIONS: Although the number of cytopathology positions has been relatively constant over the past decade, there has been a recent increase in cytopathology fellowship vacancies that may indicate changes in career choices or the job market, with fellows choosing jobs over additional fellowships, and potentially signal a growing shortage of fellowship-trained, Board-certified cytopathologists in the coming years.

Flattening the World of Pathology Education and Training and Shortening the Curve of Pathology Learning.

OBJECTIVES: We review how the pandemic-related education disruption may interplay with pathology manpower worldwide and shifts in disease burden to identify workable solutions. METHODS: Literature related to pathology education, pathology services in low-resource settings, and application of digital tools to pathology education was reviewed for trends and training gaps. Publications covering pathology manpower and cancer incidence worldwide were also included to assess needs. RESULTS: Pandemic-related virtual teaching has produced abundant online training materials. Pathology learning resources in low- to middle-income countries remain considerably constrained and dampen pathology manpower growth to meet current needs. Projected increases in disease burden toward the developing world thus pose a major challenge. Digital pathology resources have expanded and are beginning to appear beyond the developed countries. CONCLUSIONS: This circumstance offers a unique opportunity to leverage digital teaching resources to enhance and equitize training internationally, potentially sufficient to meet the rising wave of noncommunicable diseases. We propose four next steps to take advantage of the current opportunity: curate and organize digital training materials, invest in the digital pathology infrastructure for education and clinical care, expand student exposure to pathology through virtual electives, and develop further competency-based certification pathways.

Next-Generation Digital Histopathology of the Tumor Microenvironment.

Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology-which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.