iForensic, multicentric validation of digital whole slide images (WSI) in forensic histopathology setting according to the College of American Pathologists guidelines.

Pathology has benefited from the rapid progress of image-digitizing technology during the last decade. However, the application of digital whole slide images (WSI) in forensic pathology still needs to be improved. WSI validation is crucial to ensure diagnostic performance, at least equivalent to glass slides and light microscopy.

Future of Artificial Intelligence (AI) - Machine Learning (ML) Trends in Pathology and Medicine.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of medicine. Healthcare organizations are now starting to establish management strategies for integrating such platforms (AI-ML toolsets) which leverage the computational power of advanced algorithms to analyze data and to provide better insights which ultimately translates to enhanced clinical decision-making and improved patient outcomes. Emerging AI-ML platforms and trends in pathology and medicine are reshaping the field by offering innovative solutions to enhance diagnostic accuracy, operational workflows, clinical decision support, and clinical outcomes.

Introduction to Artificial Intelligence (AI) and Machine Learning (ML) in Pathology & Medicine: Generative & Non-Generative AI Basics.

This manuscript serves as an introduction to a comprehensive seven-part review article series on artificial intelligence (AI) and machine learning (ML) and their current and future influence within pathology and medicine. This introductory review provides a comprehensive grasp of this fast-expanding realm and its potential to transform medical diagnosis, workflow, research, and education. Fundamental terminology employed in AI-ML is covered using an extensive dictionary.

Ethical and Bias Considerations in Artificial Intelligence (AI)/Machine Learning.

As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications and potential biases within such integrated AI models will require careful scrutiny. Ethics and bias are important considerations in our practice settings, especially as increased number of machine learning (ML) systems are being integrated within our various medical domains. Such machine learning based systems, have demonstrated remarkable capabilities in specified tasks such as but not limited to image recognition, natural language processing, and predictive analytics.

Generative Artificial Intelligence in Pathology and Medicine: A Deeper Dive.

This review article builds upon the introductory piece in our 7-part series, delving deeper into the transformative potential of generative artificial intelligence (Gen AI) in pathology and medicine. The article explores the applications of Gen AI models in pathology and medicine, including the use of custom chatbots for diagnostic report generation, synthetic image synthesis for training new models, data set augmentation, hypothetical scenario generation for educational purposes, and the use of multimodal along with multiagent models. This article also provides an overview of the common categories within Gen AI models, discussing open-source and closed-source models, as well as specific examples of popular models such as GPT-4, Llama, Mistral, DALL-E, Stable Diffusion, and their associated frameworks (eg, transformers, generative adversarial networks, diffusion-based neural networks), along with their limitations and challenges, especially within the medical domain.

Nongenerative Artificial Intelligence in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning.

The use of artificial intelligence (AI) within pathology and health care has advanced extensively. We have accordingly witnessed an increased adoption of various AI tools that are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within health care thus far fall mostly under the nongenerative AI domain, which includes supervised and unsupervised machine learning (ML) techniques.

Statistics of Generative Artificial Intelligence and Nongenerative Predictive Analytics Machine Learning in Medicine.

The rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) in medicine has prompted medical professionals to increasingly familiarize themselves with related topics. This also demands grasping the underlying statistical principles that govern their design, validation, and reproducibility. Uniquely, the practice of pathology and medicine produces vast amount of data that can be exploited by AI/ML.

CD8 Tumor-Infiltrating Lymphocytes in Head and Neck Cancer: A Review.

CD8 tumor-infiltrating lymphocytes (TILs) are increasingly used in oncology as a prognostic and predictive tool to guide patient management. This review summarizes current literature on CD8 TILs in head and neck squamous cell carcinoma (SCC). Published meta-analyses and clinical trials evaluating CD8 TILs were analyzed.

Fine-needle aspiration and effusion cytology of thoracic SMARCA4-deficient undifferentiated tumor and SMARCA4-deficient non-small cell lung carcinoma: A multi-institutional experience with 27 patients.

Background: Thoracic switch/sucrose nonfermentable-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4 (SMARCA4)-deficient (SD) malignancies, including SD undifferentiated tumor (SD-UT) and SD non-small cell lung carcinoma (SD-NSCLC), have been recently described. The cytologic features of these neoplasms in fine-needle aspiration (FNA) and effusion specimens have rarely been reported in the literature. This study aimed to describe and compare the spectrum of cytologic, immunohistochemical, and clinical features of these high-grade malignancies recently encountered at the participating institutions.

Analysis of the sensitivity of high-grade squamous intraepithelial lesion Pap diagnosis and interobserver variability with the Hologic Genius Digital Diagnostics System.

Background: Artificial intelligence (AI)-based systems are transforming cytopathology practice. The aim of this study was to evaluate the sensitivity of high-grade squamous intraepithelial lesion (HSIL) Papanicolaou (Pap) diagnosis assisted by the Hologic Genius Digital Diagnostics System (GDDS).

Methods: A validation study was performed with 890 ThinPrep Pap tests with the GDDS independently.

Aberrant CD45 Immunoreactivity in Neuroendocrine Neoplasms: A Diagnostic Pitfall-Report of 10 Specimens and Clinical Recommendations.

Leukocyte common antigen (LCA), or CD45, is classically thought of as a leukocyte-exclusive protein, and as such, CD45 immunohistochemistry (IHC) is often used as a key differentiator between non-Hodgkin lymphomas (NHLs) and morphologically similar neuroendocrine neoplasms (NENs). Herein, we report our experience regarding aberrant CD45 immunoreactivity in a series of NENs. A natural language search was used to retrieve desired archival patient files.

Telecytology versus conventional rapid on-site evaluation for endobronchial ultrasound-guided fine needle aspiration: a single institution's experience.

Introduction: Telecytology (TC) has the advantage of allowing cytopathologists to remotely support multiple sites rapid on-site evaluation (ROSE) concurrently and represents a potential solution for an increased clinical demand for ROSE. In this study, we share our comparative experience of using TC versus conventional (in-person) ROSE for endobronchial ultrasound-guided fine needle aspiration (EBUS-FNA).

Materials And Methods: We evaluated 475 consecutive cases of EBUS-FNA that underwent TC-ROSE from May 2020 to August 2021 along with 475 consecutive cases which had conventional ROSE from November 2019 to August 2021 at the University of Michigan.

Globalization of a telepathology network with artificial intelligence applications in Colombia: The GLORIA program study protocol.

In Colombia, cancer is recognized as a high-cost pathology by the national government and the Colombian High-Cost Disease Fund. As of 2020, the situation is most critical for adult cancer patients, particularly those under public healthcare and residing in remote regions of the country. The highest lag time for a diagnosis was observed for cervical cancer (79.

Regulatory Aspects of Artificial Intelligence and Machine Learning.

In the realm of health care, numerous generative and nongenerative artificial intelligence and machine learning (AI-ML) tools have been developed and deployed. Simultaneously, manufacturers of medical devices are leveraging AI-ML. However, the adoption of AI in health care raises several concerns, including safety, security, ethical biases, accountability, trust, economic impact, and environmental effects.

Utilization of an artificial intelligence-enhanced, web-based application to review bile duct brushing cytologic specimens: A pilot study.

Background: The authors previously developed an artificial intelligence (AI) to assist cytologists in the evaluation of digital whole-slide images (WSIs) generated from bile duct brushing specimens. The aim of this trial was to assess the efficiency and accuracy of cytologists using a novel application with this AI tool.

Methods: Consecutive bile duct brushing WSIs from indeterminate strictures were obtained.

Are we ready to bridge classification systems? A comprehensive review of different reporting systems in thyroid cytology.

The evaluation of thyroid lesions is common in the daily practice of cytology. While the majority of thyroid nodules are benign, in recent decades, there has been increased detection of small and well-differentiated thyroid cancers. Combining ultrasound evaluation with fine-needle aspiration cytology (FNAC) is extremely useful in the management of thyroid nodules.

Synthetic Data and Its Utility in Pathology and Laboratory Medicine.

In our rapidly expanding landscape of artificial intelligence, synthetic data have become a topic of great promise and also some concern. This review aimed to provide pathologists and laboratory professionals with a primer on the role of synthetic data and how it may soon shape the landscape within our field. Using synthetic data presents many advantages but also introduces a milieu of new obstacles and limitations.

Cytologic features of differentiated high-grade thyroid carcinoma: A multi-institutional study of 40 cases.

Background: Differentiated high-grade thyroid carcinoma (DHGTC) is recently recognized by the World Health Organization (WHO) as a subgroup of thyroid carcinomas with high-grade features while retaining the architectural and/or cytologic features of well-differentiated follicular-cell-derived tumors. The cytomorphology of DHGTC is not well documented despite potential implications for patient triage and management.

Methods: The pathology archives of six institutions were searched for cases diagnosed on resection as "high-grade thyroid carcinoma" using WHO criteria.

Second opinion for pulmonary and pleural cytology is valuable for patient care.

Introduction: Thoracic cytology can be challenging due to limited procured material or overlapping morphology between benign and malignant entities. In such cases, expert consultation might be sought. This study aimed to characterize all pulmonary and pleural cytology consult cases submitted to our practice and provide recommendations on approaching difficult cases.