Artificial Intelligence: Laboratory Medicine / Pathology

Artificial Intelligence and Deep Learning in Pathology by Stanley. Cohen, 2021 Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, with a team of experts, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.

Artificial Intelligence in Microbiology: Scope and Challenges Volume 1 by Akanksha Srivastava; Vaibhav Mishra, 2024 Nowadays, the field of microbiology is undergoing a revolutionary change due to the emergence of Artificial Intelligence (AI). AI is being used to analyze massive data in a predictable form, about the behavior of microorganisms, to solve microbial classification-related problems, exploring the interaction between microorganisms and the surrounding environment. It also helps to extract novel microbial metabolites which have been used in various fields like medical, food and agricultural industries. As the pace of innovation in Microbiology is accelerating, the use of AI in these industries will be beneficial. AI will not only show its extraordinary potential in expanding the market of antibiotics, food, and agriculture but also offer an eco-friendly, safer, and profitable solution to the respective industries. It would be challenging to search out specific features and discuss future research on AI in microbiology with a wide perspective.

Artificial Intelligence in Pathology: Principles and Applications, 2nd ed. by Chhavi Chauhan; Stanley Cohen, 2025 Artificial Intelligence in Pathology: Principles and Applications provides a strong foundation of core artificial intelligence principles and their applications in the field of digital pathology. This is a reference of current and emerging use of AI in digital pathology as well as the emerging utility of quantum artificial intelligence and neuromorphic computing in digital pathology. It is a must-have educational resource for lay public, researchers, academicians, practitioners, policymakers, key administrators, and vendors to stay current with the shifting landscapes within the emerging field of digital pathology. It is also of use to workers in other diagnostic imaging areas such as radiology. This resource covers various aspects of the use of AI in pathology, including but not limited to the basic principles, advanced applications, challenges in the development, deployment, adoption, and scalability of AI-based models in pathology, the innumerous benefits of applying and integrating AI in the practice of pathology, ethical considerations for the safe adoption and deployment of AI in pathology.

Fine Needle Aspiration Cytology of the Breast: Atlas of Cyto-Histologic Correlates , 2nd ed. by Gary Tse; Fernando Schmitt; Puay-Hoon Tan, 2023 This book provides a detailed update on all aspects of fine-needle aspiration cytology of breast lesions. It will serve as an up-to-date reference and atlas on new entities in breast pathology, including classic benign and malignant lesions, in accordance with current classification schemes and staging protocols. Emphasis is placed on description of characteristic diagnostic features as well as common cytological pitfalls. Correlation of cytology with histology is achieved through cyto-histological illustrations, enabling readers to gain a good understanding of the morphologic features of various lesions. Core-needle biopsy and fine-needle aspiration cytology are compared in depth, with attention to their relative strengths and weaknesses and indications. In addition, the impact of molecular classification of breast cancers on cytologic diagnosis is explored, and the use of molecular techniques and artificial intelligence in diagnostic cytology is discussed.