Contributors, i
Authors
Artificial Intelligence, Bioinformatics, and Pathology: Emerging Trends Part I—an Introduction to Machine Learning Technologies, e1
By Joshua Levy, Yunrui Lu, Marietta Montivero, Ojas Ramwala, Jason McFadden, Carly Miles, Adam Gilbert Diamond, Ramya Reddy, Ram Reddy, Taylor Hudson, Zarif Azher, Akash Pamal, Sameer Gabbita, Tess Cronin, Abdol Aziz Ould Ismail, Tarushii Goel, Sanjay Jacob, Anish Suvarna, Sumanth Ratna, Jason Zavras, and Louis Vaickus
Artificial intelligence technologies are capable of performing complex tasks after being presented with many examples from which to build associations. In this review article, we introduce challenges introduced through burgeoning use of complex high-dimensional assays in Pathology and how machine learning technologies are well equipped to address many of these challenges. Finally, we summarize and comment on future trends, considerations and challenges that will need to be addressed to ensure the viability of these technologies in the decades to come.
Introduction, e2
Grasping the enormous complexity of anatomic and molecular pathology information, e3
Overview of Anatomic Pathology Data Types, e3
Overview of Molecular Pathology Data Types, e3
Further Complexities Emerge When High-Dimensional Clinical Variables Interact, e4
Artificial intelligence overview, e4
Modern Computers Exhibit a Narrowly Defined Intelligence, e6
Rules-Based Artificial Intelligence Approaches and Challenges, e7
Machine Learning, e8
Types of Machine Learning, e8
Emergence of Deep Learning Methods, e9
Common Machine Learning Algorithms, e10
Developing a machine learning model, e10
Popular Applications of Artificial Intelligence in Medicine, e16
Popular Applications in Pathology, e16
Perspectives on future trends for facilitating adoption of artificial intelligence technologies in pathology, e17
Clinical Trials Applications and Common Statistical Fallacies, e17
Rapid Data Annotation and Data Valuation, e17
Algorithmic Bias and Ethics, e18
Validation of Technologies, Interfacing with Stakeholders, and Educational Efforts, e18
Artificial Intelligence in Pathology and Health Care in Low-Income Settings, e18
Summary, e20
Clinics care points, e20
Artificial Intelligence, Bioinformatics, and Pathology: Emerging Trends Part II––Current Applications in Anatomic and Molecular Pathology, e25
By Joshua Levy, Yunrui Lu, Marietta Montivero, Ojas Ramwala, Jason McFadden, Carly Miles, Adam Gilbert Diamond, Ramya Reddy, Ram Reddy, Taylor Hudson, Zarif Azher, Akash Pamal, Sameer Gabbita, Tess Cronin, Abdol Aziz Ould Ismail, Tarushii Goel, Sanjay Jacob, Anish Suvarna, Taein Kim, Edward Zhang, Neha Reddy, Sumanth Ratna, Jason Zavras, and Louis Vaickus
The advent of complex molecular assays and anatomic imaging platforms has enabled unprecedented characterization of disease pathogenesis. However, challenges remain in formulating diagnostic and prognostic tests that can effectively navigate and synthesize the massive treasure troves of information now available in modern pathology laboratories. Artificial intelligence (AI), and, in particular, machine learning, are pattern mining algorithms that can efficiently uncover biomarkers and risk factors personalized to the individual. These technologies will also have the benefit of providing efficiency gains, resolving ambiguity, and reducing the unnecessary resource expenditures in the Pathology setting. Adoption of AI technologies in these settings can be improved by engaging pathologists on how AI can be meaningfully used in their practice and through dissemination and communication of these core concepts and their potential benefits. In this work, we summarize emerging application areas for AI in the fields of Anatomic and Molecular Pathology.
Introduction, e26
Applications in molecular and anatomic pathology, e26
Select Applications of Artificial Intelligence in Anatomic Pathology, e26
Select Applications of Artificial Intelligence in Molecular Pathology, e35
Integration of Anatomic and Molecular Pathology, e40
Discussion, e42
Summary, e44
Clinics care points, e44
