Artificial Intelligence and Machine Learning in Neuroimaging

Edited by Dr. Reza Forghani, this issue of Neuroimaging Clinics of North America delves into the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the field of neuroimaging. The comprehensive collection of articles provides a deep dive into the history, evolution, and applications of AI and ML in various aspects of neuroimaging.

A Brief History of Artificial Intelligence

This article offers a chronological overview of AI, from its inception to the current state-of-the-art technologies. It sets the stage for understanding the role of AI in neuroimaging and its potential applications.

Evolution of Approaches for Computerized Image Analysis

This piece explores the progression of computerized image analysis techniques, highlighting how they have paved the way for the integration of AI and ML in neuroimaging.

Overview of Machine Learning Part 1: Classic Approaches

This article provides an in-depth look at traditional ML approaches, including supervised and unsupervised learning, and their relevance in neuroimaging applications.

Overview of Machine Learning Part 2: Artificial Neural Networks & Deep Learning

Building on the first part, this article delves into the world of artificial neural networks and deep learning, shedding light on their capabilities and applications in neuroimaging.

Overview of Natural Language Processing

This article introduces readers to the realm of natural language processing, discussing its potential to revolutionize how we interact with and interpret neuroimaging data.

Artificial Intelligence & Stroke Imaging: An East Coast Perspective

This article presents an East Coast perspective on the application of AI in stroke imaging, highlighting regional approaches and experiences.

Artificial Intelligence & Stroke Imaging: A West Coast Perspective

Offering a contrasting viewpoint, this piece explores the West Coast approach to applying AI in stroke imaging, providing a comprehensive understanding of regional differences and similarities.

Artificial Intelligence Applications for Brain Tumor Imaging

This article focuses on the role of AI in improving the detection, diagnosis, and treatment of brain tumors, showcasing the potential of AI-driven solutions in this critical area.

Diverse Applications of Artificial Intelligence in Neuroradiology

This piece highlights the diverse applications of AI across various domains of neuroradiology, demonstrating the transformative power of AI in enhancing patient care and improving outcomes.

Artificial Intelligence Applications for Head and Neck Imaging

This article delves into the specific applications of AI in head and neck imaging, discussing how AI can aid in the early detection and treatment of diseases in this complex anatomical region.

Artificial Intelligence Applications for Predictive Analytics and Workflow Optimization

Exploring the predictive capabilities of AI, this article shows how AI can be leveraged to optimize workflow and improve patient outcomes in neuroimaging settings.

Artificial Intelligence, Advanced Visualization, and 3D Printing

This piece discusses the synergy between AI, advanced visualization techniques, and 3D printing, illustrating how these technologies can be combined to revolutionize neuroimaging and patient care.

Ethical & Legal Considerations for Artificial Intelligence

As AI becomes increasingly integrated into healthcare, this article addresses the ethical and legal considerations that must be taken into account to ensure the responsible use of AI in neuroimaging.

Comprehensive (or 360) Artificial Intelligence: Beyond Image Interpretation Alone

This final article presents a comprehensive view of AI, moving beyond image interpretation to explore the broader implications and applications of AI in neuroimaging and beyond.

With its diverse range of topics and expert insights, this issue of Neuroimaging Clinics of North America offers a valuable resource for healthcare professionals, researchers, and students seeking to understand the current state and future potential of AI and ML in neuroimaging.