11 - 20 of 165 results for: artificial intelligence

Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.

The widespread adoption of electronic health records (EHRs) has created a new source of big data namely, the record of routine clinical practice as a by-product of care. This class will teach you how to use EHRs and other patient data in conjunction with recent advances in artificial intelligence (AI) and evolving business models to improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of care questions that AI can help answer, describe common healthcare data sources and their relative advantages, limitations, and biases in enabling care transformation, understand the challenges in using various kinds of clinical data to create fair algorithmic interventions, design an analysis of a clinical dataset, evaluate and criticize published research to separate hype from reality. Prerequisites: enrollment in the MCiM program. This course is designed to prepare you to pose and answer meaningful clinical questions using healthcare data as well as understand how AI can be brought into clinical use safely, ethically and cost-effectively.

Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.

This interdisciplinary course explores the convergence of Web3 technologies, artificial intelligence (AI), and their transformative impact on the field of digital health. Students will examine the potential of decentralized systems, blockchain, and smart contracts to enhance health data privacy, security, and interoperability. Through case studies and hands-on projects, they will gain insights into AI-driven solutions for personalized healthcare, remote patient monitoring, medical image analysis, and clinical decision support. Additionally, students will critically analyze ethical and regulatory considerations in the context of Web3 and AI applications, fostering a deeper understanding of the future of digital health innovation.

Artificial Intelligence (AI) platforms are now widely available, and often require little training or technical expertise. This mini-course focuses on responsible development and use of AI in healthcare. Focus is on the critical analysis of AI systems, and the evolving policy and regulatory landscape. Week one covers modern AI capabilities, including computer vision, natural language processing, and reinforcement learning. Weeks two and three focus on assessing AI systems (including robustness, bias, privacy, and interpretability) and applications (including radiology, suicide prevention, and end-of-life care). Throughout this course students will develop and evaluate a hypothetical AI system. No programming experience is required.

This course delves into the fundamental principles of Deep Learning within the medical field, designed to offer a thorough yet accessible introduction to how these advanced models function, are developed, and are currently transforming healthcare practices. The curriculum covers key areas including neural network architecture, computer vision, natural language processing, convolutional neural networks, alongside classification and regression techniques, aiming to provide students with a solid foundation and intuitive insight into the workings of deep learning applications in medicine.In addition to the core content, participants will have the opportunity to engage with expert-led discussions on the latest advancements and future directions at the intersection of artificial intelligence and medicine.

The next wave of technological innovation and globalization will affect our countries, our societies, and ourselves. This interdisciplinary course provides an introduction to emerging, frontier technologies. Topics covered include artificial intelligence, additive manufacturing and advanced robotics, smart cities and urban mobility, telecommunications with 5G/6G, and other key emerging technologies in society. These technologies have vast potential to address the largest global challenges of the 21st century, ushering in a new era of progress and change.

The next wave of technological innovation and globalization will affect our countries, our societies, and ourselves. This interdisciplinary course provides an introduction to emerging, frontier technologies. Topics covered include artificial intelligence, additive manufacturing and advanced robotics, smart cities and urban mobility, telecommunications with 5G/6G, and other key emerging technologies in society. These technologies have vast potential to address the largest global challenges of the 21st century, ushering in a new era of progress and change.

This course focuses on a geometric learning approach to derive, test, and validate a wide range of artificial intelligence enabled models for engineering (meta-materials, composites, alloys) and natural materials (soil, rock, clay). Students will learn how to incorporate a wide range of data stored in graphs, manifold and point sets to train neural networks to design optimal experiments, embed high-dimensional data, enforce mechanics and physical principles, denoise data with geometry, and enable model-free simulations and discover causality of mechanisms that leads to the failures of materials. Prerequisite: Linear algebra and CEE291