New courses continued to explore AI in new areas over the years. Today, Class Central lists around 550 courses on artificial intelligence in a variety of fields. One of them is healthcare where artificial intelligence is well suited as much of healthcare consists of pattern recognition based on patient data, including physical symptoms, blood pressure, genetic data, X-ray/CT/MRIs scans, pathology data, medication data, and more.
Major tech companies such as Google, Microsoft, and Amazon have provided artificial intelligence solutions in healthcare. The healthcare AI trends have also been accelerated by Covid-19. According to data from CBInsights, a total of 11 healthcare AI companies have closed rounds of $100M+ since March 2020.
By now, many online courses and educational materials on AI in healthcare have been developed and made available to the public. I did some research and compiled the following list of 50+ online courses and webinars on AI in healthcare. I hope it is useful to some of you. Check it out!
In this specialization, we’ll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.
This course explores the fundamentals of the U.S. healthcare system. It will introduce the principal institutions and participants in healthcare systems, explain what they do, and discuss the interactions between them. The course will cover physician practices, hospitals, pharmaceuticals, and insurance and financing arrangements.
This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.
This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.
This capstone project takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient’s journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization.
This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.
This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness.
The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment) will feature guest speakers from academia and industry. The information recommendation part of this course in 2021 will address the problem of global political polarization. Students will be assigned to work on a term project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Students are welcome to formulate a project that leverages their own graduate research.
The course begins with an introduction to clinical care and data, and then explores the use of machine learning for risk stratification and diagnosis, disease progression modeling, improving clinical workflows, and precision medicine. For each of these topics we dive into methodological details typically not covered in introductory machine learning courses, such as the foundations of deep learning on imaging and natural language, interpretability of ML models, algorithmic fairness, causal inference and off-policy reinforcement learning.
This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows.
This course is part of a course series MIT 6.S191 Introduction to Deep Learning by Dr. Katherine Chou from Google Brain. It covers applications of AI in healthcare, end-to-end lung cancer screening, pathology, genomics, higher quality and more equitable learning, generating labels, bias, and uncertainty, plan for model limitations and healthcare patient vs person.
Learn to build, evaluate, and integrate predictive models that have the power to transform patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis and then move on to modeling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion.
By the end of this course, students will recognize the different types of health and healthcare data, will articulate a coherent and complete question, will interpret queries designed for secondary use of EHR data, and will interpret the results of those queries.
On this course you will consider why we might need AI in healthcare, exploring the possible applications and the issues they might cause such as whether AI is dehumanizing healthcare. You should leave the course more confident in your knowledge of AI and how it might improve today’s healthcare systems.
In this course, you’ll explore the benefits and challenges of sharing healthcare data globally. With support from industry experts, you’ll consider topics like the future of medical development, improving care, healthcare accessibility, and more. You’ll also discover the strategies used by governments, funding bodies, institutions, and publishers to get access to datasets.
In this course, you’ll learn digital health applications on complex diseases. Based on WHO guidelines, digital health intervention is defined as a discrete functionality of digital technology that is applied to achieve health objectives. We will explore research on the development of digital health. Issues like why digital health is important and how digital health can affect our future will be discussed.
In this course, you’ll discover how AI has the potential to change – and challenge – the hospitality industry. Understand how to grasp the opportunities it presents, including lowering costs, improving customer satisfaction, and innovating front office and housekeeping.
Using case studies, you will learn why regulations are essential for the safe use of robots and AI in healthcare, and understand the process of bringing a successful product to market. You will also explore how artificial intelligence is used in surgical procedures, to improve precision diagnostics, in exoskeleton technology, and even for patient care.
In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data. This is a first step on the path to mastering machine learning.
This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data. By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings.
This course will prepare you to complete all parts of the Clinical Data Science Specialization. In this course you will learn how clinical data are generated, the format of these data, and the ethical and legal restrictions on these data. You will also learn enough SQL and R programming skills to be able to complete the entire Specialization – even if you are a beginner programmer.
This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.
This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension.
This course teaches you the fundamentals of clinical natural language processing (NLP). In this course you will learn the basic linguistic principles underlying NLP, as well as how to write regular expressions and handle text data in R. You will also learn practical techniques for text processing to be able to extract information from clinical notes. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop text processing algorithms to identify diabetic complications from clinical notes.
Throughout these four modules we will examine the use of decision support, journey mapping, predictive analytics, and embedding Machine Learning and Artificial Intelligence into the healthcare industry.
This course teaches you the fundamentals of transforming clinical practice using predictive models. This course examines specific challenges and methods of clinical implementation that clinical data scientists must be aware of when developing their predictive models.
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Machine Learning for Healthcare: The Online Course This course provides a less-technical and more healthcare-tailored introduction to machine learning, and the nuances of applying it to healthcare.It will help you distinguish hype from reality, contribute to exciting research and impactful companies and, ultimately, to scale your positive health impact.
With the current pandemic accelerating the revolution of AI in healthcare, where is the industry heading in the next 5-10 years? What are the key challenges and most exciting opportunities? These questions will be answered by HAI’s Co-Director, Fei-Fei Li and the Founder of DeepLearning.AI, Andrew Ng in this fireside chat virtual event.
In this session faculty from the Stanford AI in Healthcare specialization discuss the challenges and opportunities involved in bringing AI into the clinic, safely and ethically, as well as its impact on the doctor-patient relationship. They also outline a framework for analyzing the utility of machine learning models in healthcare and will describe how the US healthcare system impacts strategies for acquiring data to power machine learning algorithms.
What does AI mean for the future of health care? Stanford Medicine first began exploring artificial intelligence in medicine in the 1980s; today, we are witnessing a renaissance in AI research. The group from Stanford University discussed everything from physician job security to AI’s potential to increase inequality in health care.
Recent advances in artificial intelligence and machine learning are changing the way doctors practice medicine. Can medical data actually improve health care? At this seminar, Harvard Medical School scientists and physicians will discuss how AI assists doctors in diagnosing disease, determining the best treatments and predicting better outcomes for their patients.
Hear panelists discuss why AI+Healthcare projects are imperative to furthering healthcare advancement and where they believe the future of healthcare is headed. Some of the topics include specific AI for healthcare projects – its genesis, hurdles, eventual learning and some of the most important specificities in AI+Healthcare projects compared to other AI+X projects such as AI+Banking or AI+Manufacturing
deeplearning.ai presents Pie & AI: Real-world AI Applications in Medicine. We’ve gathered experts in the AI and medicine field to share their career advice and what they’re working on. Come celebrate the launch of our new AI For Medicine Specialization and hear from experts in the AI and medicine field.
This webinar, hosted by the National Academy of Medicine and the U.S. Government Accountability Office explored the vision, opportunities, challenges, and implications of the use of artificial intelligence in healthcare. Speakers reviewed recent publications focused on AI and health care from the NAM and GAO.
In the fourth interview in our AI for Business Leaders Webinar Series, Alex Ermolaev, Director of AI at ChangeHealthcare, shares his experience implementing AI projects for the company, how he got into AI, what his day-to-day looks like, how they apply AI to all their business units, when AI is better than human insight and vice-versa, when it is necessary to implement, and much more!
Could machine learning give new insights into diseases, widen access to healthcare, and even lead to new scientific discoveries? Already we can see how machine learning can increase the accuracy of diagnoses from medical imaging, and may be able to predict a patient’s risk of disease. This Keynote Session includes short talks about how they are bringing technological innovations in AI and machine learning to healthcare.
Analysis of medical images is essential in modern medicine. With the ever-increasing amount of patient data, new challenges and opportunities arise for different phases of the clinical routine, such as diagnosis, treatment, and monitoring. The InnerEye research project focuses on the automatic analysis of patients’ medical scans.