Artificial intelligence (AI), with its lofty promises, has always captured the public’s attention — most recently, with ChatGPT, OpenAI’s incredibly articulate chatbot. But long before that, AI helped popularize the modern online education movement, since it was the subject of one of the “original MOOCs”, Thrun and Norvig’s Introduction to AI, back in 2011.

But more than a self-contained academic discipline, artificial intelligence has a wealth of applications in other disciplines and sectors — most notably, in healthcare. Indeed, AI is good at pattern recognition and, thereby, well-suited for analysing the ever-increasing amount of patient data that is collected, from symptoms to blood tests to medical imagery.

Important universities have recognized the potential of artificial intelligence in healthcare and launched dedicated initiatives. In 2018, Stanford founded the Center for AI in Medicine & Imaging and MIT established the Jameel Clinic for AI & healthcare, both geared toward a common goal: using AI to tackle medical problems and improve health outcomes.

Fortunately, these universities and many more around the world offer courses on the topics. Below, we’ve compiled more than 100 free online courses and webinars on the uses of AI in healthcare.

If you’d rather learn about general AI, Class Central lists over 1100 courses on the topic, and we also have a guide: 10 Best AI Courses to Take in 2023.

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AI in Healthcare Online Courses

AI in Healthcare
Stanford University via Coursera
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.

Introduction to Healthcare
Stanford University via Coursera
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.

Introduction to Clinical Data
Stanford University via Coursera
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.

Fundamentals of Machine Learning for Healthcare
Stanford University via Coursera
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.

Evaluations of AI Applications in Healthcare
Stanford University via Coursera
This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

AI in Healthcare Capstone
Stanford University via Coursera
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.

AI for Medicine
DeepLearning.AI via Coursera
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.

AI for Medical Diagnosis
DeepLearning.AI via Coursera
In this course, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders.
★★★★☆ (1 rating)

AI for Medical Prognosis
DeepLearning.AI via Coursera
In this course, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis.

AI For Medical Treatment
DeepLearning.AI via Coursera
In this course, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports.

Machine Learning for Healthcare
Massachusetts Institute of Technology via edX
An introduction to machine learning for healthcare, ranging from theoretical considerations to understanding human consequences of deploying technology in the clinic, through hands-on Python projects using real healthcare data.

AI for Healthcare
via Udacity
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.
★★★★★ (1 rating)

The Data Science of Health Informatics
Johns Hopkins University via Coursera
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.

Artificial Intelligence for Healthcare: Opportunities and Challenges
Taipei Medical University via FutureLearn
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.

AI and Big Data in Global Health Improvement
Taipei Medical University via FutureLearn
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.
★★★★★ (44 ratings)

Application of Digital Health Interventions
Taipei Medical University via FutureLearn
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.
★★★★★ (2 ratings)

Artificial Intelligence (AI) in Hospitality: Challenges and Business Opportunities
Hotel Institute Montreux via FutureLearn
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.

MedTech: AI and Medical Robots
University of Leeds via FutureLearn
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.
★★★★★ (10 ratings)

AI for Healthcare: Equipping the Workforce for Digital Transformation
University of Manchester via FutureLearn
In this course, you will develop your own digital skills and increase your understanding of technology for healthcare, so that you can join the conversation on embedding AI in healthcare practice.

Business Application of Machine Learning and Artificial Intelligence in Healthcare
Northeastern University via Coursera
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.

Medical Diagnosis using Support Vector Machines
Coursera Project Network via Coursera
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.

Clinical Data Science
University of Colorado System via Coursera
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.

Introduction to Clinical Data Science
University of Colorado System via Coursera
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.

Clinical Data Models and Data Quality Assessments
University of Colorado System via Coursera
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), and differentiate between data models and articulate how each are used to support clinical care and data science.

Identifying Patient Populations
University of Colorado System via Coursera
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.

Clinical Natural Language Processing
University of Colorado System via Coursera
This course teaches you the fundamentals of clinical natural language processing (NLP). In this course you will learn the basic linguistic principals 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.

Advanced Clinical Data Science
University of Colorado System via Coursera
This course prepares you to deal with advanced clinical data science topics and techniques including temporal and research quality analysis.

Predictive Modeling and Transforming Clinical Practice
University of Colorado System via Coursera
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.

PyTorch and Monai for AI Healthcare Imaging – Python Machine Learning Course
via freeCodeCamp

Trustworthy AI for Healthcare Management
Politecnico di Milano via Coursera
This MOOC gives an introduction to trustworthy artificial intelligence and its application in healthcare. This includes modules on basics of artificial intelligence and an introduction to trustworthy and ethical applications of artificial intelligence. A dedicated lesson will present the Z-Inspection process for assessing trustworthy AI.

AI business school for healthcare
Microsoft via Microsoft Learn
Learn to develop an AI strategy to create business value in healthcare, including machine learning technologies, culture, and responsible AI.

AI for Healthcare
Great Learning via YouTube

How Artificial Intelligence Can Support Healthcare
University of Groningen via FutureLearn
Explore how AI can be used to improve patient care and build your understanding of how to implement AI in the health professions.
★★★★★ (1 rating)

Complete Machine Learning & Data Science Bootcamp 2022
via Udemy
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

The Complete Healthcare Artificial Intelligence Course 2022
via Udemy
Creating powerful AI model for Real-World Healthcare applications with Data Science, Machine Learning and Deep Learning

AI-Powered Chest Disease Detection and Classification
Coursera Project Network via Coursera
Hello everyone and welcome to this hands-on guided project on Artificial intelligence (AI)-powered chest disease detection and classification. We will automate the process of detecting and classifying chest disease from X-Ray images to reduce the cost and time of detection. This guided project is practical and directly applicable to the healthcare industry.

Ethics and Governance of Artificial Intelligence for Health
via OpenWHO
Artificial intelligence (AI) has enormous potential for improving health outcomes and helping countries achieve universal health coverage. However, for AI to have a beneficial impact on people’s health, ethical considerations and human rights must be placed at the centre of its design, development and use.

Machine Learning Predictions with FHIR and Healthcare API
Google Cloud via Coursera
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will create a prediction pipeline for FHIR resources using Cloud Healthcare API and AI Platform.

Introduction to AI Applications in Pulmonary Medicine
Taipei Medical University via FutureLearn
Discover how AI, machine learning and deep learning have improved clinical practice in the treatment of respiratory diseases.

Big Data Science with the BD2K-LINCS Data Coordination and Integration Center
Icahn School of Medicine at Mount Sinai via Coursera
The BD2K-LINCS Data Coordination and Integration Center (DCIC) is commissioned to organize, analyze, visualize and integrate this data with other publicly available relevant resources. In this course we briefly introduce the DCIC and the various Centers that collect data for LINCS. We then cover metadata and how metadata is linked to ontologies.
★★★★☆ (1 rating)

Introduction to Digital health
Imperial College London via Coursera
This course introduces the field of digital health and the key concepts and definitions in this emerging field. The key topics include Learning Health Systems and Electronic Health Records and various types of digital health technologies to include mobile applications, wearable technologies, health information systems, telehealth, telemedicine and more.

Machine Learning for Healthcare (Spring 2019)
Massachusetts Institute of Technology via MIT OpenCourseWare
This course introduces students to machine learning in healthcare, including the nature of clinical data, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows.

AI in Practice: Applying AI
Delft University of Technology via edX
Learn about the implementation and practical aspects of Artificial Intelligence and how to write a plan for applying AI in your own organization in a step-by-step manner.

Informed Clinical Decision Making using Deep Learning
University of Glasgow via Coursera
This specialisation is for learners with experience in programming that are interested in expanding their skills in applying deep learning in Electronic Health Records and with a focus on how to translate their models into Clinical Decision Support Systems.

Wondrium Pilots: Ethical Health Care in the Age of AI
via The Great Courses Plus
Explore both the ethics and policy of health care in the age of AI with a professor of philosophy and psychology.

Machine Learning for Healthcare
via Pluralsight
This course will explore the conceptual aspects of applying machine learning to problems in the healthcare industry, discuss case studies of machine learning used in healthcare, and explore practical implementations of techniques on real-world data from that industry.

Information Extraction from Free Text Data in Health
University of Michigan via Coursera
In this MOOC, you will be introduced to advanced machine learning and natural language processing techniques to parse and extract information from unstructured text documents in healthcare, such as clinical notes, radiology reports, and discharge summaries. It’s critical that you keep up-to-date your skills in information extraction and analysis.

Machine Learning: Algorithms in the Real World
Alberta Machine Intelligence Institute via Coursera
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application.

Data Science in Stratified Healthcare and Precision Medicine
University of Edinburgh via Coursera
In this course, you will learn about some of the different types of data and computational methods involved in stratified healthcare and precision medicine. You will have a hands-on experience of working with such data.

Deep Learning Methods for Healthcare
University of Illinois at Urbana-Champaign via Coursera
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments.

Deep Learning for Healthcare
University of Illinois at Urbana-Champaign via Coursera
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks.

Health Data and Analytics
EIT Health via FutureLearn
Explore key concepts in data analytics, systems theory and information governance, and apply them to healthcare decision-making.
★★★★☆ (2 ratings)

Big Data Analytics for Healthcare
Georgia Institute of Technology via Coursera
We introduce the characteristics and related analytic challenges on dealing with clinical data from electronic health records. Many of those insights come from medical informatics community and data mining/machine learning community. There are three thrusts in this course: Application, Algorithm and System.

Data mining of Clinical Databases – CDSS 1
University of Glasgow via Coursera
This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics.

Deep learning in Electronic Health Records – CDSS 2
University of Glasgow via Coursera
Overview of the main principles of Deep Learning along with common architectures. Formulate the problem for time-series classification and apply it to vital signals such as ECG.

Explainable deep learning models for healthcare – CDSS 3
University of Glasgow via Coursera
This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model specific explanations and more.

Clinical Decision Support Systems – CDSS 4
University of Glasgow via Coursera
Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed.

Capstone Assignment – CDSS 5
University of Glasgow via Coursera
This course is a capstone assignment requiring you to apply the knowledge and skill you have learnt throughout the specialization. In this course you will choose one of the areas and complete the assignment to pass.

Applications of AI Technology
Taipei Medical University via FutureLearn
Learn how AI technology is influencing four key areas: intelligent systems, medtech, deep learning, and sustainable fishing

Artificial Intelligence in Bioinformatics
Taipei Medical University via FutureLearn
Discover the future of bioinformatics and learn how AI models of bioinformatics data help us to understand biological processes.

Advanced Deep Learning Methods for Healthcare
University of Illinois at Urbana-Champaign via Coursera
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project.

Health Data Science Foundation
University of Illinois at Urbana-Champaign via Coursera
This course is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field.

Big Data Analytics in Healthcare
Georgia Institute of Technology via Udacity
In this course, we introduce the characteristics of medical data and associated data mining challenges on dealing with such data. We cover various algorithms and systems.

Deep Learning Application for Healthcare
via Pluralsight
Deep learning is a powerful machine learning technique that has led to incredible innovations in artificial intelligence. This course will teach you the fundamentals of deep learning in healthcare through theory and applied case studies.

Population Health: Predictive Analytics
Leiden University via Coursera
Predictive analytics has a longstanding tradition in medicine. Developing better prediction models is a critical step in the pursuit of improved health care: we need these tools to guide our decision-making on preventive measures, and individualized treatments. In order to effectively use and develop these models, we must understand them better.

Deep Learning with PyTorch for Medical Image Analysis
via Udemy
Learn how to use Pytorch-Lightning to solve real world medical imaging tasks!

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.

MIT 6.S191: AI in Healthcare
Massachusetts Institute of Technology 
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.

MIT 6.S897 Machine Learning for Healthcare, Spring 2019
Massachusetts Institute of Technology 
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.

CS372 Artificial Intelligence for Disease Diagnosis and Information Recommendations 
Stanford University
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.

BIODS220: Artificial Intelligence in Healthcare
Stanford University 
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. 

AI in Healthcare Webinars

Stanford Medcast Episode 28: Hot Topics Mini-series – Artificial Intelligence in Medicine
Stanford University via Independent
Dr. Curtis Langlotz, Professor of Radiology and Biomedical Informatics and Director of the Center for Artificial Intelligence in Medicine and Imaging at Stanford University, shares his insight about the current state of research in AI and how it is currently used in medicine and where it is going in the future.

Stanford Seminar – Deep Learning for Medical Diagnoses
Stanford University via YouTube

Graphics, Artificial Intelligence and Voice UI Applications with the STM32H7A3/B3 – Webinar Replay
STMicroelectronics via YouTube

Artificial Intelligence and Health Care Webinar (RECORDED)
Stanford University via Independent
Explore the vision, opportunities, challenges, and implications of the use of artificial intelligence (AI) in health care. Speakers will review two recent publications focused on AI and health care published by the NAM and GAO.

AI + Health 2021 Recorded Webinar, Track 1: Advancing the Practice + Science of Medicine via AI
Stanford University via Independent
Presented by the Human-Centered Artificial Intelligence and Center for Artificial Intelligence in Medicine and Imaging at Stanford University School of Medicine. This recorded online conference convened experts and leaders from academia, government, and clinical practice to explore critical and emerging issues related to AI’s impact across the spectrum of health.

AI + Health 2021, Recorded Webinar, Track 2: Cross Cutting Issues + Closing the AI Chasm
Stanford University via Independent
Presented by the Human-Centered Artificial Intelligence and Center for Artificial Intelligence in Medicine and Imaging at Stanford University School of Medicine. This recorded online conference convened experts and leaders from academia, government, and clinical practice to explore critical and emerging issues related to AI’s impact across the spectrum of health.

Pediatric Grand Rounds (RECORDING) Improving Accuracy in the Diagnosis and Treatment of Acute Otitis Media in Young Children
Stanford University via Independent
This presentation is a recording of a Stanford Pediatric Grand Rounds Session.  World-renowned experts will present the latest research, practice guidelines, and treatment protocols to advance best practices in the care of pediatric patients. These online recordings will provide pediatricians and family physicians with up-to-date clinical information.

Healthcare’s AI Future: A Conversation with Fei-Fei Li & Andrew Ng
Stanford University
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.

Webinar: Bringing AI into Healthcare Safely and Ethically
Stanford University
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.

The state of artificial intelligence in medicine
Stanford University
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.

MD vs. Machine: Artificial intelligence in healthcare
Harvard University
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.

AI+X: AI Innovation in Healthcare
DeepLearningAI
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.

Pie & AI: Real-world AI Applications in Medicine
DeepLearningAI
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.

Webinar: Artificial Intelligence and Health Care 
National Academy of Medicine
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.

Alex Ermolaev on AI in Healthcare: Udacity AI for Business Leaders Webinar
Udacity
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!

Bringing AI and machine learning innovations to healthcare (Google I/O ’18)
Google Developers
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.

Machine Learning in Health Care
Microsoft Research
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.

Doctors, apps and artificial intelligence – The future of medicine
DW Documentary
Artificial intelligence is changing health care. It promises better diagnoses and fewer mistakes and all in less time. While some associate AI with a frightening dystopian future, many doctors see it as a source of support.

Applied AI in Healthcare: Challenges and Opportunities
Harvard School of Public Health

How Artificial Intelligence Can Improve Healthcare
Stanford Online

Ethics of AI in Healthcare
University of Washington

AI in Healthcare
Stanford Medicine

Building AI models for healthcare
TensorFlow

The Role of AI in Healthcare: A Better Patient Journey
University of California, Irvine

AI and Healthcare
Forum Europe

Health Care and Life Sciences Experience at Data
Databricks

Adoption of AI and Machine Learning in Healthcare
GE Healthcare

Bringing AI into Healthcare Safely and Ethically
Stanford Online