Artificial Intelligence in Health Care (self-paced online)
Dates:
In Artificial Intelligence in Health Care, the MIT Sloan School of Management and the MIT J-Clinic aim to equip health care leaders with a grounded understanding of the potential for AI innovations in the health care industry. The course explores types of AI technology, its applications, limitations, and industry opportunities.
This program is delivered in collaboration with GetSmarter. Please register on the GetSmarter website.
Program Details
Participants
Modules
Takeaways
Faculty
GetSmarter
Technical Requirements
Artificial Intelligence in Health Care (self-paced online) Certificate Track: Management and Leadership Location:
Online Tuition:
$2,800 Program Days (for ACE Credit) 2
The potential of artificial intelligence (AI) to transform health care — through the work of both organizational leaders and medical professionals — is increasingly evident as more real-world clinical applications emerge.
As patient data sets become larger, manual analysis is becoming less feasible. AI has the power to efficiently process data far beyond our own capacity, and has already enabled innovation in areas including chemotherapy regimens, patient care, breast cancer risk, and even ICU death prediction.
With this program, the MIT Sloan School of Management and the MIT J-Clinic aims to equip health care leaders with a grounded understanding of the potential for AI innovations in the health care industry. The Artificial Intelligence in Health Care online short course explores types of AI technology, its applications, limitations, and industry opportunities. Techniques like natural language processing, data analytics, and machine learning will be investigated across contexts such as disease diagnosis and hospital management.
This course aims to empower leaders in both business and medical roles with the knowledge to understand the transformative role of artificial intelligence (AI) in health care. Key learning areas include the basics of machine learning, neural networks, and deep learning. Participants can expect to immediately and directly apply the knowledge gained in this course in their own roles within the health care sector. Professionals working for health care providers will learn to identify the types of problems that AI techniques can help solve.
This online program integrates rich, interactive media such as videos, infographics, and e-learning activities as well as traditional didactic components such as downloadable course notes. There are also opportunities for collaborative learning through discussion forums. The following modules contribute to the holistic approach your learning path takes:
ORIENTATION MODULE: WELCOME TO YOUR ONLINE CAMPUS (one week) You’ll be welcomed to the program and begin connecting with fellow participants, while exploring the navigation and tools of your Online Campus. Be alerted to key milestones in the learning path, and review how your results will be calculated and distributed.
You’ll be required to complete your student profile, confirm your certificate delivery address, and submit a digital copy of your passport/identity document.
MODULE 1: AI AND MACHINE LEARNING — APPLICATIONS AND FOUNDATIONS Become familiar with supervised machine learning and the types of problems it may be applied to.
MODULE 2: USING AI FOR DISEASE DIAGNOSIS AND PATIENT MONITORING Examine real-world applications of AI for diagnosis and patient monitoring.
MODULE 3: NATURAL LANGUAGE PROCESSING AND DATA ANALYTICS IN HEALTH CARE Use AI to extract value-adding outcomes from medical literature and pathology reports.
MODULE 4: INTERPRETABILITY IN MACHINE LEARNING — BENEFITS AND CHALLENGES Appreciate the importance and benefits of interpretable algorithms.
MODULE 5: PATIENT RISK STRATIFICATION AND AUGMENTING CLINICAL WORKFLOWS Discover how AI can be applied to health care interventions and patient care.
MODULE 6: TAKING AN INTEGRATED APPROACH TO HOSPITAL MANAGEMENT AND OPTIMIZATION Investigate a holistic approach to optimizing health care processes.
Over the course of six weeks, you’ll develop a holistic understanding of AI’s growing role in health care through an immersive online experience that draws on real-world case studies. You’ll explore how AI strategies have already been successfully deployed within the sector, and learn to ask the right questions when evaluating an AI technique for potential use within your own context.
You’ll gain an overview of the technology before delving into its practical adoption challenges, with regards to both hospital processes and resource management. Guided by MIT faculty and health care experts, you’ll examine the use of AI in diagnosis, patient monitoring and care, and explore how it can be applied to enhance health care data management. You’ll also learn to apply an integrated approach to hospital management and optimization, and develop a framework to assess the viability of using AI within your health care context.
Please note that faculty are subject to change and not all faculty teach in each session of the program.
Regina Barzilay is a Delta Electronics professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.
Boeing Leaders for Global Operations Professor of Management
Dimitris Bertsimas is the Boeing Leaders for Global Operations Professor of Management, a Professor of Operations Research, the CoDirector of the Operations Research Center and the Director of the Master of Business Analytics at MIT.
A faculty member since 1988, his research interests include optimization, stochastic systems, machine learning, and their application. In recent years, he has worked in robust optimization, statistics, healthcare, transportation and finance. Bertsimas was a cofounder of Dynamic Ideas, LLC, which developed portfolio management tools for asset management. In 2002, the assets of Dynamic Ideas were sold to American Express. He is also the founder of Dynamic Ideas Press, a publisher of scientific books, the cofounder of Benefits Science, a company that designs health care plans for companies, of Dynamic Ideas Financial, a company that provides financial advice to customers, of Alpha Dynamics, an asset management company, P2 Analytics, an analytics consulting company and of MyA health, a personalized health care advice company.
Bertsimas has coauthored more than 200 scientific papers and the following books: Introduction to Linear Optimization (with J. Tsitsiklis, Athena Scientific and Dynamic Ideas, 2008); Data, Models, and Decisions (with R. Freund, Dynamic Ideas, 2004); Optimization over Integers (with R. Weismantel, Dynamic Ideas, 2005); and The Analytics Edge (with A. O'Hair andW. Pulleyblank, Dynamic Ideas, 2016). He is former department editor of Optimization for Management Science and of Operations Research in Financial Engineering. Bertsimas has supervised 59 doctoral and 31 Master students. He is currently supervising 22 doctorla students. A member of the National Academy of Engineering and an INFORMS fellow, he has received numerous research awards, including the Harold Larnder Prize (2016), the Philip Morse Lecturship prize (2013), the William Pierskalla best paper award in health care (2013), best paper award in Trapsoration (2013), the Farkas Prize (2008), the Erlang Prize (1996), the SIAM Prize in Optimization (1996), the Bodossaki Prize (1998), and the Presidential Young Investigator Award (1991–1996). He has also received recognition for his educational contributions: The Jamieson prize (2013) and the Samuel M. Seegal prize (1999).
Bertsimas holds a BS in electrical engineering and computer science from the National Technical University of Athens, Greece, as well as an MS in operations research and a PhD in applied mathematics and operations research from MIT.
MIT Sloan Executive Education is collaborating with online education provider GetSmarter to create a new class of learning experience - one that is higher-touch, intimate, and personalized for the working professional. By working with GetSmarter, we are able to broaden access beyond our on-campus offerings in a collaborative and engaging format that stays true to the quality of MIT Sloan and MIT as a whole. GetSmarter's people-driven approach to online learning—centered around the importance of human support—has resulted in an aggregate completion rate consistently above 90% serving more than 48,000 participants over almost a decade.
BASIC REQUIREMENTS
In order to complete a course, you’ll need a current email account and access to a computer and the internet. You should be familiar with using a computer and accessing the internet, as you may need to read documents in Adobe PDF Reader, view Microsoft PowerPoint presentations, and read and create documents in Microsoft Word. Installing Adobe Flash Player will give you full access to certain course content, such as interactive infographics. However, you’ll still have access to this content in the form of a downloadable PDF transcript if you’d prefer not to use Flash.
We recommend that you use Google Chrome as your internet browser when accessing the Online Campus. Although this is not a requirement, we have found that this browser performs best for ease of access to course material. This browser can be downloaded here.
ADDITIONAL REQUIREMENTS
Certain courses may require additional software and resources. These additional software and resource requirements will be communicated to you upon registration and/or at the beginning of the course. Please note that Google, Vimeo, and YouTube maybe used in our course delivery, and if these services are blocked in your jurisdiction, you may have difficulty in accessing course content. Please check with a Course Consultant before registering for this course if you have any concerns about this affecting your experience with the Online Campus.
Registration for this program is done through GetSmarter.
FAST FACTS
6 weeks(excluding orientation)
Entirely online
Self-paced learning
6-8 hours/week*
*The recommended weekly time commitment for core content is 4-5 hours, taking into account the busy lifestyles of working professionals, with an additional 2-3 hours recommended for non-compulsory weekly extension activities, should you have the time. Each module is released weekly, allowing a flexible but structured approach to learning. You’ll be supported as you engage in individual activities and group discussions, ensuring you feel confident to submit your best work at each weekly deadline.
"Health care is mostly about predicting what the patient currently has and what treatment will work. AI provides you with a toolbox of algorithms that can remove the guesswork." – Regina Barzilay, Delta Electronics Professor of Electrical Engineering and Computer Science
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