Artificial Intelligence in Healthcare: Fundamentals and Applications

Harness the power of artificial intelligence (AI) to elevate modern-day medical treatments

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Solutions for the Future of Healthcare With AI

AI plays an increasingly important role in patient treatment. With its ability to accurately predict diseases at early stages, AI is regarded as a powerful tool in today’s healthcare industry.

Since AI offers benefits such as informed patient care, improved patient safety, and innovative treatment options, it isn’t surprising that 56% of clinicians believe that most of their decisions over the next decade will be made using AI-based clinical decision support tools. However, clinicians face a knowledge gap and report the rising need for professionals who understand AI-based technologies and ways of leveraging them for the advantage of healthcare providers and patients.

With a focus on the application of AI in modern-day healthcare, MIT xPRO’s AI in Healthcare: Fundamentals and Applications program is designed to give clinical leaders, healthcare IT professionals, and healthcare entrepreneurs the opportunity to learn how AI technologies can make a difference in patient treatment and enable them to develop innovative solutions to the healthcare challenges of today and tomorrow.

Who Is This Program For?

A basic understanding of artificial AI, machine learning, data science, and AI in healthcare is ideal in order to make the most of this program.

  • Technical professionals in the healthcare industry: Technical professionals, such as technical product managers, product managers, directors of product, product design managers, engineering managers, technology project managers, directors of engineering, and directors of technology, who are looking to drive the adoption of AI technology in healthcare at their organizations by adding AI products and services to their arsenals so as to empower patient care

  • Entrepreneurs: Leaders in their organizations’ foray into AI technology in healthcare who are searching for the right AI solutions to impact millions of lives

  • Clinical leaders: Senior physicians, department chairs, medical directors, chief medical officers, physician service line directors, nurse leaders, hospital administrators, directors of clinical services, and nursing/medical school deans seeking to sharpen their understanding of AI products and methodologies to advance patient care and identify new opportunities emerging from the field

  • Tech consultants in healthcare: Consultants in hospitals, digital health, pharma, med tech, research, and insurance who are responsible for shaping the AI strategies for organizations and are, therefore, looking to upgrade their knowledge about new technologies, thereby changing the healthcare system

Note: This program has no prerequisites. However, a preliminary understanding of AI, machine learning, and data is recommended to maximize the program's potential.

Program Highlights

SLP - MO-AIHC - Learn real-world - Icon

Learn real-world application of ai in healthcare simulation by creating an AI decision framework relevant to healthcare

SLP - MO-AIHC - Gain an understanding - Icon

Gain an understanding of concepts and technologies, such as machine learning, deep learning, neural network NLP, and biomechatronics

SLP - MO-AIHC - Acquire insights - Icon

Acquire insights and examples from expert MIT xPRO faculty

SLP - MO-AIHC - Develop your ability - Icon

Develop your ability to assess challenges, opportunities, and future-driven patient care solutions involving a variety of AI technologies

SLP - MO-PCCW - Earn a certificate - Icon

Earn a certificate and four continuing education units (CEUs) from MIT xPRO

Key Takeaways

This program is designed to equip you with the skills to broaden your understanding of the applications of AI technology in healthcare. The program will help you to:

Build a Foundation

  • Learn about the AI design process model through its various stages

  • Understand different machine learning algorithms and how they can be applied in varying scenarios

  • Examine neural network NLP algorithms and their widespread application

  • Discover the possibilities and limitations of biomechatronics

Apply Your Skills

  • Use the four stages of the AI design process model to solve a technical healthcare problem

  • Run an implementation of a single- and multilayer perceptron in Python

  • Develop an idea for an ingestible robot to solve a healthcare problem

  • Use the peloton framework, designed by research scientist Brian Subirana, within your specific healthcare domain


  • Assess business and technical requirements for AI

  • Map the AI design process and arrive at a cost model

  • Resolve a communication problem while using prosthetics

  • Develop an AI product or service for healthcare

Program Topics

Case Studies and Research

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Generative adversarial networks (GANs) by Elazer Edelman

Through this research, identify how to generate realistic-looking images of the heart that can be instrumental for cardiologists in diagnosis and treatment.

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AI to identify genetic predisposition to diseases by Manolis Kellis

From this research, you learn about “GENCODE — the” attempt at creating an encyclopedia of genes and genetic variants.

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Graph neural networks (GNNs) by James Collins, Tommi Jaakkola, and Regina Barzilay

This research contains a useful tool for antibiotic discovery that can aid you in your own drug discovery.

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Mammograms for early diagnosis of breast cancer in women by Regina Barzilay

Through this research, learn how to use an image-based, deep learning model that can predict breast cancer up to five years in advance.

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Developing bionic parts using AI by Hugh Herr

From this research, you learn how to create bionic limbs that emulate the function of natural limbs.

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Electromagnetic waves (Wi-Fi) for adherence-related problems by Dina Katabi and Fadel Abib

From this case study, you learn how the instrument named Emerald functions. It measures sleep stages, gait speed and mobility, human pose estimation, and adherence.

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Biopharmaceutical visual inspection (e.g., injections) using deep learning by Bruce Lawler

From this research, learn how Bruce Lawler makes great strides in his goal to find the shortest path from data to impact.



Managing Director, MIT Machine Intelligence for Manufacturing and Operations (MIMO)

Bruce Lawler is a technology entrepreneur and an executive leader. He has developed several applications across platforms, such as mobile, SaaS, AI, and video distribution net...


Former Director of MIT Auto-ID lab, MIT

Brian Subirana has taught at MIT Sloan and the MIT School of Engineering and he is also on the faculty of Harvard University. His research centers on IoT and AI and focuses on...


Clarence J. Lebel Professor, Electrical Engineering and Computer Science

Duane Boning is affiliated with the MIT Microsystems Technology Laboratories and serves as associate director of machine learning and statistical methods for modeling and cont...



Upon successful completion of this program, MIT xPRO grants a certificate of completion to participants as well as 3.50 CEUs. This program is graded as a pass or fail; participants must receive 75% to pass and obtain the certificate of completion.

After the successful completion of the program, your verified digital certificate will be emailed to you, at no additional cost, in the name you used when registering for the program. All certificate images are for illustrative purposes only and may be subject to change at the discretion of MIT xPRO.


Didn't find what you were looking for? Write to us at or schedule a call with one of our Program Advisors or call us at +1 401 443 9591 (US) / + 44 189 236 2347 (UK) / +65 3129 7174 (SG).

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