Artificial Intelligence in Healthcare: Fundamentals and Applications

Harness the power of AI to elevate modern-day medical treatments

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Course Dates

STARTS ON

March 2, 2023

Course Duration

DURATION

7 weeks, online
5–7 hours per week

Course Fee

PROGRAM FEE

US$2,600 US$2,314 or get US$260 off with a referral

Course Information Flexible payment available

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Pre-requisites for the program: A basic understanding of artificial intelligence (AI), machine learning, and data science is ideal in order to make the most of this program.

Solutions for the Future of Healthcare With Artificial Intelligence (AI)

AI plays an increasingly important role in patient treatments. 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 AI and its applications 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.

AI and Healthcare in Numbers

Healthcare technology is at a critical stage, and people with AI expertise have the power to determine its future.

USD 208 Billion

The projected market size of AI-based global healthcare solutions by 2030

Source: Research and Markets

3x

The increase in the number of U.S. hospitals that have implemented AI since 2020

Source: World Economic Forum

46%

The percentage of life sciences leaders who report a shortage of people with AI-implementation skills in their organization

Source: World Economic Forum

Who Is This Program For?

  • 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 technology adoption 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 technologies 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

Program Highlights

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

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

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Acquire insights and examples from expert MIT faculty

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

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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-based technologies 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 multi-layer 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

Execute

  • 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

Module 1:

The Stages of Designing an AI Product

This module focuses on the four stages of designing an AI product. You learn how to identify the desired AI behavior, assess business and technical requirements, measure value, and generate a software development plan. In addition, you learn how to identify the long-term advantages of a product and analyze the three types of AI cancers and their impact.

Module 2:

The Fundamentals and Applications of Machine Learning

This module focuses on machine learning. You learn about the various algorithms, classifiers, decision trees (along with the advantages of training), and validation and testing sets. This module also shows you how to easily identify the best algorithms for different applications.

Module 3:

The Fundamentals and Applications of Deep Learning

This module examines deep learning. It covers neural networks in depth as well as their applications in drug discovery and cancer research. You learn to run implementations on convolutions and deep and recurrent neural network algorithms while gaining an understanding of Python and artificial neurons.

Module 4:

Designing Artificial Machines To Solve Healthcare Problems

Acquire a 360º approach to design with an understanding of superhuman targets, software methodologies, tool development, research, ethical responsibilities, and possible challenges to these applications. Learn about the COUHES/IRB approval processes, formulate crowdsourcing data strategies, and understand the extraordinary intelligence used in AI products and services.

Module 5:

The Peloton Framework

The peloton framework has helped facilitate applications such as modern ingestible robots. You gain a thorough understanding of this framework and develop your own ideas for ingestible robots to help solve healthcare problems.

Module 6:

Developments in Biomechatronics With AI

Deep dive into advanced prosthetics, proprioception, prosthetics, and exoskeletons. Understand their history, research, development, and current limitations, and learn how communication is crucial for the development of some of them.

Module 7:

Frontiers for AI in Healthcare

Identify the immediate challenges and possibilities in the field of healthcare technologies, including business applications, maintenance challenges, various sources of inspiration, the potentials of electromagnetic waves, and the use of RFID chips. In addition, you design and develop an AI product for healthcare.

Module 1:

The Stages of Designing an AI Product

This module focuses on the four stages of designing an AI product. You learn how to identify the desired AI behavior, assess business and technical requirements, measure value, and generate a software development plan. In addition, you learn how to identify the long-term advantages of a product and analyze the three types of AI cancers and their impact.

Module 5:

The Peloton Framework

The peloton framework has helped facilitate applications such as modern ingestible robots. You gain a thorough understanding of this framework and develop your own ideas for ingestible robots to help solve healthcare problems.

Module 2:

The Fundamentals and Applications of Machine Learning

This module focuses on machine learning. You learn about the various algorithms, classifiers, decision trees (along with the advantages of training), and validation and testing sets. This module also shows you how to easily identify the best algorithms for different applications.

Module 6:

Developments in Biomechatronics With AI

Deep dive into advanced prosthetics, proprioception, prosthetics, and exoskeletons. Understand their history, research, development, and current limitations, and learn how communication is crucial for the development of some of them.

Module 3:

The Fundamentals and Applications of Deep Learning

This module examines deep learning. It covers neural networks in depth as well as their applications in drug discovery and cancer research. You learn to run implementations on convolutions and deep and recurrent neural network algorithms while gaining an understanding of Python and artificial neurons.

Module 7:

Frontiers for AI in Healthcare

Identify the immediate challenges and possibilities in the field of healthcare technologies, including business applications, maintenance challenges, various sources of inspiration, the potentials of electromagnetic waves, and the use of RFID chips. In addition, you design and develop an AI product for healthcare.

Module 4:

Designing Artificial Machines To Solve Healthcare Problems

Acquire a 360º approach to design with an understanding of superhuman targets, software methodologies, tool development, research, ethical responsibilities, and possible challenges to these applications. Learn about the COUHES/IRB approval processes, formulate crowdsourcing data strategies, and understand the extraordinary intelligence used in AI products and services.

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 diagnoses 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.

Faculty

Profile image of the program faculty Bruce Lawler

Bruce Lawler

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 networks... More info

Profile image of the program faculty Brian Subirana

Brian Subirana

Former Director of MIT Auto-ID lab, MIT

Brian Subirana has taught at MIT Sloan and the MIT School of Engineering and is also the former director of the MIT Auto-ID lab and director of the MIT and Accenture Convergence Initiative for Industry and Technology... More info

Profile image of the program faculty Duane Boning

Duane Boning

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 control of variation in manufacturing... More info

Certificate

Certificate

Certificate

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

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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.

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