Discover what it takes to move powerful AI technologies from development into real-world healthcare settings in this timely two-day course. With the guidance of renowned healthcare AI expert Professor Regina Barzilay, you’ll learn how to evaluate AI tools for healthcare, navigate implementation challenges, and make informed decisions about when—and how—to put AI to work in a clinical setting.
THIS program MAY BE TAKEN AS A STANDALONE PROGRAM OR AS PART OF THE PROFESSIONAL CERTIFICATE PROGRAM IN MACHINE LEARNING & ARTIFICIAL INTELLIGENCE.
From enhancing the detection of certain diseases to developing new drugs, personalizing treatment options, and improving clinical decision-making, AI is opening transformative possibilities across healthcare. However, developing these powerful AI tools is only part of the challenge. Getting them successfully into clinical practice, where they can make a tangible difference in patients’ lives, can be much harder.
As AI adoption accelerates, healthcare professionals face increasingly complex decisions: Which technologies should their clinics adopt? How should AI fit into existing clinical workflows? What happens when a clinician and an AI tool disagree? And how do you know whether a technology continues to perform as intended once it reaches the real world?
In this timely on-campus course, you’ll gain a practical framework for navigating these questions. Over two intensive days, through dynamic lectures, practical exercises, and case study analysis, you’ll develop a high-level understanding of how modern AI systems learn, make predictions, and work with different types of healthcare data. You’ll then explore the clinical, organizational, economic, legal, and regulatory factors that can determine whether a promising technology ultimately reaches—and benefits—patients.
The course is led by Regina Barzilay, MIT School of Engineering Distinguished Professor for AI and Health and Faculty Lead at the MIT Jameel Clinic. A pioneer in applying AI to healthcare, Professor Barzilay and her team have developed AI models that can enhance the detection of breast and lung cancers. Drawing on her expertise, you’ll examine real-world AI applications in healthcare and develop a stronger understanding of how to evaluate technologies, monitor their performance, manage potential risks, and determine where and how they can be effectively implemented. You’ll also hear from clinicians at Massachusetts General Hospital and other hospitals, who will share their experiences adopting AI tools, navigating implementation challenges, and determining where these technologies can add value.
Beyond clinical implementation, the course addresses the wider broader considerations that can shape how AI is adopted and used in healthcare. You’ll explore how clinicians can communicate with patients about AI-informed care; how organizations can account for bias, safety, reimbursement, and regulatory requirements; and how evolving questions of legal responsibility may affect the use of AI in clinical settings. Harvard Law School Professor I. Glenn Cohen, one of the world’s leading experts on bioethics and the law, will bring a legal perspective to these emerging questions.
Ultimately, this course is about moving AI from technological possibility to meaningful clinical impact. You’ll leave with a practical framework for evaluating AI technologies, weighing their benefits and risks, and making more informed decisions about how they can be implemented safely and effectively in your organization.
MIT Professional Education’s Transforming Healthcare with AI: Strategy, Implementation, and Clinical Impact is a two-day, on-campus course designed for professionals seeking to better understand how AI technologies can be evaluated, adopted, and implemented in healthcare settings.
You can apply for MIT Professional Education’s Transforming Healthcare with AI course at the top of this page by selecting the REGISTER NOW button.
The course fee for Transforming Healthcare with AI is $2,750.
No, there are no prerequisites. The course takes a high-level approach to how AI works, with an emphasis on evaluating, implementing, and using AI technologies in real-world healthcare environments.
You’ll develop a practical understanding of how modern AI works and explore what it takes to move AI technologies from development into real-world healthcare settings. Through case studies, lectures, and exercises, you’ll learn how to evaluate AI tools, navigate implementation and adoption challenges, monitor performance, maintain appropriate clinical oversight, communicate with patients, and account for regulatory, legal, ethical, economic, and organizational considerations.
No. The course is not focused on teaching participants to build AI models or develop AI applications. Instead, you’ll gain enough understanding of how modern AI works to critically evaluate technologies, understand their capabilities and limitations, and make more informed decisions about their adoption and use in healthcare.
This course can help you build the knowledge and perspective to navigate AI adoption more effectively within healthcare. Whether you are responsible for selecting technologies, implementing them across an organization, incorporating them into clinical workflows, developing healthcare AI solutions, or evaluating opportunities in the field, you’ll gain insights you can apply to more informed decision-making around AI.
Over two high-impact days, alongside accomplished peers across AI, medicine, and law, you will:
- Build a high-level understanding of how AI systems work, including model training, inference, and the use of different forms of healthcare data.
- Evaluate AI technologies based on their capabilities, limitations, supporting evidence, potential risks, and fit for your clinic or organization.
- Identify the factors that impact whether an AI technology successfully moves from development into clinical practice.
- Analyze successful and unsuccessful examples of healthcare AI implementation to understand what enables—or prevents—effective clinical adoption.
- Assess approaches for monitoring AI after deployment—and learn to recognize signs that a tool may not be performing as intended.
- Examine how clinicians can balance the use of AI for patient assessments with their own judgment, particularly when AI makes predictions that are challenging for humans to independently verify.
- Explore approaches for communicating AI-informed recommendations, risk, and uncertainty to patients.
- Review the regulatory landscape for healthcare AI, including what FDA approval does and does not establish about a technology.
- Unpack evolving questions around legal responsibility, accountability, ethics, reimbursement, and organizational decision-making.
- Acquire a practical framework for weighing the benefits, risks, and implementation requirements of AI technologies in your own clinic, hospital, or healthcare organization.
This course is designed for professionals seeking to understand the myriad ways AI is reshaping healthcare—and the practical, clinical, organizational, legal, and ethical considerations involved in moving AI technologies into real-world use.
The course is particularly relevant for:
- Clinicians and physicians responsible for incorporating AI tools into their clinical workflows, monitoring how they’re performing, delivering care, and communicating with patients.
- Hospital administrators and healthcare leaders responsible for evaluating AI technologies, selecting vendors, allocating resources, integrating new tools into existing workflows, and determining where AI can deliver meaningful clinical or operational value.
- AI and technology developers seeking to better understand why technically strong AI tools sometimes fail to reach patients—and how clinical evidence, workflows, reimbursement, regulation, and organizational realities influence adoption.
- Healthcare innovators, strategists, and transformation leaders responsible for identifying AI opportunities and guiding implementation across healthcare organizations.
- Investors and other healthcare decision-makers looking to understand which AI technologies are positioned for real-world adoption and the clinical, regulatory, and economic factors that can influence their success.
No coding prerequisite. Participants should have professional responsibility for evaluating, developing, governing, purchasing, or implementing AI-enabled healthcare solutions.