Applied Artificial Intelligence for Health Care

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Mattie, Trishan, and team—together with an amazing faculty lineup—willingly shared their knowledge and frank perspectives. Their curation of content and authenticity in active engagement to guide our comprehension is admirable. Also, the global group of participants with their wide range of expertise (from seasoned investors to senior clinicians) stimulated informative viewpoints that is well appreciated from an amateur like me.
  • —Hui Leck
  • Manager, Agency of Care Effectiveness, Ministry of Health Singapore

Online Program Overview

Artificial Intelligence (AI) and cognitive computing are projected to empower patients, transform the practice of medicine and save the health care industry over $150 billion by 2025.1

It is estimated that if implemented correctly, AI could improve health outcomes by up to 40 percent and reduce treatment costs up to 50 percent by improving diagnosis, increasing access to care and enabling precision medicine.2

There is no denying that AI is the future of health care, however AI technologies won’t implement themselves and require considerable translational expertise to deliver on their promise. Although health care professionals have firsthand experience with health and organizational issues, they typically do not have a detailed understanding of the AI technology needed to address them. Many health care executives know implementing AI will improve their organization and keep them competitive, but find the technology and scale of data intimidating. On the other hand, data scientists and technology professionals are not familiar with the intricacies in health care that will inform the development of AI in this field.

This program is everything you want to know about AI in health care, but are afraid to ask. For health care professionals, it will help you to think like a data scientist. For technology professionals, you will learn the nuances of health care that are central to effectively developing AI. This course will bridge the two parties, opening the communication and knowledge between health care leaders and data scientists.

You will learn from the leaders in health care AI, including prominent Harvard faculty and industry experts at many of the world’s top technology companies. Course faculty will use group discussions, active learning strategies, case studies, and master classes to explore such topics as AI creation, potential implementation challenges, business models for AI in health care, and the future of the field over the next 5 years. Additionally this course is designed to encourage networking among participants, fostering a long-term support system you can lean on after the program concludes.

During this program you will learn:

  • What you need to know to understand how AI can support your organization’s strategy and serve your patients
  • State of the art in AI from business leaders and practitioners
  • Scope of applications of AI across all elements of health care: payer, provider, life sciences, consumer health, and genomics
  • How to build a business case for AI in health care
  • How to consider issues of equity to ensure that the promise of AI is realized for all patients

Solving Health Care Challenges with AI

There is a colossal amount of data being collected in the health care world – and much of it has not been fully utilized to best support care due to its complexity and scale. With the growing role of AI in health care organizations, it can be used to harness this data to help clinicians and leaders make expedient, informed, and personalized decisions when treating patients. The health care field is just starting to understand the depth and range of improvements that AI can make – and this is only the beginning of its incredible impact on improving the public’s health.

There are many ways AI is helping overcome long-standing health care challenges:

  • Diagnosis: AI is able to process complex images, like CT scans, along with health records to make an accurate diagnosis in near real-time. A 2019 study found that AI correctly diagnosed diseases 87% of the time when reviewing medical imaging, compared to 86% by health care professionals.3 By combining the AI skill set with that of clinicians, the rate of misdiagnoses goes down, also helping reduce physician overload and in turn improving productivity.
  • Precision Medicine: AI has played a substantial role in the emerging field of precision medicine, which defies the one-size-fits-all approach to health care. Precision medicine is heavily based in data, taking into consideration a patient’s behaviors, environment, genome, and medical history to develop a more personalized treatment plan. AI helps manage the massive data sets used to inform this approach, allowing clinicians to better understand the patient, provide more specialized care, and more efficiently target resources. This has ultimately been proven to better treat disease and improve patient care.
  • Prediction Models: By using prediction models, clinicians can identify how a patient compares to others with a similar diagnosis, helping calculate potential outcomes. For example, it can help when determining if a patient is at higher risk of death, may need extra support to prevent complications, or can be released from the hospital shortly.

However, evidence also shows AI is also involved with important risks such as algorithmic bias. As individuals who develop AI carry implicit bias and health care systems exist in societies with prejudice, these biases end up being reflected in algorithms. It is crucial to think proactively about bias when developing and implementing AI by taking strategic actions to minimize the risk of algorithmic bias to ensure AI is helping – not further harming – the communities it serves.

1 Artificial Intelligence in Healthcare Takes Precision Medicine to the Next Level, Frost & Sullivan

From $600 M to $6 Billion, Artificial Intelligence Systems Poised for Dramatic Market Expansion in Healthcare, Frost & Sullivan

3 A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis, The Lancet Digital Health

Objectives & Highlights

Learning Objectives

By the end of this course, participants will be able to:

  • Improve understanding about the fundamental concepts of AI
  • Appreciate the significance of some of the advances that have taken place in AI
  • Understand that AI may have a role in multiple components of the healthcare industry (payer, provider, pharma, consumer health)
  • Develop a more informed view on managing AI projects
  • Appreciate some of the weaknesses of modern AI techniques from a business perspective
  • Have a more nuanced view of bias, fairness and ethics in modern AI
  • Anticipate the upcoming developments in AI with appropriate timelines
  • See AI in the wider context of business strategy and quality improvement
  • Improve ability to make key AI related decisions such as build/buy/borrow and consider factors such as scalability and sustainability
  • Appreciate how AI specific business models are created.
  • See healthcare AI in its global context
  • Understand how difficult regulating AI can be in the healthcare space

Program Highlights

  • Learn from industry leaders about future trends in health care and the potential impact of AI
  • Attain skills that are immediately applicable once you return to your organization
  • Develop a network of business and clinical leaders from across the world
  • Benefit from a community of innovators to help you implement what you learn in the course


Introduction to AI: Definitions and Terminology

  • Introduction to AI
  • How AI is transforming society and daily life
  • How healthcare needs to benefit from AI too

Foundational Concepts and Current State of the Art

  • Key Concepts in AI
  • What is possible today using AI
  • What will be possible in the near future

Zero to AI

  • Further Foundation Concepts in AI
  • How AI works in practice

Masterclass: Emily Melton, AI in Clinical Medicine

  • How AI can revolutionize clinical medicine

Digital Health and AI

  • How AI is supercharging digital health
  • How to build robust AI-enabled digital health systems

AI in the Life Sciences

  • How biopharma are being impacted by AI
  • How to build, lead, and develop data science teams

Masterclass: Tich Changamire, How to Create Artificial Intelligence

  • The key ingredients and high level recipes required to create artificial intelligence
  • Common pitfalls and considerations

When AI Goes Wrong: Algorithmic Bias

  • Types of Bias
  • How algorithms can perpetuate or exacerbate existing biases in datasets and society
  • The need to build bias-mitigating or bias-free systems

Collaborative and Open Data Science

  • The difficulty of generalizing AI models
  • The importance of collaboration and open datasets to build robust AI

Masterclass: Javier Tordable, AI and Strategy

  • AI from the perspective of the C-suite
  • How AI enabled businesses have the edge

Business Models for Healthcare AI

  • How the innovations in AI and subtleties of healthcare combine to produce unique business models for healthcare AI
  • Practical exercises to think through some of the novel business models for healthcare AI

Masterclass: Lisa Maki, AI Ethics

  • Ethical ramifications of AI specific to healthcare
  • Difficulties and uncertainties

AI and Global Health Systems

  • How health systems across the world differ in culture, funding, scope and delivery
  • How these differences lead to different opportunities for AI


  • The importance and difficulties of regulating AI in healthcare
  • How the traditional regulatory models don’t work, and novel work being done in this area

Credits and Logistics

Continuing Education Credit

All participants will receive a Certificate of Participation upon completion of the program.

Who Should Participate

This executive education program is designed for senior managers and executives who are responsible for developing and implementing AI strategy in their organizations and are looking to understand AI, its current state of the art, and future.

Participants will come from a range of organizational functions including health care delivery, health care technology, primary care systems, payers, and governments. Some titles represented in the program will include:

  • Chief Executive Officer
  • Chief Information Officer
  • Chief Innovation Officer
  • Chief Medical Informatics Officer
  • Chief Medical Officer
  • Clinician
  • Data Scientist
  • Director
  • Engineer
  • Innovation Specialist
  • Finance Professional
  • Product Manager
  • Project Manager
  • Venture Capital Investor