AI for Health CareConcepts and Applications

Certificate of Specialization:Business Applications for AI in Health Care
  • Online
  • February 69, 2024
  • $2,600


The health care industry is in a productivity crisis. For the last half century, technology, agriculture and manufacturing corporations have outpaced health care’s innovation. However, today artificial intelligence offers a tool that can help medical doctors, administrators and other stakeholders break out of this crisis. 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

This could lead to Artificial Intelligence (AI) and cognitive computing empowering patients, transforming the practice of medicine, and saving the health care industry over $150 billion by 2025.1

This growing role of AI in health care organizations can harness data already being collected to inform and improve clinicians’ decisions and service to patients. Hospital and system leaders can make informed systems decisions to improve process and performance Unfortunately, many leaders responsible for making these decisions don’t know where to begin in applying AI for the best outcomes. This is where AI for Health Care: Concepts and Applications comes in.

For health care professionals, this program will help you think like a data scientist. It takes a “zero-to-AI” approach, using Harvard faculty to introduce AI beginners to key foundational concepts. This course outlines health care-specific subtleties that arise and places AI in the larger health care systems context. Find out how AI can change the relationship between doctor and patient and learn key principles for implementing ethical AI for progress with AI for Health Care: Concepts and Applications.

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.

Solving Health Care Challenges with AI

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


  • Improve understanding about the fundamental concepts of AI
    Appreciate the significance of some of the advances that have taken place in AI
  • Develop a more informed view on managing AI projects
  • Have a more nuanced view of bias, fairness, and ethics in modern AI
  • Anticipate the upcoming developments in AI with appropriate timelines
  • Accurately assess the current state of the technology for each of the major subfields of AI
  • Understand the main components of the health care industry (payer, provider, pharma) and be able to think through the role of AI in each of these


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

Harvard T.H. Chan School of Public Health will grant Continuing Education Units (CEUs) for this program. Specific credit counts will be published when available; please check back for updated information.

All credits subject to final agenda.

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


Current faculty, subject to change.

Heather Mattie, PhD, SM, MS

Program Director

February 69, 2024
Lecturer on Biostatistics
Department of Biostatistics
Harvard T.H. Chan School of Public Health

Trishan Panch, MD, MPH

Program Director

February 69, 2024
Harvard T.H. Chan School of Public Health



February 6 – 9, 2024

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This agenda is subject to change. All times listed are in Eastern Time (ET).

Tuesday, February 6, 2024
9:00–10:00 am Introduction
10:00–10:15 am Break
10:15–11:15 am Zero to AI
11:15–11:30 am Break
11:30 am–12:30 pm Group Projects
12:30–1:30 pm Masterclass - Innovation, Venture Capital, and AI
Wednesday, February 7, 2024
9:00–10:00 am AI in Healthcare Organizations
10:00–10:15 am Break
10:15–11:15 am How to Make AI
11:15–11:30 am Break
11:30 am–12:30 pm Group Projects
12:30–1:30 pm Masterclass - Commercializing AI in Healthcare
Thursday, February 8, 2024
9:00–10:00 am AI and Health Systems
10:00–10:15 am Break
10:15–11:15 am Winning Business Models for AI in Healthcare
11:15–11:30 am Break
11:30 am–12:30 pm Group Projects
12:30–1:30 pm Masterclass - Leading Data Science Teams
Friday, February 9, 2024
9:00–10:00 am Ethics and Algorithmic Bias
10:00–10:15 am Break
10:15–11:15 am AI Safety and the Future of Regulation
11:15–11:30 am Break
11:30 am–1:30 pm Final Group Project Presentations

Who Should Participate

This online 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