AI for Health CareConcepts and Applications

Overview

Large Language Models (LLMs) and Generative Artificial Intelligence (AI) have captured the public imagination and have potential to drive significant change in healthcare. This course, AI for Health Care: Concepts and Applications, aims to unveil the core principles of AI, the capabilities of Large Language Models and Generative AI, and their profound implications for health care.

Under the tutelage of distinguished Harvard faculty, AI for Health Care: Concepts and Applications offers a conduit to transition from traditional health care paradigms to a more data-driven and AI-augmented approach. Adopting a “zero-to-AI” strategy, this course is crafted to equip health care professionals with foundational concepts, fine-tuned for health care applications. The curriculum navigates real-world health care dynamics, exploring AI’s potential to transform the doctor-patient relationship, and establishing a foundation for ethical AI deployment within healthcare.

Immerse yourself in a stimulating learning environment encompassing group discussions, active learning strategies, case studies, and master classes that probe into the genesis of AI, tackle implementation challenges, evaluate viable business models for AI in health care, and forecast the field’s evolution over the next five years. The program further cultivates a conducive networking atmosphere, promoting enduring collaboration among participants, which will act as a robust resource post-program.

Tackling AI Challenges in Health Care:

• Diagnosis AI’s prowess in harnessing large multimodal reservoirs of health data accelerates accurate diagnoses, thereby reducing misdiagnosis rates and easing clinician workload.
• Precision Medicine With AI as an ally, precision medicine transitions from generic treatment models to a more patient-centric approach, managing extensive data sets to formulate personalized treatment plans, thus enhancing patient care and resource allocation.
• Prediction Models Leveraging prediction models, clinicians can perform comparative analyses aiding in precise prognostics, which are instrumental in creating patient-specific care plans, mitigating risks, and optimizing resource utilization.To fully harness the potential of these formidable technologies and avert the potential harms, it’s pivotal for practitioners to be well-versed and proficient in the work that needs to be done before and after algorithm development. A proactive approach towards mitigating issues like algorithmic bias is crucial to ensure AI acts as a benefactor to the communities it serves. This course accentuates these dimensions, offering a public health lens to AI, and empowers students with the insight to catalyze meaningful transformations in patient care and organizational efficiency.

Objectives & Highlights

LEARNING OBJECTIVES 

• 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

Curriculum

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

Regulation

• 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 1.4 Continuing Education Units (CEUs) for this program, equivalent to 14 contact hours of education. Participants can apply these contact hours toward other professional education accrediting organizations.

All credits subject to final agenda.

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

Faculty

Current faculty, subject to change.

Heather Mattie, PhD, SM, MS

Program Director

February 6 – 9, 2024

Lecturer on Biostatistics
Department of Biostatistics
Harvard T.H. Chan School of Public Health

Trishan Panch, MD, MPH

Program Director

February 6 – 9, 2024

Instructor
Harvard T.H. Chan School of Public Health

Co-Founder
Wellframe

Rifat Atun, MBBS, MBA, FRCP, FRCGP, FFPH

Faculty

February 6 – 9, 2024

Professor of Global Health Systems
Harvard T.H. Chan School of Public Health

Junaid Bajwa

Faculty

February 6 – 9, 2024

Chief Medical Scientist
Microsoft Research

Nikhil Bhojwani

Faculty

February 6 – 9, 2024

Managing Partner
Recon Strategy

Felix Greaves

Faculty

February 6 – 9, 2024

Director
National Institute for Health and Care Excellence

Gianluca Mauro

Faculty

February 6 – 9, 2024

CEO
AI Academy

Javier Tordable

Faculty

February 6 – 9, 2024

Technical Director, CTO Office
Cloud
Google

Agenda

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