Responsible AI for Health CareConcepts and Applications

Overview

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

Under the tutelage of distinguished Harvard faculty, Responsible AI for Health Care: Concepts and Applications offers a conduit to transition from traditional health care paradigms to a more data-driven and ethically sound AI-augmented approach. Adopting a “zero-to-AI” strategy, this course is crafted to equip health care professionals with foundational concepts, fine-tuned for responsible AI applications in health care. The curriculum navigates real-world health care dynamics, exploring AI’s potential to transform the doctor-patient relationship, while 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 delve into the genesis of AI, tackle implementation challenges, evaluate viable business models for responsible 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. 

Objectives & Highlights

Learning Objectives

• Understand the first principles of AI ethics and safety in health care
  Grasp the core ethical principles and safety measures essential for AI in health care. 
• Discuss how large language models can and have been applied ethically in health care
  Explore real-world applications of LLMs in health care, emphasizing ethical use cases, patient privacy, and mitigation of biases. 
• Understand prompt engineering, tuning, and optimizing large language models with ethical considerations
  Learn techniques for improving the performance of AI through effective prompt engineering and model tuning. 
• Describe the process of implementing AI projects in large health care organizations
  Outline the steps for integrating AI into health care settings, focusing on ethical deployment, stakeholder engagement, and compliance with regulatory frameworks. 
• Identify future challenges and opportunities in AI with a focus on ethics and safety
  Anticipate emerging issues and potential advancements in AI, with a focus on ensuring ethical practices and addressing societal impacts. 

Program Highlights

• Program faculty will include industry experts from the world’s top technology companies
• 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
• Interactive program format including case studies, group discussions, active learning strategies, and master classes
• 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

Curriculum

Tackling AI Challenges in Health Care: 

Diagnosis
Responsible AI harnesses large multimodal reservoirs of health data to accelerate accurate diagnoses, thereby reducing misdiagnosis rates and easing clinician workload while ensuring ethical data use and patient privacy. 

Precision Medicine
With responsible 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. This enhances patient care and resource allocation while adhering to ethical standards and minimizing biases. 

Prediction Models
Leveraging ethical prediction models, clinicians can perform comparative analyses aiding in precise prognostics. These models are instrumental in creating patient-specific care plans, mitigating risks, and optimizing resource utilization while ensuring transparency and accountability. 

To fully harness the potential of these formidable technologies and avert 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 through responsible AI practices. 

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 4 – 7, 2025

Lecturer on Biostatistics, Co-Director, Health Data Science Master’s Program, Director of EDIB Programs
Department of Biostatistics
Harvard T.H. Chan School of Public Health

Trishan Panch, MD, MPH

Program Director

February 4 – 7, 2025

Instructor
Harvard T.H. Chan School of Public Health

Santiago Romero-Brufau, MD, PhD

Program Director

February 4 – 7, 2025

Director of AI and Systems Engineering
Department of Otolaryngology — Head and Neck Surgery
Mayo Clinic

Adjunct Assistant Professor
Department of Biostatistics
Harvard T.H. Chan School of Public Health

John Brownstein, PhD

Faculty

February 4 – 7, 2025

Chief Innovation Officer
Adebona Account

Professor of Pediatrics
Harvard Medical School

Emily Lindemer, PhD

Faculty

February 4 – 7, 2025

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