AI in Health Care: From Strategies to Implementation

The two-month online AI in Health Care: From Strategies to Implementation program is designed to equip health care leaders with the knowledge and strategies to design, pitch and implement AI-driven solutions and bring about transformative change in their organizations. The program’s curriculum prepares leaders to make informed decisions and envision strategic AI implementation in the unique cultural, economic and regulatory context of health care.

Guided by expert Harvard Medical School faculty and guest speakers, develop a deep understanding of real-world data, digital medicine and AI applications and learn to translate AI concepts into actionable success. Through case studies, real-world examples and a capstone project, you will gain the skills needed to frame a machine learning solution for a health care challenge, evaluate and select appropriate AI models and implement AI strategies from end to end. Explain the fundamentals of modern methods in AI, including the role of data and computing in building successful AI applications.

Evaluate existing AI systems in health care to understand their strengths and weaknesses.
Identify new opportunities for AI in health care to address unmet needs.
Assess the ethical implications and potential biases of AI technologies in health care settings.

The capstone project is a core component of your readiness to implement AI in a real-world health care setting. The project will require you to ideate and pitch a new AI-first health care solution that addresses an unmet need. Your project will span the course of the program, enabling you to incorporate the concepts and frameworks you learn in each module to develop your pitch.

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Module 1: The History and Foundations of AI

AI has come a long way in a short amount of time. Examine its evolution, differentiate between supervised and self-supervised learning and explore emerging AI applications in health care.

Module 2: A Framework for the AI Development Pipeline

Creating and implementing an AI health care solution involves a careful balance of training, validation and deployment. Explore each stage of the AI development pipeline and formulate an idea for an AI-driven health care solution that you will work on throughout the program to fulfill the capstone requirement.

Module 3: From the Lab to the Real World

Leveraging the power of AI in a health care setting can be challenging. Examine the complexities of developing successful AI-driven products for the health care industry and identify the factors that must be considered to create a successful product.

Module 4: Transparency, Reproducibility and Generalizability in AI

Risk prediction models assess the likelihood of a given outcome. Discover common evaluation measures used with these models and the level of certainty required for clinical decision-making.

Module 5: The Potential for Bias and Harm in AI

The AI development pipeline offers a host of benefits, but it also introduces potential ethical concerns. Examine each pipeline stage through the lens of ethics and equip yourself with the knowledge to identify and avoid ethically problematic choices and practices throughout the development process.

Module 6: AI for Startups

AI implementation carries a unique set of considerations for startups. Guided by a leader in the AI startup world, learn to transform your idea for an AI-first health care solution into a compelling story and viable investor pitch.

Module 7: AI for Wearable Data

Person-generated health data (PGHD) gathered by wearable devices and machine learning algorithms offer a wide range of new health care possibilities. Explore the advantages and disadvantages of leveraging machine learning to collect and assess wearable data and examine the role of community benchmarks and transparency in developing machine learning methods.

Module 8: Live Session—Capstone Presentations

A viable pitch is instrumental to securing funding for an AI-first health care solution. Join the members of your cohort as some of them share pitches for their solutions and examine what makes an effective pitch and why.