"Generative AI is reshaping clinical decision-making and patient care. As clinicians, we must understand its potential and challenges to integrate it responsibly. This course provides the foundation to do just that."
Dr. Andres Jimenez MD
Course Units:
Use AI in Healthcare, or Lead AI in Healthcare....
Acquire the knowledge and skills necessary to reshape your role in the ongoing transformation of healthcare!
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Preview Unit #1
Generative AI For Clinicians: Foundational Understanding of Gen AI
Generative AI is transforming healthcare by creating new data rather than just analyzing existing information. This sub-topic explores its foundational principles, distinguishing it from traditional AI models used for classification and prediction. It covers the technical infrastructure required for AI deployment, including GPUs, TPUs, and energy-intensive computational processes. The training of Large Language Models (LLMs) is examined, highlighting data requirements, backpropagation, and fine-tuning. The sub-topic also traces the historical evolution of Generative AI, with OpenAI playing a key role in its rapid advancement. Practical applications in healthcare, such as clinical documentation and decision support, are discussed alongside ethical considerations like bias, privacy, and regulatory compliance. As AI adoption grows, balancing innovation with sustainability and ethical responsibility remains crucial for healthcare professionals.
Lecture (6 min)
Textbook Chapter
We encourage you to watch the lecture above first, then read through the chapter text before attempting the Case Scenario and Quiz below.
Case Scenario
Quiz
Educational Objectives
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Explain the fundamental principles of Generative AI, including its core functionality and how it differs from traditional AI models in healthcare applications.
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Analyze how Generative AI can enhance clinical workflows, including documentation automation, clinical decision support, and patient interactions.
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Evaluate the hardware and computational requirements necessary for training and deploying Generative AI models in a healthcare setting.
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Assess the impact of energy consumption and sustainability concerns associated with large AI models, and propose strategies to optimize efficiency in healthcare applications.
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Describe the training process of large language models (LLMs), including backpropagation, gradient descent, and fine-tuning for specialized medical applications.
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Compare the cost, time, and infrastructure considerations between developing proprietary AI models and leveraging pre-trained or open-source alternatives in clinical settings.
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Investigate the historical evolution of Generative AI, including OpenAI’s role in advancing the field and the competitive landscape of AI research and development.
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Synthesize emerging trends in Generative AI and predict future developments that may influence medical research, diagnostics, and patient care.
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Formulate strategies for integrating Generative AI into a healthcare institution while addressing ethical considerations, patient privacy concerns, and legal constraints.