"Innovation in healthcare isn’t just about technology—it’s about responsibility. As clinicians, we must balance rapid AI development with compliance, ethics, and real-world impact. The future of AI-driven medicine is in our hands."
Dr. Andres Jimenez MD
Course Units:
Unit #7
Generative AI For Clinicians: Building an MVP & IP Strategy
This unit explores the essential steps in developing AI-driven healthcare solutions, from pre-seed funding to building a minimally viable product (MVP) while navigating regulatory challenges. Clinicians will learn how to balance rapid innovation with HIPAA compliance, FDA regulations, and intellectual property strategies to safeguard proprietary advancements. The lecture emphasizes the importance of avoiding overengineering, managing technical debt, and ensuring early user feedback to refine AI applications for clinical use. By understanding these key principles, healthcare professionals can actively shape AI-driven innovations, ensuring they enhance patient care while maintaining security, privacy, and regulatory integrity.
Lecture (9 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 #7
Quiz #7
Educational Objectives
-
Analyze the role of minimally viable products (MVPs) in healthcare innovation and assess their impact on stakeholders, including clinicians, patients, and investors.
-
Evaluate regulatory requirements such as HIPAA and FDA guidelines for Software as a Medical Device (SaMD) and determine their implications for healthcare MVP development.
-
Assess the trade-offs between speed and compliance in MVP development and formulate strategies to navigate regulatory constraints effectively.
-
Identify and mitigate sources of technical debt in healthcare AI innovations to ensure long-term scalability and maintainability.
-
Compare the risks of overengineering versus underdeveloped MVPs and propose an optimal strategy for iterative healthcare technology deployment.
-
Differentiate between patents and trade secrets as intellectual property (IP) strategies and determine the best approach for protecting healthcare AI innovations.
-
Critically examine real-world case studies of successful MVPs and extract key lessons applicable to their own healthcare innovation projects.
-
Synthesize a step-by-step plan for developing a compliant, functional, and scalable healthcare MVP while balancing innovation and regulatory oversight.
-
Formulate an intellectual property strategy that aligns with the commercialization goals of a healthcare AI product.
-
Apply principles from case studies to develop an actionable framework for launching and protecting a new healthcare technology innovation.