I had the distinct pleasure of joining the vibrant community at WPI Business School for a conversation that took us to the crossroads of technology and healthcare. It was an opportunity to dive into how engineering and business principles are increasingly interwoven with clinical practice.
As a Medical Scientist Training Fellow at the University of Michigan, my work orbits around integrating Artificial Intelligence and Machine Learning (AI/ML) tools in medical practice. My talk, “Machine Learning for Healthcare: Lessons From Across The Healthcare ML Lifecycle,” aimed to shed light on the technical underpinnings and the broad, non-technical implications of these advancements.
The WPI Business School crafted an engaging platform with their inaugural Business Week, filled with diverse insights, from leadership lessons to hands-on sessions like “Elevate Your LinkedIn Game.” It was within this rich tapestry of ideas that I presented my perspectives on AI/ML in medicine.
During my talk, we navigated the nuances of developing and implementing AI/ML-based models, specifically risk stratification models, which physicians use to estimate a patient’s risk of developing a particular condition or disease. These tools have existed for a long time; however, recent advances in AI/ML enable developers to make tools with greater accuracy and efficiency, potentially transforming patient outcomes. However, the journey from an initial clinical question to a model implemented into clinical workflows is fraught with challenges, including data representation, prospective performance degradation, and updating models in use by physicians.
I was thrilled to see a curious and engaged audience, with participation that demonstrated WPI Business School’s unique role in this space as a polytechnic institution. It’s discussions like these that are critical for developing AI/ML tools that are not only innovative but also responsible and aligned with societal needs.
As a token of my appreciation for this intellectual exchange, I’m sharing my slides from the talk. I hope they serve as a resource and a spark for further conversation.
My key takeaway from this experience? Whether you’re a developer, a business strategist, or a medical professional, staying informed and involved in the conversation about AI/ML in medicine is vital. It’s at the intersection of these diverse perspectives that the most meaningful innovations are born.
I extend my heartfelt thanks to Dr. Michael Dohan and WPI Business School for hosting me and orchestrating such an insightful series of events. The future of business and STEM is a collaborative one, and I look forward to the continued dialogue that events like these foster.
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