Empowering Patients with AI: A New Era in Care Delivery With: Emily Lewis
In this information-packed interview, Emily Lewis shares a compelling vision for the future of AI in patient care. Drawing from her work in machine learning and generative AI, Lewis highlights how these tools are not just enhancing clinician efficiency but reshaping how patients engage with their own health. She explains how multimodal AI applications, from avatars to audio interfaces, can personalize communication based on learning preferences. Lewis emphasizes AI’s potential to foster equitable partnerships between patients and clinicians. The conversation also explores patient education, self-care, and the structural hurdles of deploying AI across institutions. With attention to AI for patient engagement and AI-driven personalized care, Lewis offers a deeply insightful look into the systems and safeguards necessary for responsible AI implementation.
Episode Contents:
About the Guest
Emily Lewis is an AI thought leader, specializing in responsible AI implementation in patient-centered care. Learn more about her work.
Key Takeaways
- AI can personalize patient education based on individual learning styles
- AI nudging supports adherence and self-care between visits
- Equitable AI rollout demands robust governance and diverse, clean data
Transcript Summary
How does AI enhance patient engagement and personalization?
Emily Lewis explains that large language models (LLMs) can empower patients to become active partners in their healthcare by simplifying medical communication, enabling symptom tracking, and allowing patients to better articulate their experiences to clinicians. Tools like generative avatars and infographics cater to various learning styles.
What role does AI play between clinical visits?
AI can monitor patients remotely through audio and visual inputs, offer behavioral nudges, and assist in self-management. Lewis compares this to a smartwatch prompting movement, but with greater nuance and health specificity.
What are the data and bias concerns in AI deployment?
Lewis stresses the need for diverse, trusted sources and retrieval-augmented generation (RAG) techniques. She highlights the importance of transparency, human oversight, and disclaimers to mitigate misinformation.
How can health systems implement AI ethically and efficiently?
From embedding AI into existing workflows to leveraging clinical trials and user feedback, Lewis outlines how thoughtful design and change management make adoption smoother. She points to Stanford’s Emerging Applications Lab as a model.
What’s next for AI in personalized care?
Looking forward, Lewis envisions truly integrated systems that combine genetic, visual, and textual data to tailor therapies. AI will enable hyper-personalized treatments based on "patient-like-me" data, moving beyond one-size-fits-all care.
More Topics
- AI in Patient Care
- AI in the Healthcare Industry
- AI and Medical Innovation
- Healthcare Ethics and Policy
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About the Series
AI and Healthcare—with Mika Newton and Dr. Sanjay Juneja is an engaging interview series featuring world-renowned leaders shaping the intersection of artificial intelligence and medicine.
Dr. Sanjay Juneja, a hematologist and medical oncologist widely recognized as “TheOncDoc,” is a trailblazer in healthcare innovation and a rising authority on the transformative role of AI in medicine.
Mika Newton is an expert in healthcare data management, with a focus on data completeness and universality. Mika is on the editorial board of AI in Precision Oncology and is no stranger to bringing transformative technologies to market and fostering innovation.