Madhvi Sharma

AI in healthcare has crossed a tipping point. What once began as simple chatbots answering patient FAQs has evolved into autonomous AI agents that coordinate care, close treatment gaps, match patients to clinical trials, and synthesize years of medical history in seconds even before a clinician ever enters the room.
This talk cuts through the noise and grounds the audience in what is actually happening at the frontier of AI in healthcare today. Drawing on first-hand experience building and shipping production AI agents for population health, we will demystify how GenAI and agentic systems work, explore real-world deployments across care coordination, predictive analytics, and health data intelligence, and offer an honest look at where the technology still falls short.
Beyond the technology, this talk maps the rapidly expanding career landscape that AI in healthcare is creating,  from AI engineers and health data scientists to clinical informatics specialists and responsible AI leads. Attendees will learn what skills matter most, how domain knowledge and engineering together outpace pure machine learning expertise, and how to take their first concrete steps into this field.
The session will include audience interaction and a thought-provoking discussion on where humans must always remain in the loop, connecting audience instincts to real design principles shaping clinical AI today.
Attendees will leave with a clear mental model of agentic AI in healthcare, exposure to four real-world use cases drawn from current industry deployments, a practical career roadmap, and a curated resource list to get started immediately.

Biography:

I am a Senior Engineering Manager at Oracle Health Data Intelligence, where I lead the development of generative AI solutions that are transforming healthcare data intelligence. Based in Seattle, I focus on leveraging advanced AI technologies to enable smarter, data-driven healthcare decisions and improve patient outcomes. Additionally, I play a key role in delivering Oracle’s next-generation HDI AI agents platform, driving innovation in intelligent automation and AI-powered healthcare solutions.
I’m passionate about fostering diversity and inclusion in technology and committed to mentoring the next generation of women in STEM.

I have been at the forefront of applying large language models to real-world healthcare challenges. I led the development of Oracle's Care Coordination Automation product, which dramatically reduced care manager preparation time from 35 minutes to just 5, and is currently driving an AI agents platform designed to deliver population health intelligence at scale. My technical roots are in cloud infrastructure, spanning Oracle Cloud Infrastructure, IBM Cloud, and multicloud networking that give me a uniquely deep foundation for engineering leadership in AI.

Isaac Dasari

Modern data-driven systems demand platforms capable of processing and managing data at petabyte scale while supporting real-time analytics and machine learning workloads. This talk explores the design principles and architectural patterns behind building scalable, reliable, and high-performance data systems.

We will examine key challenges in distributed data processing, including data ingestion, storage optimization, fault tolerance, and consistency at scale. The session will also highlight how modern data platforms enable advanced analytics and power ML and generative AI use cases.

Drawing from real-world industry experience, this talk provides practical insights into designing robust data pipelines, selecting appropriate technologies, and balancing trade-offs between scalability, latency, and cost. Attendees will gain a deeper understanding of how to architect data systems that serve as the foundation for next-generation intelligent applications.

Biography:

Isaac Dasari is a Data Engineer at Amazon specializing in large-scale distributed data systems and petabyte-scale data processing. His work focuses on building scalable and reliable data platforms that enable advanced analytics and machine learning across complex, high-volume environments.

With over a decade of experience across industry and consulting, he brings deep expertise in big data technologies, distributed computing, and emerging areas such as generative AI. He actively contributes to the technical community through writing and knowledge sharing on data engineering and AI topics.

He is an IEEE Senior Member and serves as Treasurer of the IEEE Computer Society chapter, where he supports professional engagement and technical initiatives within the community. Based in the Seattle area, he continues to explore innovations in data infrastructure and intelligent data-driven systems.