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Free Online Webinar:

We are very excited to organize 1 day extensive Conference on AI in Healthcare May 28th, 2026. As we get closer to the conference, we want to invite you to participate in Global AI in Healthcare Virtual Conference Webinar - Online Warm-Up on May 28th Thursday (11.00AM - 11.30AM) PST. We will feature with speakers working in AI space.

Please start registering by entering your name and email address to attend Webinar

May 28th Thursday (11.00AM - 11.30AM) PST

Welcome to webinar hosted by Global Big Data Conference! Please start registering by entering your name and email address to attend Webinar

Schedule:

-- Accelerating Breast Cancer Treatment Planning with Multi-Agent AI Systems on AWS. - Ekta Walia (Principal consultant (HCLS), AWS)

-- Challenges with LLM's in disease diagnosis and treatment - Michael N. Liebman (Managing Director, Co-Founder, IPQ Analytics)

-- The Patient Already Has a Co-Pilot — And It's Not From a Healthcare Company - Chris Hutchins (Founder & CEO, Hutchins Data Strategy Consulting)

Ekta Walia (Principal consultant (HCLS), AWS)

Topic: Accelerating Breast Cancer Treatment Planning with Multi-Agent AI Systems on AWS

Abstract:

Oncologists are overwhelmed by multimodal patient data—imaging, pathology, genomics, clinical histories, and rapidly evolving guidelines, spending as little as 5% of their time in direct patient care. This talk presents a Multi-Agent AI orchestration framework built on AWS that transforms breast cancer treatment planning into an intelligent, adaptive workflow.

Attendees will witness a demo of specialized AI agents powered by Amazon Bedrock, Strands Agents, AgentCore, AWS HealthLake, and HealthImaging that collaborate to synthesize patient data, apply evidence-based guidelines (NCCN/ASCO), and surface the latest research, all while keeping the oncologist firmly in the loop. The framework delivers a 9x improvement in patient-facing time and establishes a scalable blueprint applicable across all oncology domains.

What Attendees Will Gain

Agentic AI in healthcare — How multi-agent systems represent a paradigm shift in clinical decision supportArchitectural blueprint — A replicable design pattern using AWS services (Bedrock, AgentCore, Strands Agents, HealthLake, HealthImaging)

Clinical AI design principles — Human-in-the-loop, evidence-based grounding, and HIPAA-aligned securityReal-world impact — How this framework reclaims oncologist time and accelerates treatment initiation

Bio

Dr. Ekta Walia is a Principal AI/ML/Generative AI Consultant at AWS, specializing in Healthcare and Life Sciences AI solutions. With 26+ years of experience across academia and industry, she is an expert in AI, machine learning, medical imaging informatics, and healthcare interoperability. She is a recognized speaker at leading conferences including HIMSS, MICCAI, and RSNA, and has previously worked with Philips Healthcare and several academic institutions in India and Canada.

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Michael N. Liebman (Managing Director, Co-Founder, IPQ Analytics)

Topic : Challenges with LLM's in disease diagnosis and treatment

Abstract :

The use of Large Language Models, LLM’s, is increasing rapidly in medicine, both by patients and physicians. The most common uses focus on diagnosis and treatment and are being used to influence clinical decision support, but foundational medical research evolves rapidly impacting data and interpretations on a daily basis.

Take home lessons:

1. LLM’s are extremely sensitive to the data upon which they are trained and this is very critical in a rapidly evolving field

2. Gaps and conflicts may appear and disappear over the window selected to provide training data

3. While simply repeating a query is known to produce different results, complex queries can produce even greater variability, e.g. “diagnosis” vs “treatment” vs “diagnosis and treatment”

Bio

Michael N. Liebman is a computational biology, translational medicine, and digital health leader with experience across academia, biotech, and pharma, including leadership roles at Roche, Wyeth, and the University of Pennsylvania Cancer Center.

He is currently Managing Director of IPQ Analytics and advises on disease modeling, AI-driven risk detection, healthcare strategy, and quantum computing.

His work focuses on computational models for clinical decision-making, pharmaceutical risk-benefit analysis, and women’s health, including cardiovascular disease, multiple sclerosis, breast cancer, pregnancy-related disorders, and health disparities.

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Chris Hutchins (Founder & CEO, Hutchins Data Strategy Consulting)

Topic: The Patient Already Has a Co-Pilot — And It's Not From a Healthcare Company

Abstract:

The most important new entrant in healthcare this year doesn't have a hospital, a payer contract, or an FDA clearance. It has hundreds of millions of users.

While the industry has debated AI governance frameworks and pilot programs, patients have quietly made their choice. They're using general-purpose AI assistants to decode lab results at midnight, prep questions before specialist visits, translate medical jargon for aging parents, and get second opinions they could never afford to seek out. The recent formal moves by frontier AI labs into clinical partnerships — and the senior healthcare policy talent they've begun hiring — don't create this shift. They legitimize a behavior already happening at unprecedented scale.

This session explores what the frontier labs' entry into healthcare actually changes, and what it doesn't. We'll look at the new patient archetype emerging from this moment: informed, skeptical, and increasingly arriving at appointments with a printout. We'll examine why patients trust consumer AI tools — none of them built for clinical contexts — more than the systems designed to serve them. And we'll map what clinicians, health systems, and payers can do Monday morning to meet patients where they already are, before the gap becomes uncrossable.

Key Takeaways

Why the patient-as-pilot shift is already a fact on the ground, not a forecast

What the frontier AI labs' healthcare entry signals about the future of patient education and decision-making

The emerging patient archetype — and what it means for the clinical encounter

Concrete actions for clinicians, systems, and payers to stay relevant in a patient-led AI era

Bio:

Most health systems still use governance frameworks built for older systems, while today’s AI changes continuously and can create serious regulatory and legal risk.

With 25+ years leading healthcare data systems, he has seen projects stopped when automated decisions could not be explained or defended.

He now advises health system executives and boards on AI governance, accountability, and defensible deployment so AI decisions can stand up to regulators, courts, and patients.

Register for the Global Artificial Intelligence in Healthcare event (June 25th):
/e/global-ai-in-healthcare-virtual-conference-tickets-1988295787223