Industry News Details
Interview with Ekta Walia, Principal Consultant (HCLS), AWS - Global AI in Healthcare - June 25th Posted on : Jun 11 - 2026
"How Multi-Agent AI Is Accelerating Breast Cancer Treatment Planning: Insights from AWS Healthcare AI Leader Ekta Walia"
Accelerating Breast Cancer Treatment Planning with Multi-Agent AI Systems: An Interview with Ekta Walia, Principal Consultant (HCLS), AWS
AI is transforming healthcare into smarter clinical decision support. In breast cancer care, multi-agent AI helps clinicians quickly synthesize imaging, pathology, genomics, and patient data to make faster, evidence-based treatment decisions while spending more time with patients.
In this interview, Ekta Walia, Principal Consultant (Healthcare & Life Sciences) at AWS, discusses her journey, the inspiration behind her work in breast cancer treatment planning, and how multi-agent AI is accelerating evidence-based clinical decisions while keeping clinicians in control.
## Tell us about yourself and your journey into AI and healthcare.
I am a Healthcare and Life Sciences (HCLS) Principal Consultant with AWS, focused on Generative AI, based in Vancouver, Canada. I specialize in machine learning, AI, data interoperability, and medical imaging informatics. I serve as a trusted advisor for scoping and delivering AI-driven solutions on AWS, working closely with major pharmaceutical and healthcare enterprises. I have authored seven blog posts on utilizing AI technologies for HCLS use cases spanning clinical, commercial, radiology.
I am a frequent speaker at premier healthcare technology conferences including HIMSS, MICCAI, and RSNA. With over 27 years of experience in academia and industry, I hold a Ph.D. in Computer Science with 5,105 citations, an h-index of 26, and an i10-index of 38.
Before joining Amazon, I supported the development of AI/ML-driven Radiology solutions at Philips Healthcare and served as an adjunct professor in Medical Imaging at the University of Saskatchewan, Canada. I previously held faculty leadership roles at multiple academic institutions in India, including Chairperson of Computer Science at the South Asian University (university of SAARC nations) in New Delhi.
## What inspired you to focus on applying AI to breast cancer treatment planning, and what challenges are you helping to solve?
This is deeply personal for me. A few years ago, I was diagnosed with a BI-RADS 4c finding, a highly suspicious lump in my breast that required biopsy. In that moment, I experienced first-hand the anxiety, and the complexity of navigating the clinical journey from imaging to pathology to treatment decisions. I empathized with oncology patients in a way I never had before.
That experience transformed my professional mission. With over 27 years in AI and medical imaging informatics, I had always been passionate about the technology but now I understood the human urgency behind it.
Oncologists spend disproportionate time on administrative data aggregation instead of direct patient care. The data volume across imaging, pathology, genomics, and evolving clinical guidelines is simply overwhelming to synthesize manually, creating bottlenecks that delay treatment initiation. That's exactly what I'm helping to solve.
## Can you share a real-world example of how AI can help clinicians make faster, more informed treatment decisions while improving patient outcomes?
At AWS, we're building a multi-agent AI system for breast cancer treatment planning that tackles oncology's biggest bottleneck: the time clinicians spend synthesizing imaging, pathology, genomics, clinical history, and evolving guidelines before they can develop a treatment plan.
Our solution deploys specialized AI agents on Amazon Bedrock AgentCore, one synthesizes patient data, another extracts clinical history, another retrieves latest evidence from NCCN, ASCO, PubMed, and clinicaltrials.gov. An orchestrator coordinates them in parallel, delivering a comprehensive, evidence-grounded treatment summary in minutes rather than hours.
The design is human-in-the-loop wherein AI handles the cognitive heavy lifting while the oncologist retains full decision-making authority. The result is a 9x improvement in patient-facing time, meaning clinicians spend less time aggregating data and more time with their patients making collaborative treatment decisions.
## What are the top three take aways attendees can expect from your session on Accelerating Breast Cancer Treatment Planning with Multi-Agent AI Systems on AWS?
Agentic AI is a paradigm shift for clinical decision support. Understand how multi-agent systems go beyond conventional AI by autonomously reasoning, coordinating, and synthesizing multimodal clinical data (imaging, pathology, genomics, guidelines) to deliver evidence-grounded treatment summaries in minutes rather than hours.
A replicable architectural blueprint on AWS. Learn a deployable design pattern using Amazon Bedrock, Strands Agents, AgentCore, HealthLake, and HealthImaging, applicable not just to breast cancer but extensible across all oncology domains.
Human-in-the-loop design for regulated healthcare. Learn the design principles that make agentic AI safe and trustworthy in clinical settings: evidence-based grounding, HIPAA-aligned security, and full oncologist control, delivering a 9x improvement in patient-facing time without compromising clinical authority.
## Looking ahead, what AI in healthcare trends are you most excited about, and how do you see them impacting patient care over the next few years?
1. Multi-agent AI systems are moving from research concepts to real clinical deployment at enterprise scale.
2. Agentic AI is rehumanizing healthcare by giving clinicians their time back for direct patient care.
3. Governed, domain-specific AI with built-in compliance is becoming the new standard for clinical adoption.
## Final Thoughts
AI is helping clinicians make faster, more informed decisions while keeping them in control of patient care. Ekta Walia's work shows how multi-agent AI can reduce administrative burden and give doctors more time to focus on their patients.
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Bio:
Dr. Ekta Walia is a Principal AI/ML & Generative AI Consultant at AWS, specializing in Healthcare and Life Sciences solutions. With 26+ years of experience across academia and industry, she is an expert in AI, machine learning, medical imaging informatics, and healthcare innovation. Ekta is a recognized speaker at leading conferences including HIMSS, MICCAI, and RSNA, and has authored multiple AWS healthcare AI publications.
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 support
Architectural 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 security
Real-world impact — How this framework reclaims oncologist time and accelerates treatment initiation