This blog is part of a series covering the 2026 AWS Life Sciences Symposium.

Highlights from the 2026 AWS Life Sciences Symposium: MedTech Track

The global robotic surgery market reached $13.79 billion in 2025, growing at a 16.5% CAGR — a pace that reflects both the scale of adoption and the urgency behind it. FDA-authorized AI/ML-enabled medical devices surpassed 1,400 by end of 2025, nearly doubling since 2023, with close to 300 cleared in a single year. And yet, for all this momentum, the fundamental challenges facing MedTech companies have not gone away — they have intensified. Data volumes are exploding, but most of it remains siloed and unusable. Regulatory complexity is growing. Costs are rising. And the workforce is stretched thin.

At the 2026 AWS Life Sciences Symposium, we brought together some of the most innovative companies in MedTech to share what it takes to build in this environment, and what becomes possible when you have the right data foundation and AI backbone. The stories shared by medtech companies across the industry underscore a critical truth: the technology infrastructure choices companies make today will define their competitive position for the next decade.

The Platform Beneath the Innovation

Dr. Rowland Illing, Chief Medical Officer and Director for Healthcare and Life Sciences at AWS, opened the MedTech track by naming the forces reshaping the industry. The problem is not a lack of data — it is that most of that data is trapped. Interoperability gaps prevent the rich signals generated by connected devices, wearables, and clinical systems from reaching the people and algorithms that could act on them. At the same time, the push toward personalized health is accelerating, driven by Internet of Medical Things (IoMT), physical AI, and a new generation of agentic systems that can reason, plan, and act across complex workflows.

AWS’s response to this is not a single product — it is an end-to-end architecture that spans the full lifecycle of a smart connected medical device, from the edge to the cloud. AWS IoT Core and AWS IoT Greengrass handle device connectivity and remote software deployment. Amazon Kinesis and AWS IoT Analytics normalize and stream data in real time. Amazon Bedrock, Amazon Nova, and Amazon SageMaker turn that data into clinical intelligence. Amazon Connect and Amazon Connect Health extend that intelligence into agentic patient engagement — enabling AI-powered, automated interactions across voice, chat, and digital channels that can proactively reach patients, support care navigation, and close the loop between device data and human action.

Successes of our customers: Siemens Healthineers reduced device connectivity setup time from two hours to five minutes. Fresenius Medical Care built a remote dialysis monitoring system that captures patient data every ten seconds and identifies intradialytic hypotension risk 15 to 75 minutes before it occurs. And Pfizer’s Digital Medicine and Translational Imaging group processed approximately 36,000 hours of wearable sensor data — in minutes rather than days — to accelerate decentralized clinical trials.

Building the Future of Surgery: Johnson & Johnson MedTech’s Polyphonic™

Every year, 300 million surgeries are performed each year globally (reference WHO). Surgical complications affect one in three patients; the cost of care is unsustainable, and burnout among surgical teams is pervasive. And yet the operating room remains one of the least digitized environments in healthcare — not because the data isn’t there, but because no one has built the infrastructure to capture, connect, and act on it at scale.

That is the problem Johnson & Johnson MedTech set out to solve with Polyphonic™. Daniel Carchedi, Global Head of Partnerships and Alliances, and Alexandre Hennen, Global Head of Design, described a vision that is as ambitious as it is grounded: an open, device-agnostic multimodal AI ecosystem that captures intra-operative surgical video, imaging, device logs, EHR signals— and turns that data into surgical intelligence that can be embedded directly into clinical workflows. The volume of data being generated in the OR is growing faster than most organizations can process it. To put the data challenge in perspective: one minute of HD surgical video contains 25 times more data than a CT scan.

The AWS architecture underpinning Polyphonic™ was chosen to match the data volumes, AI workloads, and compliance requirements the platform demands. The architecture — spanning EKS Auto Mode for intelligent microservices management, Amazon Aurora PostgreSQL for high-availability data storage, AWS Control Tower for governance across accounts, and Amazon OpenSearch for system-wide visibility — 38 AWS services, with more than 100 people contributing to its development. The Digital Surgery Flywheel at the heart of Polyphonic™ captures multimodal data, curates de-identified datasets, accelerates model development, and embeds proven AI solutions into the workflows where surgeons and care teams work.

Insight-Driven Care at Scale: Medtronic’s Federated Innovation Model

Medtronic set out to make healthcare more predictive, personalized, and proactive — what the company calls Insight Driven Care. As teams across the organization built toward that vision independently, the result was a pattern that scales poorly: siloed solutions, inconsistent customer experiences, mounting technical debt, and R&D investment that could not compound because it was never shared. Rashmi Kumar, Medtronic’s CIO, and Karl Anderson, Senior Director of Product Innovation, share the important of moving fast by enabling the organization to operate within a federated model — a shared, reusable foundation of data, AI, and software capabilities built on AWS, while preserving R&D autonomy at the therapy and patient experience layer. Non-differentiating capabilities — data science workbench, healthcare imaging, connectivity, compliance, master data management — moved to the center. Differentiation was reserved for where it actually matters specific therapies, patient journeys, and clinical workflows. The economics followed: 30% cost savings per use case, a two-quarter improvement in cycle time, and build and maintenance costs reduced from three times and two times the baseline to one. Amazon S3, AWS Lambda, AWS HealthLake, AWS Glue, AWS Lake Formation, and Amazon SageMaker AI are the services that make the platform run at the speed Medtronic’s business demands.

Experience as the Engine: DocSpera and the MedTech Innovation Flywheel

Samuel Ethiopia, CEO of DocSpera, and Luca Santarella, CTO of DocSpera made a point that reframes where MedTech innovation actually happens not in the lab but in every patient interaction, in every moment care is made easier.

DocSpera operationalizes this across the full surgical journey — AI-powered patient preparation and scheduling before surgery, real-time OR coordination and case readiness on the day of the procedure, and automated outcome collection and patient-reported surveys post-operatively. The platform serves more than 1,100 surgical sites, integrates with 650-plus systems, and supports more than 40,000 surgeries per month. It runs on AWS — HealthLake for FHIR-native clinical data, Amazon Connect for real-time patient communication, SageMaker for ML-based risk scoring and OR optimization and Comprehend Medical for extracting intelligence from clinical notes.

The results are concrete. A global pain management device company using DocSpera’s platform achieved a 90% patient engagement rate, with more than 10,000 patients contributing real-world outcome data. That data demonstrated therapy superiority over alternatives and enabled continuous OR technique refinement from live post-operative evidence.

Robots, Real-World Data, and the Future of Clinical Labor: Diligent Robotics

Andrea Thomaz, CEO of Diligent Robotics, opened up with the numbers that define the problem. Seventy percent of nursing time is spent on logistics, not patient care. Fifty percent of newly hired RNs leave their hospital within two years. The United States faces 200,000 open nursing positions annually for the next decade. Moxi, Diligent’s autonomous hospital delivery robot, addresses the challenges of the clinical workforce directly.

It handles point-to-point delivery of medications, lab samples, and supplies across hospital floors — freeing clinical teams to work at the top of their license. The results are measurable: 1.2 million tasks completed; 500,000 clinician hours saved. At Children’s Hospital Los Angeles, Moxi has completed 15,000 infusion center deliveries since 2022. Carol Taketomo, Chief Pharmacy Officer at CHLA, was direct: Moxi has helped our staff recoup 20 to 30 minutes per delivery. In a system facing a decade-long workforce shortage, that compounds quickly.

What makes Diligent’s model strategically significant is what happens behind the robot. Hospital environments are the hardest to simulate, which makes real-world deployment data the most valuable asset in clinical robotics AI. Every Moxi deployment feeds a growing corpus of 400 terabytes of multimodal interaction data, training a three-billion-parameter Vision-Language-Action model on 100,000 real-world trajectories added every month. AWS provides the infrastructure to make this work at scale — through the AWS and NVIDIA 2025 Physical AI Fellowship, the Generative AI Innovation Center, and Amazon SageMaker HyperPod for model training and hyperparameter search. Each deployment improves the model. Each improvement makes the next deployment more capable.

The Broader Amazon Advantage

AWS is the foundation — but the full Amazon ecosystem unlocks capabilities that no other cloud provider can offer MedTech companies building the next generation of connected, patient-centered solutions.

Amazon LEO brings high-speed satellite internet to geographies where traditional connectivity fails, enabling remote patient monitoring and decentralized clinical trials at truly global scale. Critically, Amazon Leo is not a standalone product — it integrates with the full AWS architecture. The connectivity layer and the intelligence layer are from the same provider, which simplifies architecture, security, and compliance.

Alexa and Amazon’s voice-driven experiences open new possibilities for patient engagement and clinical workflow. Voice interfaces help patients navigate their care journey with less friction and more confidence.

Amazon’s logistics network and Amazon Business bring a different kind of advantage: the ability to deliver medical devices and supplies to patients with the convenience and reliability that consumers now expect from every other part of their lives.

Combined with AWS’s purpose-built healthcare services, the agentic AI stack — Amazon Bedrock, AgentCore, Amazon Nova, Strands Agents — and the Generative AI Innovation Center, Amazon offers MedTech companies something genuinely unique: a partner that spans the physical and digital dimensions of care, from satellite connectivity to surgical AI, from last-mile logistics to FHIR-native data infrastructure.

The Only Partner Built for What’s Next

The results from the MedTech Track speak for themselves. Medtronic cut R&D costs by 30% per use case and compressed cycle times by two quarters by shifting to a federated platform on AWS. DocSpera achieved a 90% patient engagement rate and put more than 10,000 patients’ real-world outcome data to work for a single device company. Diligent Robotics reclaimed more than 500,000 clinical hours — 20 to 30 minutes per delivery, per nurse, per shift — while training the next generation of hospital AI on real-world data at a scale no simulation can replicate. And Pfizer’s clinical trials team processed 36,000 hours of wearable sensor data in minutes rather than days, compressing the path from trial to therapy.

These outcomes share a common foundation. Not a single product, but a platform — one that spans connected devices at the edge, real-time data infrastructure, purpose-built healthcare services, agentic AI, and the physical reach of the broader Amazon ecosystem. MedTech companies that build on that foundation are not just moving faster. They are building compounding advantages: data that improves models, models that improve devices, devices that generate better outcomes, and outcomes that attract more data. The flywheel is real, and it runs on AWS.

The path forward is straightforward: define the business outcomes that matter, build with AWS and our partner network, and scale with confidence.