Industry Spotlight: Healthcare & Life Sciences

AI for clinicians, researchers, and patients.

7 sessions at the summit3 external resources

Overview

AWS supports healthcare with HIPAA-eligible services, AWS HealthLake for FHIR data, Amazon HealthScribe for clinical note generation, and Amazon Bedrock for life-sciences research (drug discovery, clinical-trial analysis). AWS HealthOmics handles genomic, transcriptomic, and other omics data at scale. Privacy-preserving techniques (federated learning, AWS Clean Rooms) enable cross-organization collaboration on sensitive data.

Key concepts

  1. FHIR and interoperability standards
  2. Clinical NLP and ambient AI scribes
  3. Genomic and multi-omics workflows
  4. Privacy-preserving collaboration
  5. Real-world evidence and clinical trials

Key AWS services

  • AWS HealthLake
  • Amazon HealthScribe
  • AWS HealthOmics
  • Amazon Comprehend Medical

Learn more — curated resources

Hand-picked official docs, foundational papers, and the best community guides for going deeper on this topic.

Sessions on this topic

7 sessions from the Summit covered this topic. Each is a self-contained mini-lesson.

  1. DEV207Intermediate

    Data Observability Without the Pain - Lessons from a Production System

    Modern IoT platforms are inherently data platforms. Events flow through APIs, queues, AWS Lambda Serverless functions, storage systems, and device networks before becoming meaningful data. When something goes wrong, tracing a single event across these distributed components quickly becomes painfuland the question shifts from _what happened_ to _where do I even start looking Ill walk through three practical observability patterns drawn from building and operating a production, event-driven IoT healthcare platform on AWS that processes tens of thousands of device events daily. Using OpenTelemetry, AWS X-Ray and Honeycomb, well explore techniques for gaining visibility into asynchronous event pipelines, correlating activity across services, and tracing events as they move through distributed systems. Youll leave with three concrete patterns you can apply immediately to your own event-driven data systems.

  2. WPS203Intermediate

    Optimising Outpatient Waitlists with ML at Gold Coast Health

    Deploying ML in high-stakes environments demands enterprise readiness, governance, and continuous monitoring. In this session, you'll learn how Gold Coast Health moved from pilot to production with a predictive model identifying patients unlikely to attend procedures — achieving 33% precision, doubling the 15% manual baseline — while ensuring fairness across cohorts. The session covers real-world ML architecture on Amazon SageMaker Pipelines, production monitoring including data quality, pipeline health, and drift detection, plus navigating AI governance through bias analysis and impact assessment. Whether you're in healthcare, financial services, or any regulated industry, walk away with actionable patterns for deploying responsible ML at scale.

  3. WPS301Advanced

    AWS for healthcare analytics: accelerating time to insights

    In today's data-driven healthcare landscape, organisations must rapidly transform diverse data sources into actionable insights that improve patient outcomes and accelerate operational efficiency. This session showcases how AWS' integrated analytics capabilities can deliver unmatched price-performance for every analytics workload, from data processing and SQL analytics to streaming and business intelligence. Through real-world healthcare examples, learn how AWS' built-in governance and scalability enable organisations to build secure, efficient analytics pipelines that accelerate time-to-insight. Ideal for data practitioners, IT decision-makers, and executives evaluating enterprise analytics platforms to drive their data-driven transformation.

  4. STP204Intermediate

    How Heidi Health Fine-Tunes Speech-to-Text Models on AWS

    Join Heidi Health and AWS's Generative AI Innovation Center (GenAIIC) for a behind-the-scenes look at building and deploying custom speech-to-text AI for healthcare. Learn hard-won lessons and a practical blueprint: curating domain-specific training data, fine-tuning open-weight models, validating non-deterministic outputs at scale, and shipping to production with optimized inference. Both teams share how AWS services reduced infrastructure complexity, accelerated iteration cycles, and scaled custom models across diverse real-world use cases — all while maintaining security and cost efficiency. Ideal for ML engineers, data scientists, and technical leaders exploring fine-tuning and production ML on AWS.

  5. ISV101Foundational

    How AI is Transforming Pharmacy Care with Amazon Nova:MedAdvisor Story

    MedAdvisor, Australia's leading medication management platform connecting over 90% of community pharmacies, faced a critical challenge: pharmacists were losing hours daily to manual documentation, diverting time from patient care. To solve this, MedAdvisor built an AI-powered Scribe using Amazon Nova on Amazon Bedrock. The tool listens to pharmacy consultations in real time, transcribes conversations, and automatically generates structured clinical notes. Through iterative prompt engineering, output quality improved from 3.5 to nearly 4.5 out of 5, surpassing market alternatives. Now in beta across Australia, the solution went from concept to production-grade clinical documentation in under six months.

  6. STP216Intermediate

    Building AI Agents: From Open-Source Frameworks to Production-Grade

    AI agents are moving from demo to deployment. Startups across ANZ are building production-grade assistants using open-source orchestration frameworks, fine-tuned foundation models, and GPU-accelerated inference on AWS and NVIDIA infrastructure. This panel explores what it actually takes to ship agentic use casesfrom choosing the right models and frameworks to managing latency, cost, and reliability at scale. We'll hear from AirTree VC on where the investment thesis is heading, from NVIDIA on how accelerated compute is shaping the agent stack, and from Heidi Health building and scaling these systems in production. Whether it's vertical agents for healthcare, customer support, or code generation, we'll focus on what's working, what's hype, and where the real startup opportunities lie in the agent ecosystem.

  7. SMB205Intermediate

    How Blackmores accelerated SAP RISE connectivity with an EBA and Kiro

    Healthcare company, Blackmores, accelerated their SAP RISE program by utilising Experience Based Acceleration (EBA) to build a production-ready AWS Landing Zone with Terraform in two days using Kiro.

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Non-obvious insights

From the Playbook

One sharp, contrarian insight per session — the things teams don't think of unprompted.

The biggest observability win is not tools — it's a *correlation ID standard* the team enforces. Pick one (the X-Ray trace ID is fine), enforce it everywhere, and stop debating. Tooling matters far less than you think once the IDs are consistent. ---DEV207 — Data Observability Without the Pain - Lessons from a…
33% precision means 67% false positives. Deployment success depends on what you *do* with the prediction — calling patients vs. removing slots vs. double-booking. The model is only as good as the workflow around it. Build the intervention design before chasing higher precision. ---WPS203 — Optimising Outpatient Waitlists with ML at Gold Coas…
Most "healthcare analytics" wins come from joining clinical and operational data — currently siloed in most hospitals. Bringing them together unlocks insights neither team has individually. The technical work is medium; the political work is hard. ---WPS301 — AWS for healthcare analytics: accelerating time to i…
The dominant accuracy issue in healthcare STT in Australia isn't medical jargon — it's *accents and code-switching*. Patient cohorts are linguistically diverse; clinicians switch registers. Train accordingly; English-only test sets miss most of the failure cases. ---STP204 — How Heidi Health Fine-Tunes Speech-to-Text Models on…
Iterative prompt engineering moved quality from 3.5 to 4.5 — meaningful, but more importantly a sign that prompts are now *load-bearing IP*. Treat them like code: versioned, code-reviewed, regression-tested. Most orgs still treat prompts as folklore. ---ISV101 — How AI is Transforming Pharmacy Care with Amazon Nov…
VC investment thesis is shifting from "agent capability" to "agent vertical depth." Generic agents are commoditising fast; domain-specific agents have moats. If you're raising for a generic agent platform in 2026, you're raising in a saturated market. ---STP216 — Building AI Agents: From Open-Source Frameworks to P…