Agentic AI Meets Patient Care: Inside AWS’s Blueprint for the Next Era of Healthcare

Artificial intelligence is rapidly becoming one of the most powerful forces shaping the future of healthcare and life sciences. From improving patient access and operational efficiency to accelerating research and drug discovery, AI is beginning to move beyond experimentation and into real-world deployment across the healthcare ecosystem. As organizations look to unlock the value of vast volumes of clinical and scientific data, the ability to translate AI innovation into scalable, secure solutions is becoming increasingly critical.

Amazon Web Services (AWS) is expanding its AI and cloud capabilities to support this transformation, providing healthcare providers, pharmaceutical companies, and research organizations with tools designed to turn complex health data into actionable insights. Through purpose-built services, advanced generative AI technologies, and a global cloud infrastructure, AWS is helping organizations apply AI across clinical workflows, patient engagement, scientific research, and operational processes.

In a conversation with Dr. Angela Shippy, Senior Physician Executive and Clinical Innovation Lead for Healthcare and Life Sciences at AWS, the discussion explores AWS’s broader vision for AI in healthcare, the key challenges organizations face in deploying AI at scale, and how emerging technologies are helping healthcare teams move from experimentation to real-world impact.

What is AWS’s vision for AI in healthcare and life sciences, and how do you see it shaping patient care?

The organizations that will thrive in the AI era will be those that master both the technological implementation and the human factors of AI adoption. AWS is uniquely positioned to help healthcare organizations navigate both dimensions successfully. While the promise of AI across healthcare is extraordinary, from lower costs to personalized treatments, the reality is that 70-80% of healthcare AI initiatives stall at the pilot phase. While there are a number of factors behind this, one of the biggest blockers is access to data. 97% of healthcare data goes unused because it is trapped in unstructured data. At AWS, our goal is to help healthcare organizations unlock the power of their data, from clinical notes to multi-modal data, and even social determinants of health data, to arrive at actionable insights to improve outcomes. We do this by leveraging purpose-built managed health services and capabilities aligned to common healthcare business needs from data transfer and storage to health data protection and governance, all while meeting the most stringent security and compliance regulations in the industry. As a result, we’re seeing real, in-production results with our customers.

Just like how AWS democratized access to cloud computing services, AWS is democratizing access to building and using generative AI technology so that companies of all sizes, with developers of all skillsets, can take part in this transformation and grow their businesses. AWS’s approach is to help customers quickly take advantage of the latest technologies available now (and what is likely coming tomorrow) and begin using generative AI and agentic AI within their organizations to transform their offerings and operations. That said, we also understand that partnerships are essential to making the promise of AI real for our healthcare customers. AWS supports the organizations bringing together clinical and tech expertise to advance the use of AI for some of the most impactful challenges that will help improve patient outcomes. For example, the Cancer AI Alliance (CAIA), with which AWS is proud to partner, is a federated AI learning framework that allows researchers to learn from huge amounts of de-identified cancer data in a secure environment.

Which clinical or operational challenges in healthcare are being most impacted by AWS AI today?

It’s fascinating to see that healthcare is now deploying AI at more than 2.2x the rate of the broader economy. Today, I’m seeing innovation span across the spectrum of customer sizes and use cases. Our healthcare provider customers are exploring new ways to use AI for areas such as education and research, operational efficiency, clinical workflows, and patient engagement. Similarly, our pharmaceutical customers are applying AI applications across the whole pharma value chain, from R&D to enhancing manufacturing oversight.

One challenge that AI is helping solve that I find particularly interesting is improving the patient experience. Using Amazon Connect, an AI-powered contact center solution from AWS, our healthcare customers are streamlining patient interactions, improving efficiency, and enhancing overall service quality in clinical settings. By leveraging Amazon Connect’s advanced AI capabilities, healthcare providers can offer personalized, efficient, and responsive customer service at scale. For example, using AI-powered capabilities in Amazon Connect, Jupiter Medical Center cut call center operating expenses by 30%, eliminated a persistent backlog of 1,800 radiology appointments by 60%, and improved equipment utilization to 95%. This ultimately enables patients to schedule critical diagnostic care within 24 hours instead of waiting 2 weeks.

Another example is using generative AI-powered multimodal agents for research report generation. To find key information, researchers need to comb through data from various internal and external data sources. They also need to separate quality data from noise, and then summarize and analyze this data quickly – all while protecting the data and adhering to regulatory requirements. AWS offers an AI-powered multi-agent solution that helps researchers fast-track their research by autonomously combing through millions of articles on PubMed, analyzing clinical trials, searching through molecular databases, and reasoning with the information collected to understand the clinical and scientific relevance of the problem.

Finally, AWS is helping healthcare organizations move from failed proofs-of-concept to production-ready agentic AI deployments that deliver measurable business value. While many initial agentic AI experiments have struggled with poor ROI and scalability challenges, AWS provides the strategic approach and trusted foundation healthcare organizations need to deploy AI agents – software systems that can reason, plan, and act autonomously – securely at scale across the entire healthcare value chain.

For example, Wiley and AWS have partnered to launch a generative AI agent designed for full-text scientific literature search, allowing researchers to access detailed content from Wiley’s extensive journal catalog, including methods and results sections, moving beyond traditional abstract-only searches. The AI agent is part of AWS’s open-source toolkit for healthcare and life sciences agents, supporting use cases from biomarker discovery to clinical trial protocol generation.

How does AWS help developers and healthcare professionals build AI solutions without deep technical expertise?

AWS meets healthcare organizations wherever they are on their AI journey with flexible offerings that adapt to their specific needs. For example, AWS offers a comprehensive suite of solutions for organizations seeking agent applications. These include ready-to-deploy options like Amazon Quick Suite, robust tools for building custom agents via Amazon Bedrock and AgentCore, and essential capabilities for open agentic platforms. We also provide organizations with the most comprehensive toolkit and fastest path to production for AI agents through things like our open-source life sciences agents toolkit on GitHub. This includes more than 20 starter agents purpose-built for healthcare use cases, enabling organizations to accelerate development and deployment.

Customers are already seeing remarkable results, with organizations like AstraZeneca reaching production-ready deployments in six months while meeting rigorous cybersecurity and AI governance requirements.

We're collaborating deeply with our customers to understand the most value-add use cases, and building accelerators around these to help other healthcare organizations get started faster. Rather than broad experimentation that often fails to scale, AWS helps healthcare customers identify and implement the specific use cases that align with their business goals and deliver tangible outcomes.

Could you share an example of a healthcare team using AWS AI to improve outcomes or efficiency?

Similar to how Jupiter Medical Center is leveraging Amazon Connect, UC San Diego Health is using Amazon Connect’s AI capabilities to handle inbound appointment requests. In doing so, UCSD is reducing patient abandonment rates by an average of about 30% (and up to 60% in some departments) and achieving a 20% reduction in call handle time.

What sets AWS apart from other cloud providers for healthcare and life sciences organizations worldwide?

Thousands of healthcare organizations trust AWS to power their infrastructure, become more agile, and lower costs. With the most comprehensive set of data, IoT, and AI capabilities, including purpose-built services that make it easier to extract insights from structured and unstructured data, AWS offers the easiest way to connect and act on all your data, collaborate with the right data partners, and make data-driven decisions with the latest AI advances. AWS provides the most comprehensive AI capabilities and global infrastructure footprint, with healthcare experts who have joined AWS from previous roles ranging from lab directors to physicians to Chief Executive Officers. Organizations in the heavily-regulated healthcare and life sciences industries – from biopharmas to healthtechs to providers and payors – need to accelerate time to diagnosis and insights, increase the pace of innovation, and bring differentiated therapeutics to market faster with an end-to-end data strategy. AWS provides a centralized hub for innovation and collaboration on a global level, connecting customers with the data and machine learning tools they need and partners they can trust, all while keeping health data secure and private.

That last point is especially important when you consider AI adoption and using AI agents. Healthcare organizations choose AWS for the most secure and compliant agentic AI platform designed for heavily regulated environments. As the first major cloud provider to receive ISO 42001 certification for responsible AI, AWS provides more than 130 HIPAA-eligible services that healthcare organizations can trust. Amazon Bedrock AgentCore delivers isolated compute environments and security dashboards for regulatory compliance, with unique memory management capabilities that allow regulators to audit agent decision-making processes, critical for healthcare compliance requirements.

This is why healthcare leaders trust AWS to power their most critical AI agent deployments .19 of the top 20 pharmaceutical organizations globally by revenue use AWS for generative AI and machine learning, and industry leaders, including Roche, Lilly, and AstraZeneca, are now leveraging AWS agentic AI capabilities to drive greater efficiencies and accelerate discoveries at scale.

Finally, I’ll add that our unique culture of customer-centric innovation enables AWS to develop services at a faster pace than any other cloud provider, empowering healthcare organizations to ignite their own innovation engines, increasing the speed at which new products and services are developed. Our health customers have multiple stakeholders, and they need to innovate on behalf of all of them at the same time. It’s important to work backwards from these many stakeholders and use Amazon’s Day 1 thinking to reimagine how healthcare innovations are delivered.

Could you share a success story where AWS AI made a meaningful difference for a healthcare customer?

Let me share one success story from our work with Genentech. Their scientists spend a vast amount of time on procedural tasks, like sourcing data and sifting through journals. By collaborating with AWS on their gRED Research Agent, Genentech was able to eliminate many of the tedious manual aspects of research by having the AI agent comb through vast data sets and cross-reference scientific journals. Built using Anthropic Claude Sonnet 3.5 in Amazon Bedrock Agents, Genentech estimates that the gRED Research Agent can potentially help automate more than 43,000 hours of manual biomarker validation across therapeutic areas each year, giving scientists more time to bring new medicines to patients faster. Another example that comes to mind is our work with Pfizer.

Pfizer leverages generative AI on AWS to identify and validate protein targets in a fraction of the time, with their VOX platform. The platform is estimated to help Pfizer save $750 million to $1 billion annually through accelerated research and automated content generation.

Of course, we cannot forget how patients benefit from these success stories. For example, as part of AWS’ $60M commitment to improve health equity, we provided resources to Seattle-based startup Hurone AI to help build and scale AI-powered applications that enable oncologists to provide remote patient monitoring and tele-oncology care. Hurone AI developed its applications using data derived from people of African descent and offers its solutions to patients in Rwanda, which has less than 15 oncologists for a population of 13.5 million. Powered by AWS, Hurone AI’s Gukiza app allows oncologists to communicate with patients using digital devices and text messages, increasing the ability to provide care to more patients in more places. AWS believes in technology as a force for good, and I’m so inspired by the innovations our customers deliver every day to improve the lives of people around the globe.

Wrapping up
As healthcare organizations continue to integrate AI into clinical, operational, and research environments, the emphasis is increasingly shifting toward solutions that can deliver measurable results while maintaining security, compliance, and trust. The ability to combine advanced AI capabilities with robust data infrastructure will play a critical role in shaping how healthcare organizations innovate and scale in the coming years.

From improving patient experiences to accelerating scientific discovery, the next phase of AI adoption in healthcare will depend on how effectively organizations harness their data and translate technological advancements into meaningful outcomes. As Dr. Shippy emphasizes, the opportunity ahead lies in using AI not only to enhance efficiency but also to unlock new possibilities for improving patient care and advancing medical innovation.

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