Healthcare produces a large amount of data, and this number is expected to keep growing. By 2025, healthcare will make up over one-third of the world’s 180 zettabytes of data. For healthcare providers in the US, this means dealing with records, images, tests, treatment plans, and patient histories on a very large scale. However, hospitals and clinics currently use only about 3% of all this data effectively.
The main reasons for this low use are slow data processing and the difficulty of managing multi-modal data. Multi-modal data is information stored in different forms or from various clinical sources. For example, a patient’s case might include written clinical notes, lab results, X-rays, and genetic tests. Handling and understanding such different kinds of data is hard for healthcare workers, especially when they have limited time during patient visits.
Medical knowledge grows fast; it doubles about every 73 days. This fast growth puts more pressure on doctors, especially experts in fields like cancer, heart disease, and brain disorders. New discoveries often change treatments, making it hard for doctors to stay updated and provide the best care for each patient.
Because of these problems, about 25% of cancer patients experience missed or late care. This situation creates serious health risks.
Agentic AI is a newer form of artificial intelligence that can solve many problems faced by healthcare workers and leaders. Unlike older AI that only gives suggestions, agentic AI works on its own and coordinates among several AI agents. These agents work together to analyze and manage complex healthcare tasks.
Using large language models (LLMs) and multi-modal foundation models, agentic AI can handle many types of healthcare data—like text, images, lab results, and genetic info—and create helpful insights for doctors. For example, in cancer care, different AI agents can examine biopsy results, blood markers, MRI scans, and molecular data. A coordinating AI agent then combines these findings to make a personalized treatment plan and schedule patient appointments.
Many US medical practices have too much paperwork and repetitive tasks. AI-based workflow automation, especially agentic AI, can reduce these problems for administrators and IT staff.
Using AI-powered workflow automation helps healthcare organizations work better and serve patients faster and safer.
Dan Sheeran leads AWS Healthcare and Life Sciences Business Unit. He says agentic AI can lower paperwork and scheduling tasks for doctors. This lets providers focus more on patient care. Sheeran has founded startups related to telehealth and chronic disease management, which gives him real experience with healthcare work.
Dr. Taha Kass-Hout works on Amazon projects like Amazon HealthLake and Amazon Comprehend Medical. He notes that agentic AI can bring data streams together and break down barriers between institutions. His work during the COVID-19 pandemic supports his idea that AI with human oversight helps provide safe, personalized care.
Both leaders mention using AI with cloud services like Amazon Bedrock. This helps manage complex AI workflows, keeps context, handles long tasks in the background, and supports ongoing learning. These features are important in healthcare where data and patient needs change all the time.
As healthcare moves toward using more data, administrators and IT staff must get ready to adopt agentic AI tools to manage rising data amounts. Benefits include:
To use these technologies, US healthcare providers should check AI platform options, prepare cloud systems, train staff, and set up clear human review steps to keep care safe.
The US healthcare system is at an important point where using data well can improve patient results. Agentic AI offers a way to move past the current low use of healthcare data. It automates workflows, connects different data types, and supports team-based clinical care. With secure, cloud-based AI systems, healthcare managers and IT leaders can guide this change to make operations more efficient and improve patient care.
Agentic AI systems address cognitive overload, care plan orchestration, and system fragmentation faced by clinicians. They help process multi-modal healthcare data, coordinate across departments, and automate complex logistics to reduce inefficiencies and clinician burnout.
By 2025, over 180 zettabytes of data will be generated globally, with healthcare contributing more than one-third. Currently, only about 3% of healthcare data is effectively used due to inefficient systems unable to scale multi-modal data processing.
Agentic AI systems are proactive, goal-driven, and adaptive. They use large language models and foundational models to process vast datasets, maintain context, coordinate multi-agent workflows, and provide real-time decision-making support across multiple healthcare domains.
Specialized agents independently analyze clinical notes, molecular data, biochemistry, radiology, and biopsy reports. They autonomously retrieve supplementary data, synthesize evaluations via a coordinating agent, and generate treatment recommendations stored in EMRs, streamlining multidisciplinary cooperation.
Agentic AI automates appointment prioritization by balancing urgency and available resources. Reactive agents integrate clinical language processing to trigger timely scheduling of diagnostics like MRIs, while compatibility agents prevent procedure risks by cross-referencing device data such as pacemaker models.
They integrate data from diagnostics and treatment modules, enabling theranostic sessions that combine therapy and diagnostics. Treatment planning agents synchronize multi-modal therapies (chemotherapy, surgery, radiation) with scheduling to optimize resources and speed patient care.
AWS services such as S3, DynamoDB, VPC, KMS, Fargate, ALB, OIDC/OAuth2, CloudFront, CloudFormation, and CloudWatch enable secure, scalable, encrypted data storage, compute hosting, identity management, load balancing, and real-time monitoring necessary for agentic AI systems.
Human-in-the-loop ensures clinical validation of AI outputs, detecting false information and maintaining safety. It combines robust detection systems with expert oversight, supporting transparency, auditability, and adherence to clinical protocols to build trust and reliability.
Amazon Bedrock accelerates building coordinating agents by enabling memory retention, context maintenance, asynchronous task execution, and retrieval-augmented generation. It facilitates seamless orchestration of specialized agents’ workflows, ensuring continuity and personalized patient care.
Future integrations include connecting MRI and personalized treatment tools for custom radiotherapy dosimetry, proactive radiation dose monitoring, and system-wide synchronization breaking silos. These advancements aim to further automate care, reduce delays, and enhance precision and safety.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
