By 2025, healthcare will generate over one-third of the world’s 180 zettabytes of data, with cancer care producing a large part of it. Despite having so much information, only about 3% of healthcare data is used well because current systems have limits. Medical knowledge grows quickly, doubling every 73 days, especially in cancer. This makes it hard for doctors to review all the complex information during short patient visits that last 15 to 30 minutes.
For example, doctors check prostate-specific antigen (PSA) levels along with other information like molecular markers, scans, biopsy reports, and patient history. Putting all this data together by hand takes a lot of time and can lead to mistakes or missed details. This can delay decisions or cause patients to miss needed care. Around 25% of cancer patients miss some care because of delays, workflow problems, and poor coordination between different departments.
Agentic AI means smart computer systems that work on their own but also together to analyze data, do tasks, and manage workflows in healthcare. These systems use advanced language models and models that handle many types of data like notes, lab tests, genetic information, images, and pathology reports.
Unlike older AI, which does one task at a time, agentic AI has special agents for different data types. For instance, one agent looks at electronic medical records, while others check lab tests or genetic markers. A central agent brings together all the information from these smaller agents to make useful suggestions for cancer treatment plans. This helps doctors make better decisions faster and helps different hospital departments work together more smoothly.
With these features, agentic AI helps create treatment plans that are more on time, accurate, and match the needs of each cancer patient.
Healthcare in the U.S. is often split up across many systems and departments, which makes smooth patient care hard. Agentic AI helps fix this by linking workflows between different specialties and parts of the healthcare system. It collects and looks at data while helping teams in oncology, pathology, radiology, and surgery communicate quickly.
Doctors have too much data to handle and only a short time with patients. Agentic AI lowers this pressure by automatically pulling out important details and giving clear recommendations. This way, doctors can spend more time with patients instead of on paperwork.
Cloud services like Amazon S3, DynamoDB, Fargate, and Amazon Bedrock support these AI systems. Amazon Bedrock helps keep agents working smoothly by remembering context and managing tasks without losing important clinical details over time.
In cancer care, it is very important to check AI results carefully. Agentic AI works on its own, but humans always review its suggestions before using them in treatment decisions. Doctors use easy-to-understand dashboards to check that the AI’s outputs are correct.
This approach helps catch errors or false information and keeps human judgment in control. Regular audits and reviews also build trust among healthcare workers and patients.
Leading healthcare groups have accepted the value of agentic AI. Dan Sheeran from AWS says this type of AI can shorten the time it takes to develop AI tools from months to days. This helps bring useful tools to doctors and administrators faster.
Dr. Taha Kass-Hout from Amazon explains that agentic AI breaks down barriers between information systems. His work on Amazon HealthLake and Amazon Comprehend Medical has helped link AI better with clinical work.
GE Healthcare and AWS are building multi-agent AI systems for cancer care workflows, managing resources, and coordinating treatments. This shows how cloud technology and AI can create smoother patient care paths in the U.S.
Healthcare groups caring for cancer patients across the U.S. should consider these AI tools and cloud services to improve patient safety, follow rules, and smooth workflows.
Integrating complex healthcare data with agentic AI offers a clear way to plan personalized cancer treatment and improve diagnostic and therapy coordination in the United States. By using advanced AI models, safe cloud systems, and human checks, healthcare providers can improve the quality of care, work more efficiently, and better serve cancer patients.
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.
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