Healthcare workers face a lot of data every day. This data is often complex and hard to manage. By 2025, healthcare is expected to produce over 60 zettabytes of data worldwide. The total amount of data in the world will be more than 180 zettabytes by then. Healthcare will make up over a third of this. However, only about 3% of healthcare data is used well now because it is hard to process and connect.
This causes a big problem called cognitive overload for doctors and nurses. They must understand many types of data quickly to make good decisions. For example, doctors treating prostate cancer look at many things. These include PSA levels, test results, images, biopsy reports, and medicines the patient takes. They have only 15 to 30 minutes per patient. This short time can lead to missed care and delays. It is estimated that 25% of cancer patients do not get all the care they need on time.
System fragmentation makes this worse. Patient data is kept separately in areas like oncology, radiology, surgery, and labs. This makes it hard for teams to work together. As a result, patients get incomplete care, poorer treatment plans, and longer waits.
Agentic AI is a type of artificial intelligence made of many smart agents that work together on their own, all the time. Normal AI often does one simple task at a time. Agentic AI uses groups of agents to handle many tasks across different areas. Each agent studies certain types of data, like clinical notes, lab results, images, or molecular information. Together, they combine their findings to manage patient care plans immediately.
Agentic AI uses large language models (LLMs) and models that work with many data types. These models can remember patient details and past interactions over time. This helps with making decisions that fit each patient’s situation.
Unlike simple AI agents that do set tasks like scheduling or summarizing data, agentic AI agents work continuously together. They can break down complex jobs and assign tasks in smart ways. This makes them good for healthcare settings that need to handle many challenges at once.
Agentic AI helps reduce cognitive overload by handling many kinds of healthcare data that humans find hard to process alone. It combines electronic medical records, lab results, images, genetic data, drug information, and clinical rules to create useful insights fast.
For example, in cancer care, specialized agents check chemical markers, images, pathology results, and genetics. Another agent puts this information together to make treatment suggestions. These recommendations are automatically added to the patient’s record. This saves doctors time and lets them focus on treating patients.
Agentic AI systems also watch for urgent changes in patient health. They can schedule tests or treatments quickly. This helps avoid delays in care which happen in systems where communication is poor. Automating these steps cuts down missed appointments and forgotten priorities.
One big problem in healthcare is that departments work separately from each other. Agentic AI helps fix this by linking different apps and databases smoothly. It supports teamwork between oncology, radiology, pathology, and surgery by joining their workflows into one system.
For example, agentic AI can organize “theranostic” visits in cancer care. This means patients get tests and treatments in the same visit. Diagnostic tests like MRIs happen alongside chemotherapy or radiation. This helps use resources better, reduces patient visits, and speeds up care.
Agentic AI follows healthcare rules for data sharing like HL7, FHIR, HIPAA, and GDPR. These rules keep data safe and let systems talk to each other. This reduces mistakes and makes care safer for patients.
Good workflow is very important in running healthcare facilities. Agentic AI helps administrative workers by automating tasks like scheduling, prioritizing urgent cases, and managing appointment loads. This improves how hospitals and clinics use their space and staff.
Reactive agents in AI can set up appointments for tests based on how urgent a case is. For example, if tests show fast disease growth, the system books an MRI or biopsy quickly. Compatibility agents also check if there are any problems, like if a patient with a pacemaker can safely have an MRI. This keeps patients safe during automatic scheduling.
Cloud technology supports these AI systems by offering secure and fast data handling. Services like AWS provide the computing power and storage needed. This helps IT managers run AI tools while protecting patient privacy and following rules.
These automations help in two ways: they reduce the workload on staff by cutting scheduling mistakes and delays, and they improve patient experience by making sure appointments happen on time. This is important since many cancer patients miss important care.
Even though agentic AI automates many tasks, humans must still be involved. This is called the human-in-the-loop approach. Doctors and healthcare workers check AI suggestions to make sure they are correct and safe.
This review helps stop problems like AI making up false information. Humans can correct or reject suggestions as needed. It also makes the AI process transparent and keeps care safe. This is important to follow healthcare laws and to build trust in AI.
Leaders like Dr. Taha Kass-Hout from GE HealthCare and Dan Sheeran from AWS say agentic AI is starting to change healthcare. Their companies use cloud computing and AI systems to fix problems of too much data and split systems.
Dr. Kass-Hout says agentic AI helps break down barriers between departments, leading to more connected care for patients. Dan Sheeran adds that these AI systems help doctors spend less time on admin tasks and more time with patients.
GE HealthCare and AWS research groups work together to use agentic AI in cancer care. Their tools combine clinical data, lab results, pathology, and imaging with cloud systems to plan treatments better and schedule care. These work directly for administrators and IT staff who want better clinic operations and patient results.
In the future, agentic AI may link real-time images with personalized treatment amounts, like radiation doses. This will help make treatments more exact and safer. AI scheduling tools will keep improving too, managing resources automatically and handling changing patient needs.
The ability of agentic AI to scale and handle errors will help both small clinics and big hospitals use coordinated care systems.
Healthcare managers and IT leaders will need to invest in agentic AI and strong cloud systems to stay efficient and provide patient-focused care.
Agentic AI systems have the potential to solve problems caused by too much information and separated systems in today’s healthcare. They help connect complex workflows while keeping safety standards high. In the U.S., with growing care demands and complex work, these technologies will play a bigger role in making healthcare more efficient, reducing doctor burnout, and improving 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.
