Agentic AI, you might have heard about it; now it is appearing everywhere. Hundreds and thousands of blogs, microblogs on social platforms are being written about it. It is a new big thing emerging in the AI field, influencing almost every industry, including the healthcare sector. And its adoption and interest are high. Gartner predicts usage will surge by 2028, it was just 1% in 2024, and the market will reach around $200 billion in 2034.
Agentic AI offers healthcare professionals and organizations the ability to automate complex workflows. Yet human oversight is crucial.
This blog explores how agentic AI is reshaping healthcare. We will also know how it solves several challenges and improves healthcare services.
What Is Agentic AI and How Is It Reshaping Healthcare?
Agentic AI is an advanced artificial intelligence system that includes multi-step reasoning. Unlike traditional AI, it can make decisions with minimal human oversight. Agentic AI goes one step further and can make better and more accurate decisions.
How?
Generative AI uses data from LLMs to produce results and is also dependent on the prompts used by users. Agentic AI pulls information from various sources. It involves multi-layered reasoning to conclude tasks.
Let's say you run a clinic; here's how generative AI and agentic AI can work together. Generative AI tech can look for patient records and then write instructions accordingly.
Agentic AI can take this one step further. It can check if the patient has read the instructions. If they haven’t, it sends a friendly reminder, like, “Hey, check your care plan!”
- The generative AI saves your staff from writing instructions.
- The agentic AI makes sure patients follow the plan and get help fast.
What's Special About Agentic AI
- It operates independently
- It requires minimal human input
- Breaks down complex problems into manageable steps. Agentic AI is mainly used as an automation framework in business settings.
How Agentic AI Addresses Data Overload in Clinical Settings
Healthcare professionals can easily find themselves submerged in data. They are overwhelmed, even when working as a team. Agentic AI can help make sense of whole datasets at once with speed, precision, and accuracy. Therefore allowing clinicians to focus on the important tasks.
Reducing Manual Workload
Agentic AI systems are capable of simultaneously searching patient records. For example, agentic AI can search through and process data related to patient conditions in just seconds. It can summarize relevant literature in a matter of hours.
Capabilities:
- Real-time Processing
- Pattern Recognition
- Contextual Analysis Integrate Multiple Data Streams Agentic AI is well-suited for synthesizing electronic health records (EHRs), wearables, devices, and research databases. Multimodal AI also applies to combining text, images, and numerical data to provide more valuable insights than single-source systems. For instance, using wearable data alongside EHRs will provide a more nuanced sense of changes in vitals and provide clinicians with an added level of early intervention for scenarios like heart failure.
Examples of Insights:
- Predicting disease progression by combining genetic data with lifestyle factors.
- Identifying drug interactions by cross-referencing patient records with pharmacological databases.
Core Use Cases of Agentic AI in Healthcare
Agentic AI is transforming healthcare through practical, high-impact applications. Here are the primary use cases driving change.
Diagnosis and Clinical Decision Support
Agentic AI improves clinical decision support systems. It can evaluate symptoms, test results, and medical literature to provide evidence-based recommendations. For example, it can suggest differential diagnoses for uncommon diseases.
Diagnostic Applications:
- Oncology: Identifies cancer subtypes from imaging and genomic data.
- Cardiology: Detects arrhythmias from ECGs with 95% accuracy.
- Neurology: Flags early signs of neurodegenerative diseases in imaging. Patient Monitoring and Engagement Agentic AI powers remote monitoring through IoT devices and wearables. Thereby detecting subtle changes in patient health. For example, AI agents can alert clinicians to irregular heart rhythms.
Benefits:
- Continuous monitoring for chronic conditions like diabetes.
- Tailored educational content to boost patient engagement.
- Early detection of complications, reducing emergency visits.
Drug Discovery and Research
Agentic AI can accelerate drug discovery by analyzing large datasets efficiently. It simulates drug interactions through specialized hardware and predicts potential side effects. Additionally, agentic AI can review existing content to identify previously overlooked research approaches and molecular collisions that may cause unexpected drug interactions.
Applications:
- Identifying novel drug targets for Alzheimer’s.
- Simulating protein interactions for precision medicine.
- Analyzing clinical trial data for faster insights. Workflow Automation and Administration Agentic AI reduces administrative workload. This means less burnout for healthcare professionals. It can be used for automated scheduling, automated documentation, and streamlining the processing workflow.
Robotic-Assisted Treatments
Agentic AI can enable robot-assisted surgery, providing improved precision and safety. For example, systems like the da Vinci Surgical System improve the likelihood of fewer complications in procedures like prostatectomies and orthopedic surgeries with placement of implants and cardiac procedures with improved accuracy.
How to Implement Agentic AI in Healthcare Systems
Healthcare organizations can adopt agentic AI by following a structured roadmap.
1. Assess Existing Infrastructure
Assess current processes to identify the gaps that would benefit from agentic AI. Some guiding questions are: Which processes are the most time-consuming? Where in the process are errors most likely to happen? Be sure that the infrastructure supports real-time data processing and integration with EHRs.
2. Define Clear Objectives and Use Cases
Set measurable goals. You can set it like you can reduce diagnostic errors by 15% or reduce administrative hours by X%. Focus on high-impact use cases like clinical decision support or workflow automation aligned with your organization's priorities.
3. Assemble a Multidisciplinary Team
You will want to assemble a team of clinicians, data scientists, and administrators. Clinicians will be the ones able to ensure clinical relevance for the problems you are working to solve, while the technical experts will handle the integration of AI into your clinical system. Ensure to have regular cross-functional meetings to encourage collaboration.
4. Ensure Data Privacy and Compliance
A data governance framework is very crucial to ensure compliance with HIPAA and any other regulations. As part of the framework, anonymization and encryption of patient data are essential. At a high level, you will also need to make sure that the AI systems will be transparent and explainable.
5. Monitor and Refine Performance
Track key performance indicators like diagnostic accuracy, time savings, and patient satisfaction. You can gather feedback from users to identify pain points. This feedback can be used to improve these systems.
Future Outlook: Emerging Trends and Innovations
Agentic AI is poised to evolve rapidly, transforming healthcare delivery.
Key Trends:
- Multimodal AI Advancements: Improved integration of text, images, and sensor data for richer insights.
- IoT Integration: Enhanced real-time monitoring through connected devices.
- Augmented Reality: AI-guided AR for surgical precision and training.
- Regulatory Evolution: Streamlined FDA approvals for AI tools.
- New Clinical Areas: Expansion into mental health and personalized nutrition.
Conclusion
Agentic AI is poised to change healthcare as it can automate complex workflows. It improves patient outcomes and enhances decision-making precision. It works as an assistant to healthcare professionals so they can focus on core activities.
As generative AI development develops, it will pair with agentic AI for combined new solutions. This technology is going to improve the efficiency and innovation of healthcare further.
The managed adoption of agentic AI will lead to further integration of AI into clinical practice, preparing the future state of healthcare to become more proactive, personalized, and accessible.
With the rapid emergence and integration of multimodal AI in conjunction with IoT sensor solutions, the next ten years will usher in remarkable advancements that will change healthcare for the better, support human connection, and make healthcare as smart as well as more human than it has ever been.
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