Roughly two-thirds of physicians utilize artificial intelligence (AI). More than half of doctors employ the technology to reduce administrative burdens through automation. Others use it to improve patient outcomes, reduce costs and optimize the overall quality of care.
That much is easy to comprehend. What’s often harder to discern is the differences between the functionalities of varying types of AI. For example, machine learning functionality plays a crucial role by analyzing vast amounts of patient data, whereas natural language processing (NLP) makes AI interactions more intuitive and human-like.
Add to the AI puzzle agentic and generative AI. Both functionalities are becoming more commonplace in the healthcare industry but don’t utilize the same processes.
Although generative AI is more widely used, its agentic counterpart is experiencing rapid growth. Gartner estimates that 33 percent of enterprise software applications will include agentic AI by 2028 —up from less than one percent in 2024.
Understanding Agentic AI
Agentic AI is characterized by its ability to operate autonomously and solve complex, multi-step problems without human intervention. Unlike traditional AI models that require constant supervision and input, agentic AI systems analyze challenges, develop strategies and execute tasks independently.
This autonomy enables healthcare providers to focus on more complex tasks instead of repetitive and time-consuming administrative processes. It also allows them to allocate resources more effectively, ensuring timely and accurate patient care.
The core technologies of agentic AI include natural language processing (NLP), machine and reinforcement learning and knowledge representation. The four-step process that enables these AI systems to process data and operate independently with minimal human intervention and automate complex workflows includes:
- Perception
- Complex reasoning
- Acting
- Learning
Reinforcement learning plays a pivotal role in how agentic AI operates. This component allows AI agents to continuously improve their decision-making abilities through feedback, optimizing workflows and processes autonomously. This continuous learning ensures that the AI system remains effective and up-to-date with the latest medical practices and standards.
Agentic AI serves as a framework within which AI agents operate. These agents are the components that execute tasks and make decisions based on the framework’s guidelines. An AI-powered agent in a medical group might handle scheduling appointments, disseminating patient education or answering common inquiries.
Understanding Generative AI
Generative AI is a type of AI designed to create content (i.e., text, visuals, audio) by learning to identify patterns from existing data. One of the main characteristics of generative AI is its ability to generate synthetic data, which enhances training sets and allows AI models to improve and diversify their learning processes. This capability is particularly valuable in healthcare, where diverse and comprehensive training data is essential for developing accurate and reliable AI models.
Generative AI is also known for its adaptability to new information and changes in user preferences. This adaptability makes it an excellent tool for applications such as patient outreach, where AI models can generate content tailored to individual users’ preferences. For example, a generative AI system might create personalized health and wellness recommendations based on a patient’s medical history and lifestyle data.
Agentic AI vs. Generative AI
Agentic and generative AI serve different purposes and excel in distinct areas. Agentic AI is designed for proactive problem-solving, capable of making decisions and executing tasks independently. In contrast, generative AI primarily focuses on content creation based on existing data patterns.
One of the key distinctions between agentic AI and generative AI is their approach to autonomy. Agentic AI operates autonomously, making decisions and taking actions without continuous human input. Conversely, generative AI typically reacts to user prompts and requires human oversight to ensure the quality and relevance of the generated content.
Generative AI tools can produce personalized responses and content, enhancing patient interactions and outreach, but they often require human input to guide their outputs and ensure they meet the desired standards. Agentic AI’s autonomous agents can understand patient intent and emotions, leading to more meaningful interactions and allowing healthcare providers to more effectively infer intent.
Understanding the differences between agentic and generative AI can help healthcare providers make informed decisions about AI adoption. By leveraging the unique strengths of each technology, providers can enhance patient care, streamline operations and improve efficiency.
Built upon both generative and agentic AI principles, agentic AI is utilized by healthcare providers to streamline numerous tasks, including patient scheduling, post-discharge support, medication management, multilingual support collections and more. It combines advanced automation, AI and NLP to enable comprehension of and response to human language.
At Providertech, our agentic AI solution is designed to understand the way patients communicate and adapt accordingly. By meeting healthcare consumers’ expectations for a seamless experience, we produce happy patients every single call, every single time. Listen to a sample recording of Providertech.ai, or check out our video guide on choosing the best AI solution.
