Rheumatology is a medical field that focuses on diagnosing and treating problems with joints, muscles, and bones. Many patients have autoimmune and inflammatory diseases, like rheumatoid arthritis and lupus. Doctors in this field need to carefully and continuously check symptoms, lab tests, images, and patient histories. This makes the work complex and full of data. In the U.S., there is a growing need for rheumatology services but fewer rheumatologists are available. This creates a strong need for technology help in medical care.
Artificial intelligence (AI) has brought new tools to help with decisions, paperwork, and patient care. Two types of AI gaining notice are large language models (LLMs) and agentic AI systems. This article compares these two types in how well they handle changing tools and multi-step clinical tasks in U.S. rheumatology.
The American College of Rheumatology (ACR) says there will be fewer rheumatologists in the future. By 2030, the number will drop by 31%, while the demand for care will go up by 138%. Over half of current rheumatologists are close to retirement. This shortage is worse in rural areas where 72% of the counties do not have a practicing rheumatologist. This makes it hard for many patients to get proper care.
Patients with chronic inflammatory rheumatic diseases (CIRDs) usually wait about 18 months for a diagnosis. During this time, they often see many different specialists. Almost half of patients see two or more doctors who are not rheumatologists, and 21% see four or more before getting a referral. This delay can harm patients and increase medical costs. For example, people with rheumatoid arthritis spend over $4,000 more each year because of late diagnosis.
Many rheumatologists feel burned out. About 50.8% report high stress and exhaustion. Tasks like documentation and scheduling add a lot to their workload.
In this situation, AI’s role in helping rheumatology care is growing. Large language models and agentic AI offer different ways to help.
Large language models, like GPT-based systems, can understand and create natural language well. They are trained on large amounts of text. They can answer clinical questions, summarize patient data, or write medical notes.
Some methods combine LLM text with data from outside sources (retrieval-augmented generation) to update information. Though helpful, this still does not fully support real-time tool use, memory over time, or complex decision-making.
Agentic AI goes beyond regular LLMs. It mixes language skills with independent planning, memory use, and the ability to connect with many external tools and databases. This is useful in rheumatology because the work needs multi-step thinking and combining information from different places.
Key features of agentic AI include:
In rheumatology, agentic AI can spot small disease changes, predict flare-ups, and suggest treatment changes better than static LLMs.
Agentic AI can be used in several ways in rheumatology:
These uses are important since there are fewer doctors and more patients needing care.
Even with its promise, agentic AI faces several challenges:
For medical office managers, AI can affect many parts of how care is delivered, especially in rheumatology, where managing patients takes a lot of coordination.
Using AI this way can save money by making work faster and reducing overtime. It also helps with paperwork needed for payment and quality checks.
IT teams need to build safe, compatible AI tools that protect patient privacy and can handle data quickly. Training staff on these tools is important for smooth use.
The rising need for rheumatology care amid fewer doctors calls for new technology support. Agentic AI can manage complex tasks that are hard for doctors alone. LLMs help mostly with reading and writing.
For U.S. healthcare leaders, it’s important to look not only at language ability but at how AI works with existing systems, automates tasks, and offers real-time medical help. Since over 70% of U.S. counties lack rheumatologists, AI-powered telehealth and virtual care can bring help to patients in rural areas.
Cutting down diagnosis time can reduce costs linked to late-stage disease problems by more than $4,000 a year per patient. Better doctor efficiency can also lower burnout and keep doctors working longer, saving money for clinics.
Using agentic AI well needs planning that includes ethics, laws, training, and ongoing checks on how AI performs.
Large language models and agentic AI have different roles in U.S. rheumatology. LLMs can generate useful text, but agentic AI works independently on complex tasks and uses tools in a way that matches the needs of rheumatology care. Medical leaders should carefully consider these differences to make the best use of AI for better patient care and clinic operations.
Current LLMs have static knowledge and risks of hallucination, limiting their ability to handle complex, real-time rheumatologic care demands such as multistep reasoning and dynamic tool usage.
Retrieval-augmented generation helps mitigate some limitations of LLMs by incorporating relevant external information, but it still falls short for complex, real-time clinical scenarios in rheumatology.
Agentic AI extends LLMs by adding planning, memory, and the ability to interact with external tools, enabling the execution of complex, multi-step tasks beyond mere text generation.
Agentic AI combines LLM capabilities with memory management, planning algorithms, and API/tool interactions to dynamically handle complex workflows and real-time data integration.
Agentic AI is used in personalized treatment planning, automated literature synthesis, and clinical decision support, enhancing precision and efficiency in patient care.
Rheumatologic care requires real-time data access, multistep reasoning, and tool usage—complexities that agentic AI systems are uniquely designed to manage.
Agentic AI enables dynamic integration of patient data, literature, and clinical guidelines to tailor individualized treatment plans more accurately and adaptively.
Regulatory, ethical, and technical challenges must be addressed, including ensuring safety, data privacy, accountability, and managing the risks of automated decision-making.
Agentic AI can continuously retrieve and analyze new research, summarize findings, and integrate insights into clinical recommendations to support evidence-based practice.
Memory enables agentic AI to retain and utilize information from past interactions, supporting multistep reasoning and consistent decision-making over time.
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