Large language models are AI programs trained on a lot of text to produce human-like language. In healthcare, LLMs help with tasks like writing notes, talking to patients, and giving basic advice. But they have some limits when it comes to tough medical areas like rheumatology.
First, LLMs have information that is updated only sometimes when they are retrained. They can’t check real-time patient data or the newest medical rules right away. This means they can’t quickly adjust to changing patient needs. Another problem is “hallucination” — sometimes they give wrong or confusing answers because they predict words by chance. This can be risky when giving medical advice.
Also, LLMs cannot directly work with medical tools or databases. Rheumatology needs steps that connect tests, scans, and patient history to change treatment plans. LLMs can’t do these step-by-step processes or use tools like electronic health records (EHRs) by themselves.
Some improvements like retrieval-augmented generation (RAG) help bring in extra data during questions. But they don’t fully fix these problems. RAG can find relevant info, but it can’t handle multi-step tasks or keep working through complex cases like those in rheumatology.
Agentic AI systems are more advanced. They add features like planning, memory, and the ability to use outside software and tools. In rheumatology, this means agentic AI can do many steps on its own or with help, use live patient data, and change decisions as new information comes.
Agentic AI works by joining language models with planning and memory parts. Memory helps it remember past talks and patient details. Planning lets it follow several steps such as reading test results, using guidelines, and changing treatments when needed.
Agentic AI can also connect to external tools and apps. It can pull data from EHRs, check drug interactions, or manage schedules without needing humans to help. For example, it can take lab results, compare them with the newest studies, and give clear treatment advice for each patient.
Rheumatology has special challenges that regular LLMs cannot handle well. Patients with autoimmune diseases like arthritis or lupus need doctors to look at many signs, symptoms, tests, and images over time. Treatment often follows steps that change based on how the disease acts and how the patient responds. This needs close watching and flexible plans.
Agentic AI fits here because it can:
Research from the European Alliance of Associations for Rheumatology shows agentic AI can help make medicine more precise by moving from chat-style help to active agents that can take clinical actions. This change means AI can better handle rheumatology’s fast and data-heavy work.
Use cases soon include automating the review of new research to keep doctors updated, planning personalized treatments from patient data, and smart tools that make decision-making easier and more accurate.
Even with good potential, using agentic AI in U.S. rheumatology faces big challenges. Rules must keep patients safe, protect their data, and clarify who is responsible. The Food and Drug Administration (FDA) oversees AI tools used for clinical decisions. They want proof that the tools work well, plans for handling risks, and ways to check how AI performs over time.
Ethical issues include guarding patient privacy, getting informed consent, reducing unfair bias in AI advice, and keeping humans in charge to stop errors from AI decisions. Being clear about how AI works helps build trust for both doctors and patients.
On the tech side, agentic AI must fit smoothly with many types of EHR systems used in U.S. healthcare. The systems must be reliable, easy to understand, and able to learn more without risking data security. Making sure different systems work together, testing regularly, and updating software often are needed for safe use.
In U.S. rheumatology clinics, AI can help run daily work better while also improving patient care. Agentic AI’s skill in handling complex tasks and tools can automate routine but important jobs.
For example, agentic AI can answer patient phone calls, schedule appointments, gather info before visits, and do initial symptom checks. These front-office tasks ease the work for staff and help patients get care faster. Some companies offer AI phone answering and automation services. Using AI for these tasks helps clinics handle more calls, give consistent answers, and lets staff focus on harder jobs.
On the clinical side, agentic AI can help write notes by pulling important info from patient talks, labs, and scans. It can watch patient data over time and alert doctors of big changes. This helps with early treatment changes, which is key since rheumatology diseases can go up and down.
Agentic AI can also help medical staff by bringing in new rules and research and mixing it into care plans. This means doctors don’t need to spend lots of time reading all the studies themselves. This helps doctors use the best evidence and lowers their mental load.
For AI to work well in these ways, IT staff, doctors, and office teams need to work closely. Training and managing change are important to get the most from AI while keeping transparency, trust, and safety.
Clinic administrators and IT managers in the U.S. should carefully check how ready their setups are for AI. Large language models work well for less risky tasks like talks with patients and office help. But rheumatology needs smarter and flexible AI — like agentic AI.
To use agentic AI, they should think about:
With the right setup, agentic AI can lower doctor workload, help make better diagnoses, and support care that fits patients better. Automating front-office work like calls and scheduling also helps the clinic run smoother.
Rheumatology in the U.S. needs smart tools that handle tough, fast-changing info. Large language models help with AI-assisted care but can’t keep up with complex, multi-step reasoning or work with tools in real time.
Agentic AI builds on LLMs by adding planning, memory, and tool use. This lets it handle harder workflows that match real-world clinical needs. There are still challenges with rules, ethics, and technical setup. But agentic AI shows strong promise to improve rheumatology care with personalized plans, automatic research updates, and decision help.
For administrators, owners, and IT managers, knowing these differences is key to choosing the right AI that works for both care and clinic needs. AI automation, like phone answering by companies such as Simbo AI, is a good start to improve office work while clinics get ready for more advanced AI in care.
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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