AI agents are different from regular AI assistants because they can work on tasks by themselves. They do not need someone to ask them step-by-step. Instead, they can get a general instruction and figure out how to finish the job on their own. This ability comes from advances in large language models (LLMs) and their connection with other software and data sources.
In 2025, a survey by IBM and Morning Consult found that 99% of 1,000 developers working on AI for businesses are either learning about or making AI agents. This shows that many believe AI agents will have a big impact on how businesses work, including healthcare.
For clinics and hospitals in the U.S., AI agents help with front-office jobs like answering phones and scheduling patients. Some companies, like Simbo AI, provide phone automation services that use AI. This helps doctors’ offices reduce wait times for patients and manage staff better.
AI in healthcare does more than just automate simple tasks. These AI systems can assist with medical decisions, diagnostics, planning treatments, and monitoring patients. They use different kinds of information like medical records, scans, and lab results.
Research from ScienceDirect shows that next-level AI agents use probability and can adapt to make better clinical decisions. They learn from new information all the time and improve their suggestions. This helps lower errors and allows care to be more personal.
In office work, AI agents can handle workflows like insurance claims, appointment reminders, and follow-ups with patients. By taking care of routine tasks, AI lets healthcare workers spend more time with patients and handle more important tasks.
However, AI does not replace human judgment. Doctors and staff are still responsible for the final decisions. Keeping humans involved is important to maintain safety and ethics.
One clear benefit of AI agents in healthcare is that they help automate daily tasks, especially at the front desk. Services like those from Simbo AI can answer calls around the clock. They manage appointment requests, questions about prescriptions, and general patient inquiries without needing a person at every step.
Using AI for phone calls cuts down on missed calls and long wait times, which can make patients unhappy or cause them to miss appointments. These AI agents use natural language processing (NLP) to understand patient needs and securely access scheduling systems to book or change appointments. This lets office staff focus on more complex work.
Automation extends to handling patient data and billing too. AI can check insurance approvals, verify coverage, and find missing information. This speeds up payment processes and lowers the workload for billers and coders.
IBM’s Vyoma Gajjar says AI is good at handling simple, repetitive tasks that often slow down medical offices. Moving these tasks to AI lets people focus on decision making and improving patient care.
Using AI agents widely needs careful management. Healthcare data is sensitive and protected by laws such as HIPAA in the U.S. AI systems that handle patient info must keep it private and follow strict rules.
Experts including those at IBM say AI systems need to have ways to undo mistakes, keep detailed records, and be clear about how they work. For example, if AI accidentally deletes important patient data, the system should be able to reverse the mistake and find out what caused it.
Maryam Ashoori from IBM says many organizations are not ready to use AI agents yet. A big challenge is setting up data properly with APIs so AI agents can work well with existing systems. This means electronic health records (EHR) and other software need to work smoothly with AI tools.
Governance also involves addressing bias and fairness. Because AI learns from large data sets, it can inherit biases that affect patient care or fairness in administration. People from different fields like medicine, management, law, and data science need to work together to build ethical AI systems.
One main rule about AI agents in healthcare is that humans stay in control. AI is made to help people, not take the place of doctors or office staff.
Experts at IBM, like Marina Danilevsky and Maryam Ashoori, say AI is a tool to support human decisions, especially in complicated or important cases. Humans check what AI suggests, make final decisions, and step in if needed. This keeps a balance between using technology efficiently and being responsible.
Across medical offices in the U.S., having an IT manager or clinical leader oversee AI use prevents problems and makes sure the technology helps work go smoothly. Watching AI actions and keeping audit logs makes things clear. This is very important when dealing with patient care and private data.
The healthcare system in the U.S. is complex, highly regulated, and varies widely. This affects how AI agents are added to medical offices, hospitals, and admin offices.
Most U.S. healthcare providers use electronic health records and digital communication. This creates a base for adding AI. But because of different sizes and tech levels, adoption varies greatly between small clinics and big hospitals.
Smaller offices can quickly use AI for front-office tasks, which reduces the need to hire more staff and gives patients 24/7 access. Big health systems might use AI for clinical decisions and managing workflows between departments.
Healthcare managers and IT staff must check how well their data systems and software can work with AI agents. IBM’s Chris Hay calls this checking for “agent readiness.” AI works best when data is organized well and APIs let AI connect smoothly with key software.
Benefits:
Challenges:
The year 2025 is seen as a time when many are trying out AI agents in healthcare. Technology keeps changing quickly, and healthcare workers in the U.S. are starting to see real benefits in automating work and helping with clinical support.
Open source AI models may help create new and customized healthcare solutions. This can allow practices of all sizes to use AI without big costs, which is important in a diverse market like the U.S. with many different types of patients and resources.
In the end, AI agents will change healthcare offices by automating simple tasks, improving accuracy, and helping decisions. But humans will stay in charge and responsible. Companies like Simbo AI already show easy-to-use automation tools that work well in medical offices.
By learning about how AI agents work and using them responsibly, healthcare leaders and IT teams in the U.S. can make their organizations run better and provide better care to patients. The key is to add AI carefully, follow rules, and keep human control at the center of every step.
An AI agent is a software program capable of autonomous action to understand, plan, and execute tasks using large language models (LLMs) and integrating tools and other systems. Unlike traditional AI assistants that require prompts for each response, AI agents can receive high-level tasks and independently determine how to complete them, breaking down complex tasks into actionable steps autonomously.
AI agents in 2025 can analyze data, predict trends, automate workflows, and perform tasks with planning and reasoning, but full autonomy in complex decision-making is still developing. Current agents use function calling and rudimentary planning, with advancements like chain-of-thought training and expanded context windows improving their abilities.
According to an IBM and Morning Consult survey, 99% of 1,000 developers building AI applications for enterprises are exploring or developing AI agents, indicating widespread experimentation and belief that 2025 marks the significant growth year for agentic AI.
AI orchestrators are overarching models that govern networks of multiple AI agents, coordinating workflows, optimizing AI tasks, and integrating diverse data types, thus managing complex projects by leveraging specialized agents working in tandem within enterprises.
Challenges include immature technology for complex decision-making, risk management needing rollback mechanisms and audit trails, lack of agent-ready organizational infrastructure, and ensuring strong AI governance and compliance frameworks to prevent errors and maintain accountability.
AI agents will augment rather than replace human workers in many cases, automating repetitive, low-value tasks and freeing humans for strategic and creative work, with humans remaining in the decision loop. Responsible use involves empowering employees to leverage AI agents selectively.
Governance ensures accountability, transparency, and traceability of AI agent actions to prevent risks like data leakage or unauthorized changes. It mandates robust frameworks and human responsibility to maintain trustworthy and auditable AI systems essential for safety and compliance.
Key improvements include better, faster, smaller AI models; chain-of-thought training; increased context windows for extended memory; and function calling abilities that let agents interact with multiple tools and systems autonomously and efficiently.
Enterprises must align AI agent adoption with clear business value and ROI, avoid using AI just for hype, organize proprietary data for agent workflows, build governance and compliance frameworks, and gradually scale from experimentation to impactful, sustainable implementation.
Open source AI models enable widespread creation and customization of AI agents, fostering innovation and competitive marketplaces. In healthcare, this can lead to tailored AI solutions that operate in low-bandwidth environments and support accessibility, particularly benefiting regions with limited internet infrastructure.
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.
