AI agents in healthcare

They are becoming an important technology in the healthcare industry as organizations look toward AI systems that can respond to change faster and more efficiently.

AI tools are already widely used across healthcare to analyze data and optimize how staff, resources and facilities are allocated. These systems increasingly include generative AI capabilities, such as large language models (LLMs), which allow users to interact with data and systems through natural language.

Agentic AI goes further. Unlike traditional AI or rule-based automation, AI agents are designed to operate with a degree of independence (agency) while still working within defined boundaries set by an organization. This capability is driving broader adoption, with Gartner predicting that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.¹

AI agents combine multiple AI capabilities, including advanced cognitive abilities, machine learning and natural language processing (NLP), into systems that can perceive information, reason and drive outcomes. Advances in generative AI models, combined with agentic frameworks for planning and execution, have accelerated the development of AI agents by enabling them to understand instructions, generate insights and coordinate multi-step tasks.

Unlike traditional AI or rule-based algorithms, AI agents can adapt to changing conditions and operate across complex environments such as healthcare systems. By integrating with enterprise systems and clinical tools, including mobile apps, they help improve operational efficiency even as conditions shift quickly.

At a basic level, an AI agent gathers information from its environment and builds an understanding of context. It then evaluates possible actions, plans next steps and decides how to proceed. This reasoning process allows the AI agent to break down goals into tasks and adjust its approach as new information becomes available.

When a plan is formed, the AI agent runs actions through connected systems or by interacting with people. Feedback from these actions can be used to improve future performance. Together, these capabilities allow AI agents to function as active participants in workflows rather than passive tools that merely generate outputs.

Within healthcare, AI agents are becoming an important technology. They are designed to reason across complex healthcare data and pursue defined clinical or operational goals while respecting safety, regulatory and governance constraints.

In practice, AI agents are already being applied across a range of healthcare activities. They assist clinicians with documentation, surfacing relevant patient information and helping manage follow-up tasks. On the operational side, healthcare AI agents coordinate scheduling, staffing and resource allocation, and can adapt plans as conditions change. Patient-facing AI agents handle appointment management, intake and ongoing communication.

AI agents operate within boundaries defined by healthcare providers, clinical standards and regulations, including HIPAA. These guardrails allow them to responsibly respond to change faster than systems that rely entirely on human intervention.

As these systems evolve, gen AI technologies like large language models enable more natural interaction with data and workflows. This progression sets the stage for AI agents to become more deeply embedded across healthcare ecosystems rather than confined to individual use cases.

AI agents signal a structural shift in how healthcare work is organized and run. Instead of supporting isolated tasks, they can plan, coordinate and act across clinical, operational and administrative workflows. This transition shifts how value is created in healthcare, moving organizations away from fragmented processes toward more integrated and adaptive systems.

Their growing role reflects the increasing complexity of modern healthcare. Health and wellness systems must expand data volumes, workforce shortages and rising expectations for access and quality. As AI in healthcare continues to mature, AI agents help scale clinical decision-making and execution beyond human capacity, making them especially important as demand for care continues to outpace available resources.

AI agents also challenge long-standing assumptions about professional roles and accountability. As they take on more autonomous responsibilities, clinicians and administrators must rethink how they supervise, trust and collaborate with software systems. More than half of healthcare executives (53%) say that cybersecurity and patient data protection are their greatest challenges.²  AI-driven actions influence patient outcomes, raising important questions about oversight and responsibility and shaping how widely these systems are adopted.

AI agents are also accelerating the pace of healthcare innovation. By generating insights, coordinating tasks and learning over time, they shorten the gap between identifying a problem and acting on it. While this dynamic can speed improvement, it also increases the risk of misalignment with clinical standards or organizational readiness, making governance and change management essential.

AI agents put pressure on healthcare systems to evolve. They reveal gaps in processes, skills and leadership that must be addressed for adoption to succeed. Whether AI agents deliver lasting value will depend less on the technology itself and more on how healthcare organizations adapt their people, structures and norms.

AI agents function as connective tissue across healthcare systems. Unlike single purpose tools, their value comes from coordinating actions and maintaining context across workflows that would otherwise require significant manual effort. The following use cases illustrate how healthcare organizations are beginning to apply or piloting AI agents:

AI agents are often deployed in multi-agent systems to manage care pathways that span multiple teams and systems. This approach often involves AI agent orchestration, where multiple agents coordinate tasks, share context and manage dependencies across clinical and operational workflows.

For example, one agent can manage referrals. Another can monitor test completion. A third can prompt staff or patients when next steps are required. Together, these agents keep treatment plans moving without manual intervention at every step.

AI agents assist clinicians by generating consultation summaries, updating electronic health records (EHRs) and managing follow-up administrative tasks. For example, an AI agent can listen to a patient interaction, draft clinical notes and route them for review while also placing orders or scheduling follow-ups. This automation of routine data entry streamlines documentation workflows and helps clinicians stay focused on patient care.

Some AI-powered agents assist clinicians by synthesizing patient data and analyzing medical imaging such as MRIs and X-rays, particularly in specialties like radiology and oncology. Rather than deciding independently, these AI agents surface risks, guidelines and patterns that support human judgment, improve diagnostic accuracy and enable more personalized treatment decisions.

AI agents support drug discovery and development by coordinating data analysis, simulation and research workflows across large and complex datasets. They can help identify promising compounds, manage clinical trials and adapt research plans as new findings emerge. As AI in medicine continues to advance, these agents enable faster iteration and improved efficiency and collaboration across the drug development lifecycle.

Hospitals use AI agents to support scheduling, staffing and capacity management. An AI agent might adjust staffing plans based on predicted patient volume or identify bottlenecks in operating room schedules. These abilities allow healthcare professionals and organizations to respond more dynamically to changing demand.

A recent IBM study found that 69% of health care executives expect AI to enhance their ability to adapt to changing clinical demands, including quicker responses during future public health crises. ²

AI agents power virtual assistants, chatbots and other tools that handle appointment scheduling, intake and basic patient communication. These systems often use conversational AI to interact naturally with patients and collect symptoms, answer questions and manage follow-ups. This ongoing interaction improves access and enhances the overall patient experience.

AI agents monitor patient populations to identify emerging risks or gaps in care. Using predictive analytics, an AI agent can continuously scan records to flag patients showing early signs of deterioration or conditions such as kidney disease. Important real-time insights resulting from patient monitoring can trigger outreach or escalation to care teams for review.

An IBM study found that 4 in 10 healthcare executives already use AI for inpatient monitoring and to provide early warnings about patient health issues.²

AI agents support administrative workflow automation such as billing, coding and prior authorization processes. For instance, an AI agent can gather required documentation, submit authorization requests and follow up with payers. By managing these multi-step administrative processes, AI agents reduce delays and lost revenue.

In the IBM study, 34% of healthcare executives reported applying AI in revenue and budget cycle management. Meanwhile, 67% see the greatest opportunity for AI in enhancing payer-provider coordination and claims integrity.²

AI agents offer a range of benefits that extend beyond individual use cases. Their impact is felt across clinical, operational and organizational dimensions of healthcare, shaping how work is performed and how value is created.

Better coordination across systems: Healthcare work is often fragmented across tools, teams and departments. AI agents can maintain context and coordinate actions across these boundaries, reducing delays and miscommunication. One-third (34%) of healthcare executives expect AI to help improve productivity. They see its value in coordinating multidisciplinary healthcare teams within departments and between hospitals.²

Continuous improvement over time: With appropriate governance, AI agents can learn from outcomes and feedback. This capability allows healthcare organizations to refine processes dynamically rather than relying solely on static workflows.

Faster response to change: AI agents shorten the time between identifying an issue and acting on it. Their ability to plan and execute tasks enables quicker adjustments to clinical and operational conditions.

Less administrative burden: By taking on documentation, coordination and routine tasks, AI agents reduce the non-clinical workload placed on clinicians and staff. This supports focus on higher-value work and helps reduce burnout.

More consistent standards: AI agents can apply guidelines, policies and rules in a reliable way. This approach helps reduce unwanted variation while supporting quality and safety efforts at scale.

Ongoing patient engagement: AI agents make it possible to support patients outside traditional care settings. Continuous interaction enables better follow-up, monitoring and adherence between visits.

Scalable decision-making: AI agents allow healthcare organizations to extend decision support across growing volumes of data and complexity. They help teams make informed choices at scale without relying solely on human attention and availability.

Support for long-term transformation: AI agents do more than optimize existing workflows. They provide a foundation for redesigning roles, processes and organizational structures as healthcare systems evolve.

As AI agents mature, their impact in healthcare will shift from task execution to system-level coordination. Rather than supporting individual workflows, future agents will increasingly manage how work moves across clinical, operational and administrative domains.

In drug discovery and development, AI agents will coordinate data analysis, simulation and experimentation across research workflows. They will adapt research plans as results emerge, helping organizations move from insight to implementation more quickly.

Future AI agents will operate continuously instead of responding to prompts or events. They will maintain a long-term context about patients, workflows and organizational goals. This awareness will allow them to anticipate needs, adjust plans and intervene earlier.

AI agents will be trusted with broader responsibility for planning and execution. However, they’ll remain constrained by clinical rules, governance policies and human oversight. This balance of autonomy and control will reshape how clinicians and administrators interact with technology.

Healthcare will increasingly rely on networks of agents working together across departments, systems and even organizations. These multi-agent environments will enable coordination that is difficult to achieve through human effort alone, especially as care becomes more distributed.

The future of AI agents will depend on strong governance frameworks that address safety, transparency and responsible AI in healthcare. Organizations that invest early in these foundations will be better positioned to scale agentic systems without increasing risk.