America's Healthcare Worker Shortage – and How AI Could Help

The United States is grappling with a nationwide shortage of healthcare workers at a time when patient needs are rising. Hospitals, clinics, and nursing homes are struggling to fill critical positions ranging from doctors and nurses to lab technicians and billing staff. This shortfall, already evident in 2024, is projected to grow in the coming decade as the population ages and many in the current workforce retire or leave due to burnout. Below we explore the scope of the current healthcare worker shortage – which spans clinical and administrative roles – and then examine how agentic AI (autonomous AI systems capable of carrying out tasks with minimal human input) could help alleviate these workforce gaps. We'll look at a range of use cases, from clinical decision support and diagnostics to automated scheduling, documentation, and more, highlighting real examples of AI trials already underway. The goal is to present an informative, accessible overview of this pressing issue and a glimpse into potential tech-driven solutions.

The Scope of U.S. Healthcare Worker Shortages (2024–2035)

States like North Carolina, California, and Illinois face some of the largest projected shortfalls of primary care physicians by 2028, according to a Mercer study. Each number indicates the estimated deficit of doctors in that state (negative values) for key specialties.

Healthcare workforce shortages are impacting almost every region and role. Recent analyses and projections reveal a worrying mismatch between supply and demand for healthcare personnel both now and in the near future. Some of the key areas of shortage include:

Physicians (Doctors)

Many Americans are already finding it difficult to get timely appointments with doctors, especially in primary care. The Association of American Medical Colleges (AAMC) projects a shortfall of up to 86,000 physicians by 2036 if current trends hold. Other estimates that assume no significant increase in training capacity suggest the gap could be even larger – on the order of 187,000 physicians by 2037 according to federal projections. Primary care doctors are in particularly high demand; more than 87,000 additional primary care physicians may be needed by 2037 to meet requirements.

Certain states and specialties are harder hit than others. For example, by 2028 states like North Carolina could be short nearly 1,400 family doctors, and California may be lacking hundreds of OB-GYNs and pediatricians. Rural communities face the greatest challenges – non-metropolitan areas might see a 60% shortage of physicians by 2037, compared to about a 10% shortfall in cities. This doctor deficit spans primary care as well as specialists (from general surgeons to cardiologists and psychiatrists), raising concerns about access to care as the population grows and ages.

Nurses

Nurses form the backbone of the healthcare workforce, but hospitals nationwide report chronic nursing shortages. The COVID-19 pandemic led to unprecedented burnout and departures – one study found the total supply of registered nurses (RNs) in the U.S. dropped by over 100,000 from 2020 to 2021, the largest decline observed in four decades. Although new nurses enter the field each year, it's not enough to keep pace with retirements and growing patient needs. Federal projections indicate a shortage of about 78,600 full-time RNs by 2025 and remaining tens of thousands short through 2030. By 2037, the gap could widen significantly – an estimated 208,000 RN positions may go unfilled if current utilization patterns persist.

Certain states are especially vulnerable. For instance, Washington State might only meet ~74% of its nursing demand by 2035, and other states like Georgia and California could face nurse shortfalls above 15% of need. The strain isn't limited to RNs; licensed practical nurses (LPNs) are also in short supply, with projections of over 300,000 LPN vacancies by 2037 without intervention. These shortages translate to heavier workloads on the nurses who remain, contributing to a vicious cycle of burnout.

Allied Health and Mental Health Professionals

Shortages extend beyond doctors and nurses to a wide range of health professionals. The U.S. has been dealing with a chronic shortage of mental health providers, which has only been exacerbated in recent years. The federal Health Resources and Services Administration (HRSA) estimates the country would need about 50,000 more psychiatrists and 79,000 more psychologists (full-time equivalents) by the mid-2030s just to meet current levels of demand. When considering unmet mental health needs (people who need care but currently aren't receiving it), the true provider gap is far larger – for example, over 113,000 additional psychologists would be required to fully address unmet needs by 2037.

Other specialties face similar issues. The aging population is driving demand for therapists, optometrists, pharmacists, radiology technicians, laboratory technologists, and other skilled clinicians. For example, the nation could see a 46% dentist shortage in rural areas by 2037, and thousands more dental hygienists are needed. Physical and occupational therapists are also in high demand as the senior population grows. Overall, a 2022 McKinsey analysis warned that the U.S. could be short at least 200,000 allied health professionals (ranging from imaging technologists to clinical lab scientists) by 2025 if current trends continue.

Nursing Aides, Technicians, and Support Staff

Some of the most severe staffing gaps are in the ranks of lower-wage support workers who provide essential care. Nursing assistants (NAs), who help patients with daily activities and support nurses, are projected to be in dire shortage. A recent Mercer study forecasts a deficit of about 73,000 nurse assistants by 2028. Only 13 states are expected to have enough nursing assistants to meet demand; the rest will face shortfalls, some of them acute. These roles are vital in hospitals, nursing homes, and home health settings, yet they've been hard to keep filled – the work is physically and emotionally demanding, and wages are often not much higher than jobs in retail or other industries. Healthcare employers now find themselves competing with other fields for these workers.

Similarly, medical technicians (e.g. phlebotomists, EMTs) and health aides are in short supply, impacting services like blood draws and emergency response. The stress of the pandemic led many to leave, and new entrants aren't keeping up with openings. The American Hospital Association estimated that by 2028 the U.S. will be lacking on the order of 100,000 critical health workers across various categories, with some regions feeling it much more than others.

Administrative and Office Personnel

Less visible but equally important are the administrative staff who keep healthcare operations running – and they too are facing shortages. Hospitals and clinics need schedulers, billing and coding specialists, insurance coordinators, and other office staff to handle the avalanche of paperwork and coordination that modern medicine entails. In the wake of COVID-19, many experienced administrative workers left healthcare (some due to burnout or health concerns, others for higher-paying jobs elsewhere), leaving medical offices shorthanded.

One consequence is that clinical professionals are spending more time on clerical tasks. Surveys show that 87% of healthcare staff report working late at least weekly to finish administrative work. Nearly 60% say this burden negatively impacts their job satisfaction. The workload of processing insurance claims, scheduling appointments, responding to patient messages, and managing health records has outstripped the available personnel in many settings. This not only strains remaining staff but can also lead to delays in care (for example, when prior authorizations and billing issues slow down treatment). In short, virtually every role in the healthcare ecosystem – from the front desk to the operating room – is feeling the workforce crunch.

Why is this happening?

The reasons are multifaceted. Demographically, America's population is both growing and aging – by 2036 the number of people over 65 will have increased by over 30%, driving up demand for healthcare services. At the same time, a large portion of clinicians are nearing retirement (about 1 in 5 physicians is 65 or older, and many nurses are in their 50s and 60s). Burnout has become a crisis: after the intense pressures of the pandemic, more health workers are cutting back hours or leaving the profession early.

Education and training pipelines for new doctors and nurses take years to produce a fully trained professional, and they haven't expanded fast enough to replace the losses. Caps on medical residency funding and limited nursing school faculty contribute to these bottlenecks. Meanwhile, high turnover in lower-paid roles like aides or administrative staff means constant vacancies. Geographic maldistribution makes matters worse – rural and underserved urban areas struggle to attract enough providers, so shortages there are severe even if there might be a surplus in some metro areas. All these factors have created a perfect storm in which demand outstrips supply for many healthcare jobs.

For the public, the impacts are palpable: longer wait times for appointments, overwhelmed emergency departments, closed or understaffed nursing facilities, and healthcare workers who are stretched thin. The U.S. Bureau of Labor Statistics projects about 1.8 million healthcare job openings will be added each year over the next decade, yet if current trends continue, hundreds of thousands of those positions could remain vacant due to insufficient workforce. This is prompting healthcare leaders and policymakers to seek innovative solutions. In addition to training and recruiting more humans, many are looking to technology – especially artificial intelligence – as a way to extend the capabilities of the existing workforce and fill in some of the gaps.

How Agentic AI Could Help Address the Workforce Gaps

If we can't instantly produce tens of thousands of new nurses and doctors, could intelligent machines help shoulder some of the work? Enter agentic AI – AI systems endowed with a degree of autonomy to perform tasks or make decisions in a workflow. Unlike traditional software that only acts as a passive tool, agentic AI is designed to act on its own (within set guidelines), completing routine tasks from start to finish with minimal human intervention. In healthcare, this ranges from "digital employees" that handle administrative processes, to smart algorithms that analyze clinical data and suggest decisions, to physical robots that deliver supplies.

The past few years have seen rapid advances in AI, particularly with generative AI and large language models, which can understand and produce human-like text and even converse with patients. As a result, 2024–2025 is witnessing a wave of pilot projects where these AI "agents" are being deployed in health systems. In fact, investment in AI agent startups tripled from 2023 to 2024, reaching about $3.8 billion in funding, and every major tech company is developing some form of AI agent. The hope is that these technologies can "do more with less," easing the workload on an understaffed workforce without compromising patient care.

Below, we explore several key areas where agentic AI is being applied or tested to help mitigate healthcare workforce shortages:

1. Clinical Decision Support and Diagnostics

One of the most promising uses of AI in healthcare is to assist with clinical decision-making – helping doctors and other clinicians diagnose conditions, interpret tests, and plan treatments more efficiently. AI tools can rapidly analyze large volumes of medical data (like imaging scans, lab results, or medical literature) and highlight important findings, effectively serving as an extra pair of eyes and a tireless research assistant. This is especially valuable in areas experiencing specialist shortages, such as radiology and pathology, where the demand for reading scans or slides exceeds the supply of physicians.

A striking example comes from radiology. In 2024, Northwestern Medicine deployed a first-of-its-kind generative AI system across its 11-hospital network to help radiologists read and report X-rays. The AI autonomously analyzes each radiographic image and produces an initial draft of the radiology report – a summary of the key findings – in the radiologist's own style, covering about 95% of what the final report would include. The human doctor then reviews, edits if needed, and finalizes the report. The results, published in a major clinical study, were remarkable: on average, radiologists saw a 15.5% boost in efficiency for completing reports, and some experienced up to a 40% increase in productivity. Importantly, accuracy was not compromised by using the AI.

In fact, radiologists found that the AI could flag urgent, life-threatening findings (like a collapsed lung on an X-ray) within seconds, before the physician even looked at the image. By triaging the most critical cases to the top of the queue and providing a head start on documentation, this AI effectively helps a limited number of radiologists handle more cases in less time. One radiologist noted that "it doubled our efficiency… a tremendous advantage and force multiplier" for the team. This kind of productivity gain can make a real dent in the backlog of images waiting to be read, which is crucial given the ongoing shortage of radiologists (a shortage projected to reach thousands in the coming years, especially as imaging volumes rise). Similar AI-driven tools are being tested for other imaging, like CT scans and MRIs, and for pathology (where AI can screen slides for abnormal cells). While these systems don't replace specialists, they function as autonomous collaborators – handling routine parts of the diagnostic process so physicians can focus on the toughest cases.

AI isn't limited to images. In fields like oncology, researchers are creating agentic AI that can synthesize multiple sources of patient data and medical knowledge to assist with complex treatment decisions. For instance, a 2025 study described an autonomous AI agent for cancer care that combined a large language model (GPT-4) with specialized tools to analyze medical scans, pathology slides, and gene mutation data. Given a patient case, the AI could pull information from imaging (using vision AI to detect tumor markers), query medical databases and guidelines (like OncoKB and PubMed), and formulate recommendations for personalized treatment. In testing on realistic clinical scenarios, this prototype agent reached the correct clinical decision in about 91% of cases, significantly outperforming standard GPT-4 alone. It was also able to accurately cite relevant clinical guidelines and evidence as it worked through the case.

While this is still a research project, it showcases the potential of AI to act as an autonomous clinical assistant – one that can rapidly crunch through data and guidelines that would take a human hours to review. In the future, such AI decision support could help fill knowledge gaps in areas where specialists are scarce, ensuring that even small or rural hospitals have access to up-to-date expert guidance (delivered by an AI that has "read" literally all of the latest medical literature). Early forms of this are already emerging: large language models have been found capable of passing medical licensing exams and providing detailed medical explanations, and companies are beginning to integrate them as on-demand consultants for clinicians.

2. Automating Administrative Workflows (Scheduling, Insurance and More)

If you've ever scheduled a doctor's appointment or waited for your insurance to approve a procedure, you've touched the administrative side of healthcare. These behind-the-scenes processes are highly labor-intensive and have been hit hard by staffing shortages. Hospitals and insurance companies employ thousands of people for revenue cycle management – tasks like verifying insurance eligibility, obtaining prior authorizations from insurers, scheduling patients, processing claims, and following up on denied claims or billing issues. Not only are these workflows tedious and error-prone, but they also contribute significantly to costs; nearly 25% of U.S. healthcare spending goes to administrative overhead. With administrative staff in short supply, bottlenecks in these processes can delay patient care (e.g., a surgery waiting on an insurance approval) and pile extra work onto clinical staff. This is a ripe area for agentic AI to step in, and indeed we are seeing early adoption of AI "office assistants" to streamline such tasks.

One breakthrough is the use of AI agents to handle prior authorizations and insurance coordination. Prior authorization (prior auth) is the often frustrating process where a provider must get approval from an insurance company before performing a treatment or prescribing a medication. Traditionally, this involves staff phoning the insurer, filling forms, and waiting – a process that can take days. Companies are now deploying AI agents that autonomously navigate this prior auth workflow. For example, the startup VoiceCare has an AI agent named "Joy" that actually makes outbound phone calls to insurance companies to verify patient benefits, get prior authorizations, and follow up on claims. Joy speaks with the insurers' automated phone systems (or even with human reps if needed), gathers the required information or approvals, and then writes up a summary of the call for the human staff. In effect, Joy acts like a tireless call center employee handling these time-consuming calls so that real staff don't have to. The Mayo Clinic is one notable health system piloting this AI agent to help with their administrative load.

Another company, Thoughtful AI, offers a solution that goes end-to-end on claims: it can verify coverage, fill out and submit claim forms, and even generate appeal letters automatically if a claim is denied. By taking over these repetitive tasks, such agents free up human billing specialists to focus on more complex cases that truly need human judgment. The benefit is faster turnaround – providers get paid sooner and patients get approvals faster – with fewer errors and less burnout on staff. A Salesforce analysis found that healthcare professionals believe AI agents could reduce administrative workloads by roughly 30–40% for doctors and nurses, and nearly 28% for administrative staff, potentially saving 10 hours per week per worker. Those are hours that could be redirected to patient care or other important work.

Scheduling and patient access is another administrative area being transformed by agentic AI. Traditionally, booking an appointment or getting a question answered meant phone tag with a busy office staff. Now, AI-driven scheduling agents can handle these interactions digitally and efficiently. A new wave of companies (Hippocratic AI, Assort Health, Innovaccer, to name a few) are rolling out autonomous agents to act as a smart front desk. These agents can converse with patients via phone or chat 24/7, answer common questions, match patients to the right providers, and slot them into appropriate appointment times – all without needing a human scheduler.

3. Patient Communication and Scheduling Support

Keeping open lines of communication between patients and their care teams is another area strained by workforce limits. Doctors often struggle to respond to the flood of patient portal messages, nurses can't always follow up with every discharged patient, and care coordinators may be overwhelmed trying to check in on those with chronic conditions. Agentic AI is stepping up here in the form of virtual health assistants and chatbots that interact directly with patients, augmenting the work of nurses, medical assistants, and care managers.

A clear example is how AI is being used to handle patient messages and follow-ups. Large health systems that use electronic health record portals (like Epic's MyChart) have reported huge volumes of patient emails – medication questions, follow-ups about symptoms, etc. In 2023, Epic partnered with Microsoft to introduce generative AI features that draft replies to patient messages for the doctor. The AI can read the patient's query and their medical record, then compose a suggested response, which the physician just needs to review and edit as needed. Early studies at places like Mass General Brigham found that GPT-4–drafted messages to patients were acceptable (without editing) 58% of the time, and in many cases the AI replies were more empathetic and detailed than what busy doctors might have written. By letting AI handle the first pass, doctors save time – effectively the AI acts as a junior triage nurse for the inbox.

AI "care companions" are also being tested for post-discharge follow-up and chronic care management. These are systems that reach out to patients at home via phone call or text, ask them a series of questions about their recovery or ongoing health, and analyze the responses. For instance, an agent named "Sarah" developed by Hippocratic AI is used in assisted living facilities to chat with residents daily – Sarah can ask how they're feeling, remind them to take medications, help organize transportation, and even report any concerns back to the nurses. Another AI agent, "Rachel," focuses on patients with chronic kidney disease, conducting regular check-ins to spot any early signs of trouble (like symptoms that could indicate worsening condition).

4. EHR Management and Clinical Documentation

If there's one task universally despised by clinicians, it's documentation – the endless note-writing, order entry, and clerical clicks required in electronic health records (EHRs). Studies have shown that for every hour a physician spends face-to-face with patients, they spend nearly two more on EHR and desk work. This administrative overload is a major contributor to burnout and effectively reduces the time providers have available to see patients. In fact, "documentation burden" is often cited as one reason some doctors cut their clinical hours or retire early. Here, AI is making significant inroads with tools like ambient clinical "scribes" and intelligent EHR assistants, which aim to give providers time back by taking on the grunt work of documentation.

A standout example is the use of ambient AI scribe technology. In late 2023, The Permanente Medical Group (TPMG) – the physician group for Kaiser Permanente in Northern California – rolled out an AI scribe system across their practices, and the impact over one year was striking. These AI scribes use speech recognition and natural language processing to listen to doctor-patient conversations in real time and automatically generate draft clinical notes for the doctor. Instead of a physician having to type or dictate a summary after each visit, the AI produces the note (history, exam findings, assessment, and plan) based on the conversation, which the doctor then quickly reviews and signs.

Over 2.5 million patient encounters were processed with the AI in the first year. The results, published in NEJM Catalyst, showed that the AI scribes saved physicians an estimated 15,790 hours of documentation time in that year – equivalent to about 1,794 eight-hour workdays. In practical terms, that's like adding back 1,800 days of physician capacity, simply by offloading note-taking to an AI. Doctors reported spending significantly less "pajama time" (after-hours EHR time) and less time per appointment on paperwork. This improved work-life balance and efficiency: by eliminating an average of one to two hours of typing per day, the AI scribes enabled doctors to either see a couple more patients or to go home earlier, both of which help address workforce strain.

Perhaps even more importantly, the quality of patient interactions improved. In surveys, 84% of physicians said the AI scribe improved communication with their patients, and 82% said their overall job satisfaction went up. Patients noticed the difference too – nearly half reported their doctor spent less time glued to the computer and more time looking at them, and a majority felt their visit quality improved with the doctor more attentive. Essentially, by freeing doctors from the data-entry role, AI scribes let them be doctors again – listening and conversing instead of typing.

5. Robots as Physical Workforce Extenders

Up to now, we've focused on software-based AI agents, but agentic AI can also take a physical form as robots that perform tasks in healthcare settings. With shortages of nurses, orderlies, and technicians, some hospitals have enlisted robots to help with routine, non-clinical chores – things like fetching supplies, delivering medications or lab samples, and even disinfecting rooms. These robots are essentially autonomous assistants that roam the halls, handling tasks that would otherwise pull human staff away from patient care.

A prominent example is "Moxi," a 4-foot-tall autonomous robot designed to assist nurses. Moxi has been deployed in a number of hospitals across the country to reduce the load of "hunting and gathering" tasks that nurses often get stuck with – such as running to the pharmacy for a medication, transporting blood samples to the lab, or collecting supplies like IV pumps or linens. Moxi can work 22 hours a day, never needs a break, and can carry up to 70 pounds in its secure compartment. It's equipped with an arm to open doors and operate elevators, plus sensors to navigate crowded hallways safely. In short, Moxi is built to autonomously traverse a hospital and do pickup-and-delivery tasks that otherwise would interrupt a nurse or nurse assistant's workflow.

A hospital patient snaps a selfie with Moxi, the nurse assistant robot. Moxi uses AI to learn hospital routines and can autonomously deliver supplies, specimens, and medications, helping to free up human nurses for direct patient care.

Hospitals such as Cedars-Sinai in Los Angeles, Medical City Dallas, Mary Washington Hospital in Virginia, and ChristianaCare in Delaware have introduced Moxi robots to their units. ChristianaCare, for instance, purchased five Moxis with a grant and deployed them to 11 inpatient units, integrating them with the hospital's IT systems so the robots can even anticipate when nurses will need certain items (based on EHR orders) and proactively bring them. Early reports indicate that robots like Moxi improve nurse productivity and allow nurses to spend more time at the bedside, since they are interrupted less for errands. Nurses have also anecdotally appreciated having mundane tasks taken off their plate, which can reduce stress and burnout.

Robots are being used in other supporting roles as well. Some hospitals employ UV-disinfection robots that autonomously roam and sanitize rooms with UV light, cutting down on cleaning staff workload (these were especially popular for infection control during COVID-19 surges). There are also specialized robots like the one from Vitestro (a Dutch company) which can draw blood autonomously – it uses ultrasound and AI to find a vein and performs a venipuncture to collect blood samples. In trials it has done thousands of blood draws successfully. Such technology could address phlebotomist shortages or free nurses from having to do routine blood draws.

Opportunities and Challenges of Embracing Agentic AI in Healthcare

The examples above illustrate that agentic AI has the potential to significantly alleviate pressure on the healthcare workforce. From boosting the efficiency of scarce specialists, to automating clerical tasks, to extending the reach of nurses and support staff, AI agents can help close some of the service gaps caused by personnel shortages. They also offer the promise of 24/7 support, consistency, and the ability to rapidly scale (you can deploy an extra digital agent far faster than training a new human hire). If fully realized, these technologies could improve access to care (patients face fewer delays), reduce burnout (by eliminating tedious work and giving clinicians more time for meaningful work-life balance), and even save costs in the long run (by streamlining processes and reducing errors). Crucially, they might allow the existing workforce to handle a greater volume of care safely, which is one way to bridge the gap between supply and demand for services.

However, adopting agentic AI in healthcare is not without its challenges and caveats. Healthcare is a high-consequence field, and any AI system must be carefully validated and monitored. Most current deployments enforce strict "guardrails" and human oversight. As noted, AI agents today tend to operate within predefined workflows and will defer to humans for anything that falls outside normal parameters. For instance, an AI scheduling assistant might handle a routine appointment booking autonomously, but if a patient reports severe symptoms in the process, it will flag a nurse or doctor to step in. This hybrid approach helps ensure safety, but it also means AI hasn't (and shouldn't) completely replaced human decision-makers.

Accuracy and reliability of AI remain prime concerns. Tools like generative AI are powerful but can sometimes produce incorrect or nonsensical outputs (so-called "hallucinations"). In the health context, errors can be dangerous. Rigorous validation is needed for each use case – the Northwestern radiology AI underwent a large clinical trial before being fully adopted, for example. Regulatory bodies like the FDA are actively developing guidelines for AI in healthcare, but as of 2025 the oversight framework is still catching up.

Human acceptance is another piece. Not all healthcare workers are immediately comfortable handing tasks to AI. Some fear that automation could threaten jobs (especially in administrative domains), though the current reality in healthcare is that there's more than enough work to go around. Training and change management are critical – staff need to understand what the AI does, how to interpret its output, and how to intervene if something seems off.

Patient acceptance is another consideration. By and large, patients have been receptive to AI assistance when it improves convenience (such as fast scheduling or quick message responses). Some patients may not even realize an AI is involved if it's seamless. However, it's important for healthcare providers to be honest with patients when an AI is contributing to their care – for instance, letting them know a "computer assistant" helped draft their after-visit summary.

From a broader perspective, while agentic AI can mitigate workforce shortages, it is not a panacea. We will still need to train and hire more health professionals to meet growing demand. AI can help each person be more productive, but it can't fully replace the empathy, critical thinking, and complex hands-on skills of healthcare providers. In fact, the hope is that by offloading the drudgery, AI will let humans focus more on those inherently human aspects of care.

Conclusion

In conclusion, the U.S. healthcare system faces a formidable workforce challenge in the coming years – a gap that threatens to undermine access and quality if left unaddressed. Agentic AI offers a ray of hope: an opportunity to innovate our way out of some of the strain by empowering the people we do have with new capabilities. From the clinic to the back office to the hospital floor, autonomous AI systems are already starting to fill in the cracks – writing notes, scheduling visits, answering calls, delivering supplies, and more.

Early trials and deployments have shown tangible benefits: shorter wait times, faster results, fewer burnout symptoms, and even improved patient satisfaction. These technologies, when thoughtfully integrated, act as force multipliers for human staff, essentially adding a new kind of workforce that collaborates with the existing one.

Yet, adopting agentic AI is a journey that requires care – in validation, training, oversight, and maintaining the primacy of human judgment and compassion. The next decade will likely see an expansion of these AI solutions, guided by the lessons learned from initial pioneers. If we get it right, the payoff could be substantial: a healthcare system where no patient's needs go unmet due to lack of staff, because AI is helping clinicians extend their reach.

One could imagine rural clinics using AI to offer specialist-level diagnostics, or an understaffed hospital maintaining quality service because AI handles all the busy-work. The path won't be without bumps, but the trajectory is set. In an industry long plagued by inefficiencies and burnout, agentic AI is emerging not as science fiction, but as a practical toolkit to help close the workforce gap and transform challenges into opportunities.

The stethoscope and the keyboard may soon share space with the algorithm and the robot – all working in concert to care for the patient. And ultimately, that is the north star: using every tool at our disposal, human or artificial, to ensure that people get the care they need when they need it, even in the face of shortages. With smart implementation, agentic AI can be a critical part of achieving that goal, heralding a new era of augmented healthcare delivery that benefits both providers and patients alike.