Artificial Intelligence in Manufacturing Market Size, Share, Trends and Growth Analysis 2033
AI Agents In Healthcare Revolutionizing Patient Care And Clinical Decision-Making
Globally, the modern healthcare domain is experiencing rapid digitization. Technology is helping healthcare organizations overcome the common challenges faced in this domain.
For instance, healthtech is helping organizations address inefficiencies in their workflow and uncertainty about how providers can move to value-based care without increasing the administrative burden.
A Forrester study reveals that nearly half of U.S. Healthcare organizations are deploying AI to improve workflow efficiency. No wonder the AI healthcare market is projected to follow a CAGR of 38.6% and reach $110.61 billion by 2030.
Healthcare technology, or healthtech, is developed with the objective of enhancing patient care, improving operational efficiency, and achieving better health outcomes. Software, hardware, cloud-based computing, and other tools that healthtech encompasses are designed to simplify healthcare.
However, the technology requires in-depth knowledge of numerous interconnected systems and processes. If each cog in the system fails to work together, organizations have to deal with healthtech software development challenges that act as a hurdle for the technology going mainstream.
In recent years, AI has emerged as a disruptive tool aiding in decision-making and personalized interventions. AI agents in healthtech reduce administrative burdens and physician burnout. They have transformed patient-clinician relationships by improving processes like patient preregistration, preparing physicians with patient data before the appointment, and supporting them with informed decision making.
In this post, we will discuss in detail the growing role of AI agents in healthcare and how it is changing patient care for the better.
AI Agents and Their Role in HealthcareAI agents are AI-enabled digital assistants that work to automate certain workloads and enhance decision-making. When combined with a variety of data types, AI agents use LLMs (large language models) and RAG (retrieval-augmented generation) to answer questions and analyze language context and tone. Thus, AI agents can be assigned tasks to solve complex healthcare problems.
In a healthcare setting, when chat, text, or voice interfaces are used, AI agents can scan data, summarize the spoken words, and uncover signals that need human intervention. These agents work by encoding human language requests and sending them to the enterprise data store. They use LLMs to decode the query, search the knowledge base, combine the most relevant content and the query for a response, and send it back to the requester.
At an advanced level, AI agents in healthcare take historical data on disease outbreaks and interpret it against laboratory patterns. If it identifies a cluster of values, it helps clinicians predict an outbreak.
Healthcare AI agents are often embedded into administrative and clinical workflows like patient registration, where they automate filling out lengthy forms. They also take the manual data entry out of the hands of clinicians, allowing them to focus on patient care and medical knowledge application.
The healthcare domain is in the early stages of AI agent deployment due to various regulations and complexities. Many of these hurdles stem from software development challenges including interoperability, data privacy concerns, and compliance with healthcare standards. However, when implemented correctly, these digital assistants can significantly improve decision-making and patient care.
10 Ways AI Agents Are Revolutionizing HealthcareThe healthcare industry is yet to realize the full potential of AI agents. Yet, agents have several practical applications that can transform healthcare. AI agents relieve clinicians of manual work and assist them in gaining an informed and focused view of patients. They also provide patients with a 'trusted assistant' that guides them through the complex healthcare system.
Here are a few benefits AI agents offer in healthcare.
1. Supports Healthcare Providers
As mentioned earlier, AI agents help healthcare providers with decision-making by providing them with patient history before an appointment. It also offers access to medical informatics tools trained on specific clinical data sets.
For instance, a nephrologist with a chronic kidney disease patient can ask the AI agent to pull data on the latest research on the condition, lab and biopsy reports, CT scans, and more. This helps in predictive modeling analysis, which helps the doctor recommend an ideal course of action. This also reduces clinician and administrator burnout.
2. Reduces Costs
In a recent report by the Medical Group Management Association, 92% of medical groups shared their concerns about the increasing operating costs. Hence, healthcare organizations are looking to control costs.
AI agents can automate and streamline tasks like billing, coding, and payer reimbursement, thus reducing administrative costs.
3. Allows Improved Diagnostics
AI agents offer in-depth data on patient history, relevant medical research, and data from medical devices like CT scans and MRIs. All this helps them provide accurate diagnostics and suggest the best future course of action.
4. Offers Personalized Treatment
Since they collect patient data from various sources, AI agents can help generate personalized treatment plans for clinician review and approval. These agents also gather sensor data from personal devices and alert healthcare professionals when the readings are abnormal.
5. Enhances Efficiency
An American Medical Association report reveals that physicians spend over five hours with electronic health records (EHRs) for every eight hours spent with patients. Automating EHR updates and coding of treatments through AI agents can facilitate accurate reimbursement while helping physicians gain back time.
AI agents can ensure that doctors spend quality time with their patients to make appropriate clinical decisions.
6. Allows Real-Time Monitoring
AI agents help healthcare providers constantly monitor patient health by connecting with remote monitoring tools like smart watches, heart monitors, and glucometers. Thus, instead of relying on the data collected during office visits, physicians can access ready data that is interpreted by the AI agent.
Physicians get alerted when an intervention is necessary. The intervention encourages patients to be more health-conscious.
7. Accelerates Drug Development
Physicians cannot possibly remember every clinical trial that might be useful to their patients. AI agents can effortlessly track the latest clinical trials relevant to the patient's case and medical history.
Further, they analyze patient data, predict drug interactions, and share potential side effects before the trial begins. By simulating the patient responses, AI agents help pharma companies design targeted trials, thereby improving the success rates and reducing side effects and associated costs.
8. Improves Accessibility
AI agents leverage natural language, making it easy for patients to take charge of their health. For instance, a patient could enter a query about a symptom or an adverse event, alert a healthcare provider, schedule an appointment, or get reminders on prescription renewal.
9. Offers Predictive Insights
AI agents leverage predictive analytics to help doctors predict a patient's medical condition, remission rate, and health risks. This allows them to create an informed and tailored treatment plan for their patients.
10. Ensures Data Integrity and Security
In the healthcare domain, compliance with regulatory bodies like HIPAA, GDPR, and CCPA is a must. AI agents help ensure data integrity and security by automating regulatory and compliance tasks.
This saves the organization from legal consequences and hefty fines and frees up valuable time for healthcare professionals.
The Need for an Expert Healthtech Development PartnerAI agents come with a world of benefits for the healthcare industry. Nonetheless, deployment cannot be complete without having the necessary technical expertise on hand. There must be an agreement with those experts who understand the nitty-gritty details of medical data standards, interoperability requirements, and different types of compliance frameworks.
Working with a HealthTech software development expert will allow you to develop systems that perform well and integrate with existing systems such as those for EHRs or imaging, among others.
Moreover, these professionals are mindful of protecting the privacy of their users and understand the ethical issues in telehealth. Once in partnership, they reduce the risks associated with deployment and speed up time-to-value. Working alongside AI agents and a healthtech development partner gives way to a reliable, scalable, and patient-centric solution.
Summing UpRelieving physicians and healthcare professionals from their administrative tasks and improving operational efficiency have been the priorities for healthcare tech companies. AI agents promise to do just that-will relieve the burden, reduce costs and diagnostic errors, and ensure physicians have more quality time with their patients.
Looking forward, the role of AI will be much more dominant in not just enhancing patient experience but also overall diagnosis-and-treatment.
Gaurav Belani is a Senior SEO and Content Marketing Analyst at Growfusely, where he specializes in crafting data-driven content strategies for technology-focused brands.
2025 Is Becoming The Year Of AI Agents In Healthcare
AI agents are all the rage – and this time healthcare isn't far behind other industries in adoption.
Healthcare traditionally trailed other industries in digital transformation and hospitals resist change, even with incentives. But this time, with big challenges ahead, health systems big and small are working on artificial intelligence and agentic AI solutions to revitalize the workplace, expand capacity, reduce stress, accelerate the revenue cycle and improve patient care. Rochester, Minn.-based Mayo Clinic has been on the forefront of AI for years and is leveraging agentic AI for a variety of functions.
"Building on the automation foundation in place across Mayo Clinic, we are now entering a bold new phase of innovation and impact," said Anjali Bhagra, MD, medical director for automation at Mayo Clinic. "Our focus for the remainder of 2025 centers on pioneering and integrating agentic automation capabilities that seamlessly support both clinical and operational workflows."
The next step for the health system will be developing an agentic automation architecture and integrated framework to scale and deploy intelligent agents. Dr. Bhagra and his team understand the importance of thoughtfully developing the technology for heightened security and sustainability. They're also working on accelerating the next generation of human-AI interactions.
"By leveraging emerging technologies such as holographic interfaces and digital avatars, we are reimagining how care is delivered, experienced and supported: all anchored in our commitment to innovation, excellence and compassionate care," said Dr. Bhagra.
Mount Sinai Health System in New York City is also creating an agentic AI roadmap focused on responsible, scalable AI aligning with strategic priorities. Robbie Freeman, chief digital transformation officer at Mount Sinai, told Becker's the organization is co-designing solutions with frontline teams and building governance structures for safe and effective implementation.
"Our top focus for the second half of this year is advancing our digital strategy to better support our workforce, patients, consumers and care teams," he said. "We're prioritizing tools that enhance experience, streamline workflows and enable more personalized, coordinated care."
AI agents are poised to transform the healthcare workplace, both for clinicians and operational teams. Darrell Keeling, PhD, senior vice president and chief information security officer at Fort Wayne, Ind.-based Parkview Health, said these tools could be part of the solution for workforce shortages and resource depletion; but they aren't cheap.
"These tools come at a significant cost and must deliver value well beyond basic tasks like grammar correction, writing emails, creating presentations or creating a unique drawing to justify their return on investment," said Dr. Keeling. "The real opportunity lies in training staff to thoughtfully integrate these agents into their daily workflows, not only to automate repetitive tasks but to enhance the value of their expertise."
Many hospitals and health systems still have tight margins and are recovering – financially and operationally – from the COVID-19 pandemic. While it's been shown that tech-forward hospitals adopting AI and meaningful digital transformation have bigger financial growth long-term, the upfront investment is still prohibitive.
"Can we cost-effectively invest in agentic agents without undermining our existing workforce? True success lies in leveraging the intellectual capital of our employees to guide, shape and optimize these technologies, not replace them," said Dr. Keeling. "It's more about reskilling our workforce and workforce augment."
Lewis Marshall Jr., MD, chief medical officer at NYC Health + Hospitals / Lincoln Hospital is keeping a close eye on how AI and machine learning could quickly support the hospital with Medicaid cuts on the horizon. AI can automate the standard operating procedures, allowing the hospital to run efficiently with a lean team. AI can also help providers converse with patients, schedule appointments and identify specific diagnoses more accurately.
"We will need to look at agentic AI to make some decisions and take some actions, but we will need to make sure we have human oversight," said Dr. Marshall. "While we are entering challenging times, there are potential solutions that, if identified, implemented and used thoughtfully, will reduce staff stress while improving patient satisfaction."
Michael Laukaitis, director for revenue cycle analytics, accounting and quality assurance at UT Southwestern Medical Center in Texas, is also doubling down on agentic AI to evaluate inbound and outbound revenue cycle calls into an autonomous, voice-driven workflow. He's using the technology for:
"Every call is transcribed, summarized and written back to Epic and our CRM, giving staff instant context for the next touchpoint and shaving minutes off each interaction," he said. "LLM-powered agents will verify eligibility, answer balance questions, start prior authorizations and even schedule follow-up calls while a human stays in the loop only when nuance is needed. We're training custom acoustic models that understand regional accents and medical jargon, then pass intent and sentiment to our RPA bots so the next task fires before the caller hangs up."
Integrating the AI agents effectively is easier said than done. AI literacy has become a big focus for many health systems, aiming to teach their teams about the technology and how to use it. Ideally, team members will hand over task-based responsibilities to AI agents and focus on the high-level, human-to-human interactions within their role.
"It's not just about individual productivity," said Dr. Keeling. "It's about creating repeatable processes that others can adopt and build upon, enabling the enterprisewide adoption of agentic AI in a controlled and responsible manner."
The Adoption Of Artificial Intelligence In Clinical Care
As a newly minted division chief at a major children's hospital at the beginning of the 2000s, I (FWP) observed the growing impact of the electronic medical record (EMR) movement on clinical care. Now, as a wizened graybeard, I'm wondering about the effects of artificial intelligence (AI).
Lessons From the Electronic Medical Record MovementIn my opinion, much of the promise of EMRs has yet to be realized. The widely hyped potential of EMRs to simplify the transfer of medical records from one institution to another is often thwarted by the fact that, even when nominally the same EMR model is implemented by the same vendor at two separate institutions, one hospital's version is incompatible with the other's. This is a result of the "customization" of the EMR to accommodate each institution's special needs and policies.
Furthermore, as I consult on cases, I note that although the patient's symptoms have changed profoundly over the course of a hospitalization, the clinical notes fail to reflect these changes accurately. Instead, outdated excerpts from previous clinical notes are often repeatedly "cut-and-pasted" into the progress notes, sometimes perpetuating serious errors.
Finally, when we attempt to transmute an EMR's bits and bytes into a paper record, it often proves unusable. For example, every blood pressure, pulse, lab value, or medication administration is printed on a separate page, resulting in a haystack of paper that defies organization.
The Clinical Promise of Artificial IntelligenceAI is inexorably coming to clinical care and will be far more impactful than the EMR movement to date. AI is often promoted as improving diagnosis and prediction of the clinical course. It is increasingly being used to inform decision-making and patient management. AI is also used to review patient records and generate notes and discharge summaries. Finally, AI is emerging as an important tool in research and teaching.
A number of studies find that AI can equal or exceed the diagnostic accuracy of experienced specialists—in a fraction of the time. AI's capacity to process enormous amounts of data is unmatched. For example, a recent study seeking to predict the likelihood of agitation and violence in emergency rooms examined more than 3 million visits. Interestingly, the findings were "…consistent with existing literature that identified historical violence as a predictor of future agitation events" (Wong et al., 2025). Thus, in this case, AI confirmed the longstanding clinical truism that past behavior is the best predictor of future behavior.
AI can also "see" behaviors that are difficult for clinicians to recognize. For instance, identifying a seriously ill premature infant's state of arousal is extremely important for the optimal timing of feeding and care (Putnam, 2016). Cell phone videos of infants in a neonatal intensive care unit were analyzed with an AI object detection model quantifying head and hand movements (Nishio et al., 2025). The results provided an easy way to identify the best times to care for desperately ill preemies.
Problems With Artificial IntelligenceWith the rapidly growing use of AI, a number of serious problems have emerged that affect a broad range of applications. So-called "hallucinations" are false "factual" statements made by AI programs. These may include invented references and even fabricated data. Disturbingly, the frequency of hallucinations seems to be increasing with each new version released (a peculiarity common to all AI platforms to date) and may occur as frequently as 40 to 75 percent of the time.
Old-timers, such as myself, worry about an uncritical overreliance on AI by clinicians as well as misrepresentations of risks and benefits to patients. Indeed, some studies suggest that AI-based health care searches may generate massive amounts of disinformation, including convincing, but AI-fabricated, medical images (Menz, 2023).
What Is Being Done to Improve the Clinical Uses of AI?Much of the pioneering development of EMRs occurred in the Veterans Administration (VA) with its 170 hospitals, about a decade before the rest of the medical community began adopting EMRs. Now, drawing on its extensive experience with EMRs, the VA has become a leader in using AI to develop clinical predictive models as well as integrating their results into routine patient care.
One lesson is that AI predictive models drift over time and probably should be recalibrated on an annual basis. Another is that AI models are poor at predicting low-frequency medical or mental health events (e.G., suicide), leading to disruptive false alarms that vastly outnumber true positives.
Although many AI models focus on clinical prediction, one of their more important functions is simply searching a patient's EMR for relevant symptoms and linking them to the appropriate scientific literature. The VA is pioneering technologies such as "ambient listening," which uses AI to record, transcribe, and analyze patient-clinician conversations, automating documentation and freeing the clinician to focus on the patient and not the keyboard.
Stephan Fihn, one of the pioneers in the VA's work on EMRs and an early adopter of AI, believes that the future of AI processing of a patient's EMR will look more like a Google search rather than a traditional medical chart—pulling together all of the relevant information with a single click (Perlis, 2024).
If so, AI may help the EMR to finally realize its true potential.
Comments
Post a Comment