There’s a new presence in the hospital, the operating room, the pharmacy, and the research lab. It doesn’t breathe, but it learns—agentic AI is no longer a futuristic concept in healthcare; it’s here, and it’s revolutionizing the industry. It is poised to address some of the sector's most pressing challenges, from skyrocketing costs to staff shortages and slow and long-drawn-out drug development.

AI agents, autonomous AI agents, and agentic AI are closely related concepts in the field of AI, each with different characteristics and capabilities. So, what are AI agents? They are software entities designed to perceive their environments, make decisions, and take actions to achieve specific goals. They can range from simple reflex-based systems to more complex, goal-oriented entities capable of learning and improving over time. OpenAI CEO Sam Altman recently described these agents as the next “giant breakthrough.” Additionally, senior executives called 2025 “the year of AI agents” and the year when “agentic systems finally hit the mainstream.” This was on the back of several breakthroughs in 2024 in the deployment of agents and the underlying GenAI models that power them. As developments in the field rise, the potential applications of AI agents are multiplying. Moreover, Deloitte expects 25% of companies using GenAI to launch agentic AI pilots or proofs of concept in 2025, with this figure rising to 50% by 2027.

So, in this blog, we’ll explore why agentic AI is a game-changer in healthcare, some use cases, and what this transformative tech will bring us in the future.

Why is agentic AI a key aspect of healthcare?

‍The combination of advanced AI algorithms, increased computing power, and the growing availability of healthcare data enables agentic AI systems to operate at scale. These systems not only assist but also autonomously manage complex tasks, from diagnosing diseases to optimizing hospital operations. The urgency to adopt agentic AI is further fueled by the healthcare industry’s mounting challenges: rising costs, workforce shortages, and the need for faster, more accurate decision-making. Agentic AI is, therefore, going from a “nice-to-have” to a “must-have” for healthcare organizations.

Agentic AI’s potential in healthcare lies in its ability to handle massive amounts of data, navigate strict regulatory environments, and address urgent needs for efficiency. Healthcare generates an enormous volume of data daily—patient records, imaging data, genomic information, and more. Traditional systems struggle with processing and analyzing this data effectively, but agentic AI thrives in this. It can autonomously sift through data, identify patterns, and make decisions, all while adhering to regulatory requirements.

Moreover, healthcare is an industry where efficiency can mean the difference between life and death. Agentic AI can streamline operations, reduce waste, and free up healthcare professionals to focus on patient care. Its ability to learn and adapt in real time makes it uniquely suited to tackle the dynamic and complex challenges of the healthcare sector:

1. Rising costs: Healthcare costs are spiraling out of control, with administrative inefficiencies significantly contributing. Agentic AI can automate repetitive tasks like billing, claims processing, and appointment scheduling, reducing operational waste and saving billions annually.
   
   
2. Staff shortages: The global healthcare workforce is under immense strain, with doctors, nurses, and other healthcare workers often overwhelmed by their workloads. AI-powered assistants can alleviate this burden by handling routine tasks, allowing healthcare professionals to focus on patient care.
   
   
3. Slow drug development: Developing new drugs is a time-consuming and expensive process. Agentic AI can accelerate R&D by analyzing vast datasets, predicting drug efficacy, and identifying potential side effects, significantly shortening the time to market for life-saving treatments.
   

Six high-impact use cases of agentic AI in healthcare

1. Clinical trials

• Issue: Clinical trials are essential for developing new treatments, but they are often slow, expensive, and prone to inefficiencies. Patient recruitment, data collection, and analysis can delay the process.
• Solution example: Agentic AI can autonomously identify eligible patients for trials by analyzing electronic health records (EHRs) and medical histories. It can also monitor trial participants in real time, ensuring compliance and collecting data seamlessly.
• Impact: Faster patient recruitment, reduced costs, and accelerated trial timelines mean life-saving treatments reach the market sooner.
  

2. Drug discovery and development

• Issue: Traditional drug development is a lengthy and costly process, often taking years and many thousands of dollars (if not millions) to bring a single drug to market.
• Solution example: Agentic AI can analyze vast datasets, including genetic information, chemical compounds, and clinical trial results, to identify promising drug candidates. It can also predict potential side effects and optimize drug formulations.
• Impact: Reduced R&D timelines and costs, leading to faster development of innovative therapies for diseases like cancer, Alzheimer’s, and rare genetic disorders.
  

3. Medical research and data analysis

• Issue: Medical research generates enormous amounts of data, but analyzing it manually is time-consuming and prone to human error. Researchers need tools to uncover insights quickly and accurately.
• Solution example: Agentic AI can autonomously analyze complex datasets, such as genomic data or population health records, to identify trends, correlations, and potential breakthroughs. For instance, it can uncover genetic markers linked to specific diseases.
• Impact: Accelerated research timelines, more accurate findings, and the potential to discover new treatments or preventive measures for complex diseases.
  

4. Health record management

• Issue: Managing health records is a critical but labor-intensive task. Inefficiencies in record-keeping can lead to errors (which could even result in death), delays in care, and increased costs.
• Solution example: Agentic AI can automate the organization, updating, and retrieval of EHRs. It can also ensure compliance with regulatory standards like HIPAA by flagging discrepancies or unauthorized access.
• Impact: Improved accuracy, reduced administrative burden, and faster access to patient information, leading to better patient outcomes and lower operational costs.
  

5. Remote monitoring

• Issue: Chronic diseases and post-operative care require continuous monitoring, but traditional methods often rely on in-person visits, which can be inconvenient and costly.
• Solution example: Agentic AI can power wearable devices and remote monitoring systems that track vital signs like heart rate, blood pressure, and glucose levels in real time. It can detect and alert healthcare providers to anomalies, enabling timely interventions.
• Impact: Enhanced patient care, reduced hospital readmissions, and improved quality of life for patients managing chronic conditions.
  

6. Admin workflow automation

• Issue: Administrative tasks, such as scheduling, billing, and claims processing, consume significant time and resources, diverting attention and resources from patient care.
• Solution example: Agentic AI can automate repetitive tasks like appointment scheduling, insurance verification, and claims processing. For instance, it can autonomously resolve billing discrepancies or prioritize urgent appointments.
• Impact: Reduced administrative costs, improved operational efficiency, and more time for healthcare professionals to focus on delivering high-quality care.
  

What’s next for agentic AI in healthcare?

The future of agentic AI in healthcare is brimming with possibilities. Here are a few areas to watch:

• AI in robotic surgery: Agentic AI could enable autonomous surgical robots to perform complex procedures with precision, reducing the risk of human error.
  
• Predictive disease modeling: AI could predict disease outbreaks and spread patterns, helping public health officials take proactive measures.
  
• Mental health support: AI-powered chatbots could provide mental health support, offering therapy and crisis intervention to those in need.
  

Challenges

While the potential of agentic AI is immense, it’s not without concerns. Ethical issues, such as data privacy and algorithmic bias, must be addressed to ensure equitable and safe AI deployment. Regulatory frameworks will also need to evolve to keep up with technological advancements. Additionally, healthcare organizations must invest in training and infrastructure to fully leverage agentic AI’s capabilities.

What now?

Agentic AI is not just a technological advancement—it shifts the way healthcare is delivered. By automating tasks, enhancing decision-making, and enabling personalized care, agentic AI can transform healthcare into a more efficient, effective, and equitable system. In time, adopting agentic AI will no longer be optional but essential for healthcare organizations in this increasingly complex and demanding landscape. Download the Speeda Edge report on agentic AI in healthcare for more insight.