Dr. Anthony Chang

“Agentic AI systems promise to transform many aspects of human-machine collaboration, especially in areas of work that were previously insulated from AI-led automation, such as proactively managing complex IT systems to pre-empt outages; dynamically re-configuring supply chains in response to geopolitical or weather disruptions; or engaging in realistic interactions with patients or customers to resolve issues.”

Mark Purdy, Harvard Business Review 12/24

I have been kindly invited to speak on the topic “Future Applications of Generative AI in Healthcare” at the recent Becker’s annual meeting, so here are some of my thoughts regarding generative AI and its AI partner, agentic AI, in healthcare now and in the future.

GenAI and agentic AI

Generative artificial intelligence (GenAI) refers to AI systems that create new content, data, or outputs from learned patterns in existing data, and the generated outputs can be text, image, video, code, or synthetic datasets. These AI systems have low to moderate autonomy with human oversight for interpretation and decision-making.

Agentic AI, on the other hand, involves AI systems that are designed to autonomously perform actions, make decisions, and achieve goals, typically in interactive or real-time settings. These AI systems have higher autonomy compared to GenAI, and usually possess reasoning capabilities, goal-directed planning, and autonomous decision-making. These tools can operate with minimal human supervision in complex, dynamic clinical environments. Because agentic AI tools have higher complexity due to autonomous decisions and real-world actions, these tools have greater potential risk associated with decision-making errors, unintended actions, or ethical dilemmas.

One can think of GenAI as the coach of a sports team on the sidelines that supports strategic preparation and knowledge sharing before the game, and agentic AI as the captain of the team of players on the field that handles tactical execution and adaptive decision-making in the real world.

Current applications of GenAI and agentic AI in healthcare.

The compound annual growth rate (CAGR) of GenAI in healthcare is estimated to be 35-38% for the next 5 years, and is expected to reach $12 billion per annum in healthcare during this time. There is a myriad of uses of generative AI in healthcare, and these include:

• synthetic medical data for training
• clinical note summarization
• virtual health assistants
• educational tools
• medical image generation and enhancement.

There is also a recent focus on patient-provider communications and education.

Agentic AI in healthcare is beginning to be used in:

• real-time clinical decision-making support with intervention recommendations
• autonomous healthcare monitoring systems, including outpatient monitoring devices, with dynamic alerts and intelligence patient care coordination
• robotic surgeries or automated diagnostic procedures with precision tasks to improve surgical outcomes

Agentic AI in healthcare, more nascent than generative AI in this space, is estimated to have a relatively high CAGR of about 45-50% for the next 5 years, reaching $5 billion per annum during this time period. While agentic AI is just gaining experience in healthcare, it has an even higher potential market than generative AI in healthcare with the infinite number of tasks that can be executed. 

Future applications of GenAI and agentic AI in healthcare

In the future, GenAI can be deployed for precision medicine with personalized drug discovery and therapeutics to accommodate individual genetic and pharmacogenomic profiles; virtual patients in the form of digital twins for “n=1x” clinical trials; and immersive experiences of medical education (for both students and patients) and clinical training with AI-enabled tools for realistic learning and real-time assessment. Agentic AI can also be more mature in the near future with applications such as fully autonomous diagnostic systems that have edge AI embedded in medical devices that autonomously conduct diagnostic procedures and data interpretations with appropriate interventions; adaptive healthcare logistics management that will deploy AI agents to optimize resources and scheduling to maximize efficiency; and possibly autonomous robots for easier procedures such as biopsies, minimally invasive procedures, or radiotherapy.

Combining generative and agentic AI as an AI dyad in the future will leverage the strengths of each AI tool: generative AI for its predictive insights and personalized strategies and agentic AI for its autonomous execution and adaptive decision-making. This AI generative-agentic AI dyad will finally deliver a personalized and intelligent healthcare delivery system.

Generative and agentic AI will be key themes at AIMed25, the world’s longest-running event dedicated to artificial intelligence in medicine and healthcare. Since 2013, AIMed has brought together over 1,000 clinicians across 50+ subspecialties, along with healthcare leaders, data scientists, trainees, investors, and innovators from around the globe.

This year’s 3-day meeting, taking place November 10–12, 2025 at the Manchester Grand Hyatt in San Diego, features dedicated tracks on AI in pediatrics and neonatology, AI in health professional education, and AI and mental health. Attendees can also expect hot-topic breakfast workshops, specialty breakout sessions, an abstract competition with scholar awards, the popular American Board of AI in Medicine (ABAIM) course, and—for the first time—a Chief AI Officer agenda.

Join us as we explore the future of healthcare, including the role of AGI, generative AI, agentic AI, large language models, intelligent XR, and much more.

We look forward to seeing you there!