AI agents and the next leap toward proactive, personalized health care

When OpenAI announced in 2025 that it would prohibit the use of ChatGPT for health care advice, the decision made headlines and revealed something deeper about where patients are headed. The fact that millions of people were already asking generative models for guidance underscores a fundamental shift that consumers are asking for health care to be personal, immediate, and proactive.

They are not looking for one-size-fits-all symptom checkers. They want the kind of tailored, real-time guidance they get from digital experiences in other parts of their lives applied to their health.

The problem isn’t that patients are turning to technology. The problem is that the health care system still meets consumers too late, when illness has already taken hold, or after they’ve disengaged from care.

AI agents can change that.

By blending large language models with health care-specific data and workflows, these intelligent systems are stepping in to bridge persistent gaps in care, providing hyper-personalized, real-time guidance before a condition worsens.

From reactive to proactive care

For decades, U.S. health care has been structured to respond to illness and not anticipate it. We have optimized health care for acute encounters and transactions, not prevention or continuity.

With autonomous AI, it’s now possible to invert that model. Health care has had analytics and predictive models for a long time. They are designed to detect early patterns, initiate engagement, and help people stay ahead of risk. But it was often a dead-end inside organizations — no one to interpret the insights, act, and reach out to members. Call centers were overwhelmed, and mobile and web usage was paltry, and providers were too busy. But now the world has changed.

Imagine a digital care agent that notices that a patient hasn’t refilled a diabetes prescription or scheduled a follow-up exam. Instead of relying on a human call center weeks later, the agent proactively reaches out (in natural language) to remind, motivate, or connect the patient to a telehealth visit.

The outreach is adaptive. And, if the individual expresses confusion about side effects or cost, then the agent can route to the right clinical resource or financial support program.

These “smart cohorts” are groups of individuals dynamically identified and engaged based on evolving health behavior, not static attributes in a database. They are “learned” for your members, regional needs, and benefit design.

The power lies in the personal context where the agent understands that two patients with the same diagnosis might need entirely different messages or interventions. One might respond to a practical reminder, while the other may need reassurance or a connection to behavioral health.

What hyper-personalization really means

Health care often borrows the language of “personalization” from consumer tech, but this isn’t completely the same as creating a Netflix experience for care. The goal isn’t to entertain or sell, instead, it is to align health care outreach with human need at the moment it matters most.

AI agents excel at this because they can continuously learn from millions of data points. Things like lab results, claims, communication history, even non-clinical factors such as transportation access or food insecurity allow the agent to tailor every interaction.

According to McKinsey, engaging health care consumers in a highly personalized way leads to better member experiences, higher quality of care, and a reduction in avoidable health care costs.

The larger opportunity is not only operational efficiency but also rebuilding trust. Patients who feel seen and understood are more likely to act. Hyper-personalization, when done responsibly, can transform digital communication from noise into care.

Why health care needs its own architecture

Deploying AI agents responsibly in health care, however, is technically and ethically complex. Consumer-grade AI models are not trained for the nuances of clinical interpretation, privacy compliance, or the emotional weight of medical conversations.

An effective agent must integrate securely with EHRs, respect consent frameworks, and maintain transparency around its reasoning.

That’s why health care needs purpose-built platforms that fuse domain-specific data, compliance frameworks, and ethical guardrails.

AI agents must augment clinicians, not replace them, offering the right message or action, then looping in humans where empathy and judgment are irreplaceable.

Operationalizing these systems also demands careful change management. Health plans and provider groups need to define metrics of success that go beyond open rates or chat completions.

The real measure is whether care gaps close faster, whether patients stay connected longer, and whether clinical workloads become lighter.

A more human system through automation

Ironically, the more intelligent our systems become, the more human the health care experience can be. When AI agents take on repetitive coordination tasks like appointment reminders, benefits navigation, medication adherence, clinicians reclaim the time and emotional bandwidth to focus on complex or sensitive cases.

The future is about creating a proactive care ecosystem where every patient interaction is timely, relevant, and rooted in understanding.

AI agents can function as that connective tissue between data and empathy, between insight and action.

The OpenAI restriction may have drawn a line, but it also illuminated the path forward. People are ready for digital health companions that guide, remind, and educate responsibly.

Health care doesn’t need to become more reactive, but it does need to become more responsive. And AI agents, done right, can help us get there.

Anmol Madan, PhD, is an entrepreneur, computer scientist and executive who has been leading the digital health and AI revolution over the last two decades. He is currently the CEO and founder of RadiantGraph, a machine learning and AI company bringing Intelligent Personalization to health plans and health services organizations. Anmol served as Chief Data Scientist at Teladoc Health & Livongo and previously co-founded Ginger.io serving as its CEO for seven years. During his tenure as CEO, Ginger.io built an AI-driven member and clinical product, established a high-growth distribution model with employers, became broadly available as an in-network health-plan benefit, and raised $35M in venture funding from top-tier Silicon Valley VCs. Anmol received a PhD in machine learning applied to human behavior from the MIT Media Lab, has authored 20+ scientific publications and holds over a dozen patents related to machine learning in healthcare. He has been recognized as one of Fast Company’s 100 Most Creative people and as a World Economic Forum Technology Pioneer.