How AI is Transforming Health Care: From Drug Discovery to Patient Care

Back in 2019, long before ChatGPT made artificial intelligence a household word, Microsoft CEO Satya Nadella called AI "the most transformational technology of our time" and described health care as "perhaps AI's most pressing application."

Fast forward to today. The tech giant just unveiled Copilot Health, a feature within its Copilot app that the company describes as an AI concierge doctor — one that can access a user's medical records, test results, medications, visit notes and biometric data from wearables, with the user's consent.

Microsoft is hardly alone. AI has moved from promise to practice, reshaping how medicines are made, care is delivered and systems are managed. And few have watched the transformation more closely than Marc Ruggiano, founding director of the UVA LaCross Institute for Ethical Artificial Intelligence and co-lead of the Darden Health Care Initiative.

"AI applications in health care are a bright spot, as we see mounting evidence that AI can be productively and safely employed against some of the most complex issues in the sector," he says.

His new technical note, "Applications of AI in Healthcare," co-written with Surbhi Guha (Class of 2026), details how AI is reshaping pharmaceuticals and biotechnology, patient care, health insurance and public health.

Their conclusion is blunt: "AI is no longer an experimental add-on in health care; it is becoming a critical enabler across the entire system."

Why Health Care's Story Matters for Business Leaders

Health care is notoriously complex — with strict regulation, entrenched legacy systems and high stakes where errors can cost lives. If AI can make inroads here, there are lessons for every sector grappling with adoption.

Ruggiano and Guha distill the opportunity into the industry's "quadruple aim": enhancing population health, improving patient experiences, boosting provider satisfaction and managing health care costs.

"AI may be the breakthrough that finally allows us to make step-change improvements in the U.S. health care system across all of the vital objectives described by the quadruple aim," says Ruggiano.

The parallels for business leaders across industries are obvious: better performance, stronger customer engagement, higher employee satisfaction and lower operating costs.

Four Key Applications: Lessons for Every Industry

Four Domains, Four Lessons

1. Pharmaceuticals and Biotechnology: Speed Matters

AI is compressing drug discovery timelines from years to months. In 2025, for example, Recursion Pharmaceuticals said its AI-based drug discovery platform took just 18 months to move a molecule into clinical testing as a cancer drug candidate, far faster than the industry average of 42 months.

The authors write that tools such as DeepMind's AlphaFold and companies like Exscientia (now part of Recursion Pharmaceuticals) are making once-impossible searches for viable compounds suddenly tractable.

But they caution: "AI models often depend on incomplete or biased datasets, particularly in the case of rare diseases or unpublished failed experiments, which can limit their reliability."

Leader takeaway: Move fast, but invest in data quality and bias checks before betting the business on AI-driven insights.

2. Patient Care: Automate Admin, Elevate Humans

Doctors now spend nearly twice as much time on administrative tasks as on patient care, and nearly half of U.S. nurses report symptoms of burnout.

Generative AI scribes and smart diagnostic tools are reversing that equation. As Ruggiano and Guha write, "AI systems are easing documentation workloads, optimizing staffing and improving appointment scheduling. Tools like Nuance DAX use ambient AI to generate clinical notes during patient visits, significantly reducing after-hours work for physicians."

Leader takeaway: Target pain points that drain your workforce's energy. Let AI handle the paperwork so humans can focus on high-value, high-touch work.

3. Health Insurance and Payers: Transparency Builds Trust

Insurance may be the ultimate case study in complexity and consumer frustration. Here, AI promises both efficiency and personalization — but missteps are costly.

"Utilization management" is the process insurers use to decide whether a treatment or service is medically necessary and cost-effective. One of its key steps is prior authorization, in which the provider must obtain approval before performing certain procedures, prescribing certain medications, or ordering certain diagnostics.

"AI is being tested to streamline this workflow. These systems compare clinical records with insurer guidelines to determine whether a service qualifies for approval," the authors note.

However, they warn that in some cases, "AI systems used for prior authorization have been found to deny care at rates far higher than traditional reviews, prompting concern from physicians and patient advocates."

Leader takeaway: Don't automate judgment without safeguards. Keep humans in the loop, especially where decisions affect customer safety or trust.

4. Public and Population Health: Think Systemwide

From wastewater data predicting outbreaks to AI-optimized vaccine distribution, population-level insights are moving from lagging to leading indicators.

As the authors note, "AI is now playing a growing role in helping detect warning signs sooner. Public health teams can use machine learning models to analyze data from sources far beyond the hospital, such as social media posts, weather trends, wastewater signals and mobility data. These systems can flag unusual spikes in symptoms, predict likely outbreak zones or alert authorities when patterns shift."

Leader takeaway: Look beyond the walls of your own enterprise. AI's greatest value may come from spotting weak signals across the broader ecosystem.

Beyond Health Care: A Playbook for Adoption

For executives in any industry, the Darden technical note offers a roadmap:

- Lay the foundations. Invest in connected, integrated data systems before chasing moonshots.

- Build trust. Transparency, explainability and safeguards against bias will determine the speed of adoption.

- Partner widely. "Hospitals, insurers, and public health agencies are partnering with startups, cloud providers and tech giants to embed AI into the core of health care delivery and operations," according to the note.

- Keep humans central. Whether it's patients, employees or customers, human empathy and oversight remain irreplaceable. As the authors observe, "The lack of empathetic interaction, especially in sensitive areas like mental health, can make AI feel impersonal or dismissive. Without transparency and human-like understanding, users are less likely to engage consistently or rely on these tools for meaningful health decisions." In addition, human experts — physicians and other trained providers — remain the best resource for many people and should be the ultimate source for health-related decisions."

Looking Ahead

Health care's story is not just about doctors and patients — it's a test case for AI's integration into the most complex systems we have. As Ruggiano and Guha conclude:

"As we stand at this inflection point, one question emerges: If we can combine the power of AI with the purpose of health care, can we finally build a system that is faster, fairer, and more human?"

This article is based on the new technical note "Applications of AI in Healthcare" by Marc Ruggiano and Surbhi Guha (Class of 2026), published by Darden Business Publishing (April 2026).