At this month’s Paris AI Summit, the global conversation around artificial intelligence took an unexpected turn.
On day two of the summit, U.S. Vice President J.D. Vance called the world’s approach to AI “too self-conscious, too risk-averse,” warning that overregulation could “paralyze one of the most promising technologies we have seen in generations.”
His remarks reflect a shift in how political and business leaders are thinking about AI—not just as a technology to regulate but as a force to accelerate.
AI Is Charging Ahead—Healthcare Is Its Biggest Frontier
This change in perspective couldn’t come at a more pivotal moment.
Just last month, a little-known Chinese company unveiled DeepSeek-V3, followed by a high-powered reasoning model called DeepSeek R1. Despite the company’s relative obscurity, independent evaluations suggest that its products already rival those of industry leaders like OpenAI, Google and Anthropic.
What’s more, DeepSeek’s models are open source, freely available for individuals and companies to download, use and build upon.
While these developments will transform dozens of industries—from finance to climate science to manufacturing—nowhere will the impact be greater than in healthcare.
However, DeepSeek’s greatest impact on medicine won’t come from its model alone. Instead, it will come from how healthcare innovators leverage its open-source availability to build a new generation of AI-powered medical tools.
Until now, only multibillion-dollar companies could afford to develop AI systems. Training a large language model from scratch requires hundreds of millions of dollars in computing resources and access to thousands of expensive Nvidia GPUs. DeepSeek changed the game by proving that state-of-the-art AI models could be developed at a fraction of the previous cost (as low as $6 million, according to the company).
How did DeepSeek do it? Its engineers pioneered a radically different approach to AI development, incorporating three key breakthroughs:
- Mixture Of Experts: Instead of training a single massive neural network, DeepSeek divided its AI into smaller, specialized “expert” networks, each focused on a distinct subject area. This reduced the need for constant communication between GPUs and drastically lowered energy consumption.
- Mathematical Compression: By optimizing how numerical data is processed and stored, DeepSeek cut memory requirements and reduced computing costs—without sacrificing accuracy.
- Knowledge Distillation: Rather than training its model from scratch, DeepSeek’s AI learned from existing models, extracting and refining knowledge to train faster, cheaper and more efficiently.
While all these innovations have contributed to DeepSeek’s early success, the widespread application of knowledge distillation will have the greatest impact. By dramatically lowering the cost and time required to train AI models, this approach will make it possible for smaller healthcare startups to build hyper-specialized AI applications without needing billions of dollars in investment capital.
What Knowledge Distillation Means For Healthcare
Until now, the most advanced generative AI models (i.e. OpenAI’s GPT-4o, Google’s Gemini and Anthropic’s Claude) have been designed as broad, general-purpose systems. Trained on vast datasets spanning law, finance, literature and medicine, they can answer an impressive array of medical questions but lack the depth and specificity required for real-world patient care without direct clinician oversight.
This is where knowledge distillation and DeepSeek’s open-source approach change the game. Instead of requiring massive resources to build AI from the ground up, smaller healthcare companies can now take existing AI foundations and refine them, incorporating disease-specific data and key learnings from millions of patient interactions. The result will be a new generation of hyper-specialized AI tools capable of improving diagnosis, treatment and chronic disease management.
Instead of relying solely on general knowledge, these AI-powered healthcare tools will be designed to emulate how doctors and nurses respond to patient inquiries—be it for immediate medical advice or long-term chronic disease management.
Unlike many industries where AI development is limited by a lack of new data, healthcare presents a vast, untapped opportunity for training advanced generative AI systems.
Currently, 97% of hospital bedside monitor data is discarded, never analyzed for improving patient care. Likewise, millions of phone and video interactions from medical call centers and chronic disease management programs are recorded for legal and quality assurance purposes but have not been incorporated into the training of large AI models like those from OpenAI or Anthropic. While human supervisors review some of this data to improve patient guidance, it has never been systematically leveraged to enhance AI-driven medical support.
Here are two AI-powered healthcare applications that will combine knowledge distillation from existing generative AI models with in-depth training on previously unused medical data—paving the way for more specialized, patient-centered solutions:
1. Acute Diagnoses With AI Virtual Care Teams
Millions of patients rely on nurse advice lines and telemedicine services for medical guidance. Companies like Teladoc and Omada Health employ human clinicians to help patients manage acute symptoms and conditions. While these services are valuable, they’re expensive, labor-intensive and difficult to scale.
With knowledge distillation and real-world training data, AI-powered virtual care teams could provide patients with the same expertise at a fraction of the cost.
Here’s how:
- Instead of developing a general-purpose AI from scratch, new models will extract relevant medical knowledge from existing large language models (just like DeepSeek did using distillation).
- These AI-powered assistants will then be trained on millions of real patient interactions with clinicians, analyzing call center transcripts, nurse consultations and telemedicine visits to refine their accuracy and decision-making.
- Before deployment, AI-powered care teams will be rigorously tested against human clinicians in real-world scenarios. Once they demonstrate a 10% superiority in accuracy, response quality and empathy, they will be eligible for FDA approval.
2. Provide Chronic Disease Management With AI Health Agents
When chronic conditions like diabetes, heart failure and hypertension are poorly managed, patients face a heightened risk of life-threatening complications. According to the CDC, as many as 50% of heart attacks, strokes, cancers and kidney failures could be prevented with more effective chronic disease management. Reducing these avoidable medical crises would dramatically cut down on the need for costly treatments, emergency room visits and hospital admissions.
The issue isn’t a lack of medical knowledge. Doctors know how to prevent these complications. The real challenge is time. Most patients see their physician just once every three to six months, with little to no oversight in between visits. As a result, many chronic diseases remain uncontrolled for years, leading to worsening health and skyrocketing medical costs.
To fill this gap, chronic disease management programs have been developed to provide ongoing patient support. But these programs rely heavily on human clinicians, making them expensive and inaccessible to most Americans.
Today, only 60% of hypertension cases—the leading cause of strokes—are well-controlled. The numbers are even worse for diabetes, the top cause of heart attacks and kidney failure.
AI health agents, paired with home-based wearable monitors, can change this equation, providing real-time disease management at a fraction of the cost. These tools would make continuous, personalized care available to all patients, regardless of income or location.
Here’s how:
- Like AI virtual care teams, AI health agents will first gain broad medical knowledge through knowledge distillation. Then, they will develop disease-specific expertise by analyzing real patient interactions from chronic disease management programs like Omada Health and Teladoc’s Livongo.
- The AI will also connect to home monitoring devices, tracking blood sugar levels, weight, breathing patterns and daily activity via Bluetooth-enabled sensors. Instead of waiting for an in-person visit or virtual interaction with a nurse, patients will receive real-time alerts when their condition is not responding to treatment as expected.
- Consequently, when a serious health issue arises—such as early signs of fluid retention in a heart failure patient—the AI will immediately identify the problem and alert both the patient and their physician, enabling faster intervention and preventing costly ER visits and hospitalizations.
These AI agents will also undergo rigorous clinical testing. FDA approval will require these models to outperform human clinicians in information accuracy, successful disease control and early identification of complications.
The Road Ahead: Driving Better Access, Equity And Health
Today, AI-powered virtual care costs around $9 per hour. As AI adoption scales, costs will drop, making expert-level healthcare guidance affordable and accessible to all Americans. The result will be improved patient outcomes and lower overall healthcare costs.
These tools won’t replace doctors and nurses, but they will fill critical gaps in care, providing continuous support between office visits while enhancing disease management.
Contrary to what many analysts might assume, the biggest cost savings won’t come from replacing human clinicians with AI. They’ll come from preventing complications in chronic diseases that afflict 60% of Americans and account for 70% of healthcare expenses.
Until now, developing innovative GenAI solutions for medicine required billions in capital, limiting opportunities for all but the largest companies.
With the availability of open-source models, application of knowledge distillation and additional training based on data and human interactions, those barriers have been lowered.
The only question is: Who will lead the charge?
