Large Language Models (LLMs) like GPT-4, Gemini, R1, and others have become the talk of the industry and social media trends—and for good reason. They’ve revolutionized how we interact with technology, enabling everything from conversational AI to content generation at scale. But here’s the truth: LLMs are no longer a differentiator. They’re a commodity.

The real magic—and the real value—lies in what you build on top of them.

The Commoditization of LLMs

Today, access to powerful LLMs is easier than ever. Open-source models, APIs, and cloud platforms have democratized AI, making it accessible to startups, enterprises, and hobbyists alike. While the underlying technology is impressive, it’s no longer enough to simply say, “We use GPT-4.” Everyone can do that.

I will not spend time using LLMs to conduct search engine queries and getting amused by asking jokes or “how are you doing today” questions. The differentiation comes from how you leverage these models to solve real-world problems, create unique user experiences, or streamline complex workflows, for instance in healthcare settings.

The Compute Power Factor

One of the most exciting developments in the LLM space is the emergence of models like R1, which are designed to be more efficient, requiring significantly less compute power compared to behemoths like GPT-4 or Gemini. This is a game-changer for healthcare organizations, many of which operate under tight budget constraints and cannot afford the massive infrastructure needed to run larger models. With lower compute requirements, models like R1 make it feasible for smaller hospitals, clinics, and even rural health systems to leverage advanced AI capabilities without breaking the bank. However, compute efficiency alone isn’t enough—it’s how these models are fine-tuned, integrated, and deployed that will determine their real-world impact.

The Real Innovation Happens Above the Foundation

Think of LLMs as the foundation of a hospital. A strong foundation is critical, but it’s the specialized departments, cutting-edge equipment, and skilled staff that save lives. In healthcare settings, whether in large hospitals, integrated health systems in the USA, Canada, or the UK, there’s a wealth of opportunities to enhance care delivery, improve clinical decision-making, and streamline operations by leveraging LLMs in a more targeted way.

Similarly, the true potential of LLMs in healthcare lies in the layers above the model:

1. Clinical Decision Support: LLMs can analyze vast amounts of medical literature, patient records, and real-time data to assist clinicians in diagnosing conditions, recommending treatments, and predicting outcomes. But the real innovation is in building systems that seamlessly integrate these insights into clinical workflows without disrupting care delivery.
2. Patient Engagement Personalized: Generic chatbots are helpful, but LLMs can be tailored to provide personalized health advice, medication reminders, and post-discharge follow-ups. For example, an LLM-powered virtual assistant could help a diabetic patient manage their blood sugar levels by analyzing their diet, activity, and glucose readings.
3. Operational Efficiency: Healthcare systems are drowning in paperwork. LLMs can automate tasks like medical coding, insurance claims processing, and appointment scheduling. The real value, however, comes from integrating these capabilities into existing electronic health record (EHR) systems to reduce administrative burden and free up staff time.
4. Population Health Management: LLMs can analyze population-level data to identify trends, predict outbreaks, and recommend interventions. But the innovation lies in combining these insights with actionable strategies—like targeted vaccination campaigns or chronic disease management programs—to improve community health outcomes.
5. Interdisciplinary Collaboration: LLMs can bridge gaps between specialties by translating complex medical jargon into understandable insights. For example, an LLM could help radiologists and oncologists collaborate more effectively by summarizing imaging results and suggesting potential treatment pathways.
6. Ethical and Responsible AI: In healthcare, trust is non-negotiable. LLMs must be fine-tuned to avoid biases, ensure data privacy, and comply with regulations. Organizations that prioritize ethical AI and are transparent in terms of AI explainability, will not only build trust but also set themselves apart in a competitive market.

The Opportunity for Healthcare Innovators

The commoditization of LLMs isn’t a limitation—it’s an opportunity. It shifts the focus from “who has the best model” to “who can create the most value for patients and providers.”

For hospitals, clinics, and integrated health systems, this means:

• Focus on solving real healthcare challenges: Whether it’s reducing clinician burnout, improving patient adherence, or cutting costs, identify pain points and build solutions that address them.
• Integrate with existing systems: LLMs should enhance, not replace, the tools and workflows healthcare professionals already rely on.
• Prioritize patient-centered design: The best applications of LLMs will be those that improve the patient experience, and care pathways, discovery to diagnosis, and to recovery.

The Future Belongs to Healthcare Builders

LLMs are just the beginning. They’ve given us a powerful tool, but it’s up to healthcare leaders, clinicians, and technologists to decide how to use it. The future of healthcare AI isn’t about who has the biggest model—it’s about who can build the most impactful, innovative, and meaningful applications on top of it.

So, what will you build next to transform healthcare?

Dr. Anjum Ahmed has several publications to his credit, including the Best Seller Book AI in HealthCare – From Basics to Breakthroughs. Learn more about this book, and the upcoming book on the Cognitive Cities Hospitals of the Future here.