The future of healthcare
Alvin W. Graylin using GPT-4O
Artificial intelligence is set to revolutionize healthcare and change the way clinical work is done in the US and abroad. While countries implement these systems in their own way, many utilize AI for all kinds of benefits, such as addressing lack of clinical work, improving diagnosis, and dealing with chronic diseases in groups.
So let's talk about some of these results. One of the first things to realize is that today's implementation of AI healthcare proves that this technology is not just about making beautiful photos or writing artificial poems. Some of us tend to think of the humanities as the first (or only in the case of myopia) use case of a large-scale language model. However, just like human undergraduates, LLM can be used to find artistic or scientific results. They can become artists, or they can become doctors. That said, many of these top use cases require significant integration. This means not only connecting to your hardware setup and training or prompting for the LLM model, but also to your existing infrastructure and business operations. That's easier said than done.
Alving Leilin from Virtual World Society and Genai consultant Karl Chao have recently sat down to talk about the reality of AI across healthcare, the state and around the world. I thought this would be helpful in an analysis of these healthcare systems and how this will unfold, and an understanding that international trade is subtle. And we operate a variety of stakeholders from their own perspective in the global economy, and in the world economy, that are globally interconnected.
The rise of open source AI in healthcare: On-premises solutions shaping the future
Artificial intelligence is revolutionizing healthcare, transforming clinical workflows, addressing key challenges such as workforce shortages, diagnostic accuracy, and chronic disease management. In the US, healthcare is leading the adoption of generation AI, with investments reaching $500 million in 2024, 67% higher than the second-ranked sector, legal services. This growth underscores the possibilities of AI, but real-world implementations require more than just cutting-edge technology. A solution is needed that focuses on seamless integration with existing systems, cost transparency, and tailors to privacy concerns.
The Benefits of Open Source: Enterprise Adoption
One of the most important changes in AI adoption is the move towards open source models such as DeepSeek R1 and V3. These models provide businesses with cost-effective, transparent, and customizable solutions, making them ideal for healthcare applications. Unlike its own systems, open source AI allows organizations to audit algorithms, ensure compliance, and adapt models to specific clinical needs without vendor lock-in.
Nvidia's recent strategic shift highlights this trend. Rather than focusing solely on hardware, the company partners with companies specializing in industry-specific AI solutions built on open source frameworks. For example, tools equipped with DeepSeek have already demonstrated a 40% reduction in diagnosis time and a 28% improvement in the identification of rare diseases. These advances, combined with open models and domain expertise, highlight ways in which they can produce tangible results in precision medicine.
Cost Breakdown: Domain-specific software controls AI deployment
An important but often overlooked aspect of AI implementation is cost distribution. Hardware such as GPUs and inference chips are attracting attention, but software services account for almost 70% of the total cost of deployment. This includes fine-tuning the model, integration with the Electronic Health Record (EHR) and ongoing maintenance. The open source model helps reduce these costs by reducing licensing fees and allowing for internal customization.
On-premises and private cloud: a new frontier in healthcare AI
Privacy and data security are driving a major shift from the hyperscalar cloud platform to on-premises and private cloud deployments. As Alvin Graylin pointed out, “Most organizations are extremely hesitant to post customer or patient data in the cloud.” This is especially true in healthcare where regulatory compliance (such as US HIPAA) and patient confidentiality are the most important.
In China, for example, strict data localization laws allow hospitals to keep patient records scheduled and increase the demand for private AI deployments. The US is more flexible, but concerns about cloud security and vendor lock-in are pushing healthcare providers to hybrid or fully on-premises solutions. Karl Zhao highlighted this trend, noting that “software and deployment flexibility are often underestimated in AI plans.”
Future: Specialized AI ecosystem
Open source models, cost transparency and convergence of on-premises solutions are restructuring healthcare AI. Companies like Stryker, Boston Scientific and Medtronic have already seen stock profits tied to AI innovation, but AWS and Google Cloud Providers face competition from localized inference chips (such as TPUs).
As Graylin properly summed up, “AI is not just plug-and-play; it takes time to integrate with existing systems.” The future belongs to an ecosystem where open models, domain expertise and secure infrastructure combine to create real-world impacts. For healthcare, this means faster diagnosis, improved patient outcomes, and a more sustainable AI adoption curve. This prioritizes privacy, cost-effectiveness and scalability.
Conclusion:
Technology isn't the only AI revolution in healthcare. It's about how that technology is implemented. Open source models such as Deepseek, combined with a clear understanding of on-premises deployment and cost structure, pave the way for a new era of enterprise AI.
