Editorial
The world of artificial intelligence (AI) is moving at a dizzying pace, and healthcare is no exception. As a healthcare professional, I’ve seen firsthand how technology can reshape patient care, improve workflows and create new possibilities.
Now, we’re talking about foundation models — the powerful AI systems that serve as a springboard for countless applications. But here’s the rub: you’ve got a choice between commercial and open-source options, and figuring out which one is the right fit for your needs can feel like a high-stakes game.
I’ve spent a lot of time thinking about this, and it comes down to understanding what makes each type tick, how they fit into the healthcare landscape and how they can support our most important purpose: our patients. This isn’t a technical game; it’s a strategic one. We're not just acquiring technology; we’re acquiring partners in our transformation.
Commercial Foundation Models: The All-in-One Solution
Think of commercial foundation models as ready-made, high-performance machines. They're created and maintained by established companies with massive resources. This means they usually offer substantial computing power, extensive training data and robust support. These models are often pre-trained on huge datasets. In healthcare, this could include medical literature, patient records and clinical guidelines, allowing them to perform complex tasks like diagnosis support, drug discovery and personalized medicine.
The advantage here is speed and reduced development time. I think many of us value the “plug and play” aspect; you don’t need to be a deep AI expert to use one, which is fantastic in fast-paced environments. There’s also usually a dedicated team responsible for updates and ongoing improvements. With a commercial model, you're paying for a level of service and reliability.
Let's take, for example, a large hospital system looking to improve diagnostic accuracy using AI-powered image analysis. By going with a commercial model specializing in medical imaging, they can bypass the long development process, get up and running faster and rely on the vendor to maintain the system, freeing up their resources to focus on patient care. A recent study in the Journal of the American Medical Association (JAMA) showed that AI models in radiology improved physician diagnostic accuracy by 12.5% and reduced interpretation time by 10% when compared with human analysis alone.
Related Article: 5 AI Case Studies in Health Care
Open-Source Foundation Models: The Collaborative Frontier
Open-source models represent a different approach. These are typically created by the AI research community and made freely available, with the code and data openly shared. This means anyone can use, modify and contribute to them. The great thing about open source is flexibility and customizability. You have the power to shape and tailor the model for your specific requirements, data and processes.
Open-source also encourages innovation and collaboration. You're not locked into a specific vendor. Instead, you become part of a vibrant community, sharing knowledge and working together to improve the model. A smaller, specialized clinic that focuses on treating rare diseases, for instance, might use an open-source model to build a custom AI diagnostic tool, training it with their unique patient data to get more specific outcomes. They can also collaborate with other researchers and clinics around the world, leading to a continuous improvement.
There’s a case study of the development of an open-source natural language processing model that helped reduce report processing time for a specific type of rare disease, resulting in a faster time to diagnosis for children in specific parts of the world. This wasn’t a big commercial effort, but the combined power of researchers around the world working to improve the tool.
Making the Choice: A Strategic Framework
So, how do we choose? It’s not as simple as “one is better than the other.” It depends on our specific needs, resources and goals. I suggest we focus on a few important questions:
- What’s your budget? Commercial models come with fees and ongoing costs. Open source is usually free, but requires in-house talent or external consultants for customization.
- Do you have the technical talent? Open source requires significant expertise in AI development, implementation and maintenance. Commercial solutions reduce that requirement.
- What’s your need for customizability? If you have very specific needs or data sets, an open-source model offers greater flexibility. Commercial models can also be adjusted but with some restrictions.
- What level of support do you require? If you need quick help and dedicated support, commercial providers have the edge. The community around open-source models can offer assistance, but it may take more time.
- What are your regulatory and privacy requirements? It's always crucial to make sure that data used complies with regulations like HIPAA. How secure is the model? Consider where your data is kept and who has access.
- What are your long-term goals? Are you looking for a quick solution or a long-term collaboration? Depending on your vision, you can make a decision between a commercial or an open source option.
The National Academy of Medicine recently published a report highlighting the need to be very careful in selecting AI tools to improve healthcare. They state that there is a need to go beyond technical details to choose technologies that align with your values, vision and goals.
Related Article: 10 Top AI Health Care Companies
Real-World Implications of Choosing the Right Model
Let's bring this to a practical level. Think about a rural health clinic. They might not have a large tech budget or in-house AI specialists. A commercial model with strong customer support and a ready-to-use interface could be the best option. The clinic could choose a model that specializes in patient triage, helping them prioritize patient needs effectively and freeing up resources for more complex cases.
On the other hand, a large research university could create a new medical imaging model using an open-source platform to work on a specific project. It has the expertise and data to make significant progress, and it could then be published and used in other parts of the world with similar needs. This shows how you can change the landscape and develop new solutions if you have the resources.
A Call to Action
I think the future of healthcare is intertwined with AI, and foundation models will be a building block. Instead of focusing on the technical details, I encourage us to view this as a decision about the future of our practices and, more importantly, the future of our patients. My invitation is to ask not what’s easiest, but what’s right. What tools will help us improve care, reach more people and build a better healthcare system?
It's about selecting a partner and a way of working that aligns with your purpose. It’s not about the tech; it’s about how we can use it for good. Let's be smart about it, informed and collaborative and, above all, let’s be clear about the direction we want to go.
Choosing between commercial and open-source foundation models is not a binary decision; it's a strategic decision that requires us to look carefully at the landscape and understand our unique needs and limitations. By asking the right questions and aligning our choices with our goals, we can create a better future for healthcare.
Frequently Asked Questions:
How often do foundation models need to be updated?
Models should be updated as new data and improvements to the model come out. This is especially important in healthcare, where the latest scientific and medical information is critical for accurate decisions and recommendations. Commercial models usually include updates in their service agreements, while open-source models need more in-house resources for updates.
If my organization has a limited budget, which type of foundation model should I consider?
In this case, it is best to consider open-source models, as they are free to use. However, you must also factor in the cost of the technical expertise needed for development and implementation. If your team does not have that ability, you may need to seek help from an external provider. In some cases, the cost of external experts might outweigh the value of using an open-source model, so an analysis is always recommended.
What is the impact of data privacy when using these models?
Data privacy is a key concern, particularly in healthcare. Both commercial and open-source models need strong data privacy measures. Commercial options might handle data with more control, but open-source models, when implemented correctly, give you greater control over data use. Always make sure to comply with HIPAA regulations and other relevant privacy laws.
How do I choose the right foundation model for my organization?
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The right model is selected through a detailed assessment of your organization's particular needs, considering budget, available expertise, customizability requirements and the need for ongoing support. It is important to align your strategic vision with your organization’s goals when choosing the right type of model. Look at which approach will serve you better in the long run.
Is it possible to mix commercial and open-source models?
Yes, it's possible to use a hybrid approach. For example, you could use a commercial model for tasks like basic patient management and use open-source models for specialized research applications. This requires strong technical expertise in your team to integrate the two approaches.
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