In recent years, advancements in medical technology have captured our attention, but while they might represent breakthroughs in the industry, their applications may come with hefty challenges. Now, imagine the potential of every new discovery powered by an AI tool that not only understands complex medical text but also adapts to a specific domain. This is the promise of Retrieval-Augmented Fine Tuning (RAFT), an alternative to big tech, more generalist Large Language Models (LLMs), which has proven to perform better for certain use cases and domains by adapting a smaller model to different sectors. In this case, healthcare.
This is especially important in specific areas, such as cancer immunotherapy, where different treatment protocols and patient data points are involved. In this context, organizations need AI solutions that can deliver precise answers while keeping sensitive information completely in-house. But does RAFT comply with these demands? In a recent research by Globant Enterprise AI’s technical team, they found interesting conclusions that answer this and more questions.
Why Cancer Immunotherapy Needs Smarter AI
Cancer immunotherapy utilizes the body’s immune system to combat cancer cells. Unlike traditional treatments, immunotherapy aims for a more targeted approach, potentially resulting in fewer side effects. That’s why the selection of this domain for the research was strategic, considering both its clinical importance and the highly sensitive, private nature of patient data involved in immunotherapy protocols.
RAFT is a method that cleverly addresses these needs by fine-tuning smaller AI models for specialized tasks. Think of it as teaching a “small-yet-mighty” model more domain-specific knowledge, which other models may lack, so it can become an expert in cancer immunotherapy, or other industry-specific domains, without hogging resources. In a nutshell:
- Fine-tuning = The process of further training a pre-trained model on a specific dataset to specialize it for a particular task. In this instance, the specialization is for cancer immunotherapy.
- Retrieval = Giving the model a “library” of specific, domain-relevant information whenever it needs it—like a digital reference desk.
This approach allows organizations to maintain control over their data while still benefiting from advanced AI capabilities, reducing computational costs and minimizing the risk of data exposure.
Surpassing the “Big Guys”
One of the study’s conclusions is that these models could achieve competitive performance compared to larger commercial models, demonstrating the potential for cost-effective and efficient AI solutions in healthcare. It’s kind of like a skilled craftsperson who masters a single, specialized trade rather than trying to do every job in the shop. By honing in on domain-specific data and carefully tuning the model’s parameters, the RAFT method allows these smaller AI systems to handle highly technical questions with remarkable accuracy, often at a fraction of the cost and computational load.
Even better, when plugged into a Retrieval-Augmented pipeline, these models consistently stay on point thanks to relevant medical references on the spot. This means they’re less likely to “hallucinate” answers, a common pitfall with large, generic AI systems. That’s huge news for businesses looking to reduce costly infrastructure needs, keep data completely under their own roof, and still deliver the same—or better—caliber of detailed, precision-focused answers.
Globant Enterprise AI: Where Innovation Meets Accessibility
This initiative was integrated into the Globant Enterprise AI (GEAI) platform, which serves as a hub for delivering advanced AI capabilities to clients. Through this exploration, Globant’s team aimed to refine their processes and enhance their ability to offer customized, efficient, and secure AI solutions that meet the specific needs of various industries. With multi-cloud capabilities and a design that effortlessly fits into enterprise environments, GEAI ensures businesses can seize the power of RAFT-ready models.
RAFT represents a new wave in AI, one where targeted expertise, budget-friendly deployments, and robust data privacy come together. Particularly for a field like cancer immunotherapy, but with the potential to expand to many more, this means faster and more accurate insights, minus the worries of sending sensitive patient information elsewhere. Organizations ready to harness AI breakthroughs for real, impactful change are primed to boost their edge with RAFT. And with Globant Enterprise AI on your side, you’ll have the tools and flexibility to bring these innovations to life, securely and scalably.
Read the full research here and find out what Globant Enterprise AI can do for your business.
