The healthcare industry stands at the precipice of transformation. While artificial intelligence (AI) has been utilized in healthcare for decades, analyzing OMICS and supporting drug discovery, recent advancements in generative AI (GenAI) and reasoning models are redefining what’s possible, especially when connected to private data. This evolution represents not just incremental improvement but a fundamental shift in how technology can augment healthcare delivery.
The Accelerating Pace of AI Evolution
The GenAI movement that emerged around 2017 added a new dimension, enabling AI to create content. However, it was the 2022 release of ChatGPT that democratized access to these capabilities, creating a “Wright Brothers moment,” springboarding the industry of AI. Suddenly, everyone from children to healthcare professionals began experimenting with these systems, often finding productivity gains despite the limitations of early versions of the technology.
Just as organizations were adapting to this new reality, reasoning models emerged in late 2024. These systems do not simply generate content, but think through problems step by step, mirroring human cognitive processes. Within months, more efficient, open-source reasoning models followed, making this technology accessible even for regulated industries like healthcare (e.g. Med-R1 8B).
GenAI Reasoning Models in Healthcare
GenAI enables healthcare professionals to work more efficiently, freeing time to engage with patients. Unlike earlier models, recent GenAI reasoning models provide transparency into their decision-making process. These models can now power advanced AI agents using healthcare-specific models like Google AIM, Med-PaLM 2 or Med-R1. This auditability is crucial in healthcare, where understanding why a recommendation was made is often as important as the recommendation itself.
Before implementing AI agents and reasoning, agencies should define clear outcomes and goals. Here are several factors to consider when integrating GenAI into your agency:
- Data Strategy: The effectiveness of AI models depends significantly on the quality and privacy of your data. Organizations need clear protocols for creating evaluation datasets and managing sensitive patient information that can be kept sovereign.
- Infrastructure Decisions: Healthcare organizations must decide whether to deploy models in the cloud or on-premises, considering regulatory requirements and data sensitivity. A hybrid approach often provides the flexibility needed to address various use cases.
- Model Selection: Open-source models now trail proprietary options by only about six months in capabilities while offering cost advantages and greater control. Many organizations are adopting hybrid strategies, using proprietary models for cutting-edge applications and open-source alternatives for routine tasks.
- Scale Considerations: Small, specialized language models can be more efficient for specific healthcare tasks, while larger models may be necessary for complex reasoning about treatment options or research questions.
Agencies should prepare robust data governance frameworks and flexible infrastructure that spans cloud and on-premise environments to enable healthcare personnel to use GenAI effectively. Overall, GenAI enables healthcare professionals to work more efficiently, enabling them to connect more with patients.
Your Journey to an AI Future Starts Now
The future of healthcare will be augmented by reasoning models, making healthcare more affordable and accessible for all.
Some new, AI-driven areas to watch for include:
- Data Interaction: LLMs will navigate complex healthcare data ecosystems, from electronic health records to genomic data, answering nuanced clinical questions without requiring complex programming.
- Planning and Research: By functioning as collaborative partners in research, the models look to help design clinical trials, analyze research literature and develop treatment protocols.
- Actionable Workflows: Reasoning models will help automate clinical and administrative processes while incorporating human feedback in a continuous improvement cycle.
AI agents will begin to help address the acute staffing shortages plaguing healthcare systems worldwide. These digital assistants can handle routine documentation, answer common patient questions, and provide decision support, allowing clinicians to focus on direct patient care. As AI systems become more affordable and consumption increases, we’re likely to see a revolution in healthcare accessibility, particularly for underserved populations, with AI agents augmenting healthcare workers’ efforts.
The journey toward AI-augmented healthcare is accelerating faster than most experts predicted. For healthcare leaders, the question is no longer whether to embrace these technologies, but how to implement them to improve care while maintaining the human connection that defines healthcare.
The content of this blog was pulled from the Healthcare Information and Management Systems Society (HIMSS) panel, “Accelerating Enterprise GenAI.” To learn more about Nutanix GenAI, visit Nutanix’s AI Solution page.
