Finally, the culture at SIIM stands out. There’s a real commitment to inclusion and a clear feeling of community since “everyone belongs at SIIM.” This culture of belonging has made it easier to engage fully, ask questions, and feel part of a shared mission.

HCB News: What trends in imaging informatics do you expect to shape the industry over the next few years?
NK: Over the next few years, I think we’re going to see exciting shifts in imaging informatics, especially with the rise of foundation models including LLMs, LMMs, and intelligent agents. Combined with model context protocols (MCPs), these tools will make it much easier to build agentic workflows—systems that can combine data sources with available tools to take on complex, multi-step tasks autonomously. This development has huge implications for radiology and healthcare. Rather than spending time on administrative or purely perceptual tasks, we’ll be able to focus more on higher-level cognitive work, contributing more meaningfully to patient care.
Another big shift will be the integration of data from across the healthcare ecosystem. Right now, valuable information is often siloed—genetic data in one place, imaging in another, clinical notes somewhere else and we rely on the human to integrate this information. But with better interoperability and intelligent systems, we’ll be able to bring together radiomics, genomics, proteomics, molecular imaging, blood-based biomarkers, and more. That advancement opens the door to truly personalized medicine, where diagnosis and treatment are tailored precisely to the individual. It’s a transformation I’m really looking forward to being part of.

HCB News: How is AI currently being integrated into medical imaging workflows, and what challenges still need to be addressed for wider adoption?
NK: AI is already playing a meaningful role in medical imaging workflows, particularly by supporting tasks like detection, quantification, triage, and diagnosis. These tools are helping improve accuracy and, in some cases, also bringing efficiency gains. On the reporting side, large language models (LLMs) are stepping in to help with everything from drafting reports and translating them for different audiences, to summarizing findings, recommending follow-ups, supporting clinical decision-making, and even helping with billing compliance. It’s a pretty comprehensive integration that will change how radiologists work.

AI is also helping connect the dots across the broader healthcare system. AI-powered care coordination tools are starting to streamline communication between specialists, breaking down silos and making it easier to collaborate across teams. That said, while adoption is growing, there are still challenges including interoperability between systems, consistent performance across institutions, regulatory approval of novel, general AI systems, and building transparency and explainability into AI outputs. Addressing these issues will be key to unlocking AI’s full potential in clinical practice.

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