Position: Adjacent Technologies Are the Key Enablers of Scalable and Safe Clinical MLLM Deployment

Azmine Toushik Wasi, Md. Iqramul Hoque

Keywords: Multimodal Large Language Models, Clinical AI Ecosystem, Healthcare Infrastructure, Precision Medicine Technologies

TL;DR: We argue that clinical impact of MLLMs depends not just on the models themselves but on a robust ecosystem of adjacent technologies enabling safe, scalable, and trustworthy deployment.

Abstract: Rise of Multimodal Large Language Models (MLLMs) represents a paradigm shift in healthcare, with the potential to revolutionize diagnostics, personalized medicine, and predictive analytics. In this position paper, we argue that the clinical impact of MLLMs depends not solely on the models themselves, but critically on an integrated ecosystem of enabling technologies. High-fidelity data curation pipelines, multimodal data lakes, continuous model monitoring, secure API infrastructures, workflow orchestration, and EHR/PACS connectors collectively form the foundation for scalable, safe, and trustworthy deployment. We contend that strategic investment and cross-disciplinary collaboration in these adjacent technologies are essential for realizing the full potential of MLLMs in real-world clinical settings, establishing a distinct and emerging sector within digital health.

Submission Number: 51