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) marks a paradigm shift in healthcare, with the potential to revolutionize diagnostics, personalized medicine, and predictive analytics. Yet, the transformative power of MLLMs cannot be realized in isolation. In this position paper, we argue that clinical impact of AI hinges not only on the models themselves but on an integrated ecosystem of enabling technologies. These include high-fidelity data curation pipelines, multimodal data lakes, model monitoring and audit tools, secure API infrastructures, workflow orchestration layers, and seamless connectors to Electronic Health Record and Picture Archiving and Communication System platforms. Far from being peripheral, these adjacent technologies are forming a critical foundation for scalable, safe, and trustworthy clinical deployment. As this ecosystem rapidly matures into a distinct sector within digital health, strategic investment and cross-disciplinary collaboration will be essential for healthcare systems and technology vendors aiming to harness the full value of MLLMs in real-world settings.

Submission Number: 129