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Datamint is an end-to-end platform designed to support medical imaging AI research. It helps researchers manage data, coordinate annotation workflows, track and analyze  experiments, collaborate across teams, deploy mature models, and maintain full reproducibility — without building custom infrastructure. It is a complete “AI-lab-in-a-box” for clinical researchers who want to integrate AI into their research programme.

Datamint is built for two types of medical researchers. If you are a clinical researcher with minimal AI experience but who recognizes the impact that AI is having on medical advances, Datamint provides you with everything you need to successfully and efficiently run an AI research project.  If you are an experienced medical AI researcher, Datamint give you the tools to consolidate your workflow into one platform and reduce administrative overhead to let you focus on better and faster science.

Datamint addresses the operational and technical challenges of running medical AI research. Many labs rely on fragmented tools, manual file transfers, and spreadsheet-based coordination, which slows progress and makes reproducibility difficult. Datamint replaces these workflows with a unified, structured system built for academic and clinical research environments. This unified structure is especially valuable for researchers new to AI, who have the additional problem of having to create processes and procedures from scratch. Datamint encodes the hard-won experience of long time medical AI researches so those new to AI techniques can focus on the science.

Medical AI research depends on complex datasets, interdisciplinary collaboration, and strict reproducibility requirements. Without structured infrastructure, teams spend significant time coordinating tools instead of advancing science. Datamint provides the clarity and continuity needed to support responsible, scalable research.

Most teams can begin uploading data and coordinating annotation workflows quickly after onboarding. Datamint provides guided setup and support to ensure research teams reach meaningful value as early as possible.