Supporting real-world medical AI research
Datamint powers active research projects across medical imaging — helping teams run structured, reproducible studies from data to validation. Used by researchers in academic hospitals and institutions to build, validate, and advance AI-driven studies.
A foundation for reproducible medical AI research.
Medical AI research is complex, Not just because of the models, but because of the need for structure, reproducibility, and collaboration across teams.
Datamint provides the foundation for running these studies in a way that’s organized, traceable, and ready for publication or validation.
So your research doesn’t just move forward — it holds up.
Active research powered by Datamint.
From early-stage studies to advanced validation work, Datamint supports researchers across the full lifecycle of medical AI development.
Each project benefits from:
1. Structured data and annotation workflows
2. Connected experimentation and evaluation
3. Full traceability across the research lifecycle
4. Reproducible results and preserved context
Fabiana Almeida is an oral maxillofacial radiologist and clinical researcher passionate about diagnosis. She is deeply committed to improving children’s arthritis diagnosis and to reducing the pain and long-term facial changes caused by undiagnosed TMJ (Temporomandibular Joint) arthritis. Identifying arthritis in the TMJ through imaging is difficult, requiring specialized training for radiologists or rheumatologists.
“AI can help health professionals to identify TMJ issues, improving the diagnosis process and potentially avoiding life-changing problems for children. Datamint has been instrumental in streamlining data and annotation workflows, which will let me get to solid results faster”.
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What these research teams have in common
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These teams aren’t all solving the same problem — but they are working in the same way.
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They run structured, collaborative workflows where data, annotations, experiments, and results are all connected. Every step is captured as it happens, creating a complete and reproducible record of the research.
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This means no missing context, no rebuilding past work, and no uncertainty when it’s time to validate or publish.
Why we’re sharing these projects early.
We’re sharing these projects while they’re still in progress because medical AI research shouldn’t be a black box.
By making this work visible early, we can highlight how these studies are being structured, run, and validated — not just the outcomes.
Because how the work is done matters just as much as the results.
How Datamint supports active research.
A flexible, non-linear workflow designed for real-world medical imaging research.
Data Intake
Annotation & Review
Model Training
Evaluation & Insight
Publication & Continuity
Commercialization/FDA Clearance
Refinement
Retraining
Refinement
Retraining