Clinical AI you can verify.
Roshan AI builds clinical-grade models that surface the concepts and evidence behind every prediction. ShifaMind turns clinical reasoning into a defensible coding workflow.
Relevant clinical signals are surfaced, grounded in the source note, and carried forward as verifiable evidence.
A member of NVIDIA's startup program as Roshan AI scales clinical-grade infrastructure.
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See the evidence behind the output.
ShifaMind does not just rank a code. It preserves the clinical concepts and note-level evidence that make the prediction defensible.
Start with the note, not a black box.
ShifaMind begins with the language clinicians already use in the workflow.
“progressive dyspnea on exertion and orthopnea,” with “bilateral lower-extremity edema” and “BNP 1850” captured as clinically relevant signals.
View clinical note detail
“progressive dyspnea on exertion and orthopnea,” with “bilateral lower-extremity edema” and “BNP 1850” captured as clinically relevant signals.
Progressive dyspnea and orthopnea, with bilateral lower-extremity edema and elevated BNP.
Illustrative, hand-curated scenario only. No patient data or live inference is shown in this proof trace.
Explore ShifaMindA performance lead built for clinical trust.
ShifaMind pairs the top Macro-F1 in this comparison with concept-mediated evidence that can be inspected alongside every prediction.
Six models. One clear leader.
View all six models+
Clinical AI that earns the trust to be deployed.
Clinical-grade reasoning
Models trained on real clinical data and structured against the concepts a doctor uses. Not a general-purpose LLM stretched into healthcare.
Built for integration
API-first from day one. Drop predictions, evidence, and concepts into existing workflows (EHR, coding tools, dashboards) without custom integration work.
Compliance posture
HIPAA-ready deployments, encryption everywhere, audit trails on every inference, and a strict no-training-on-customer-data default.
The product line compounds on one clinical core.
Roshan AI does not build each workflow from scratch. The same concept-grounded architecture turns clinical input into evidence-backed outputs across a growing set of products.
Clinical ingestion
Notes, signals, and workflow context enter a shared clinical data layer.
Concept grounding
Clinical encoders map relevant information to interpretable concepts.
Evidence-backed output
Products return predictions with the concepts and source evidence intact.
Concept-grounded ICD-10 coding
Interpretable ICU knowledge graphs
More clinical reasoning workflows on the same backbone
Build with Roshan AI.
Pilot ShifaMind, integrate the API, or talk to us about your clinical workflow.