Building clinical AI doctors can defend.
Roshan AI LLC is a clinical-AI company. We build models trained against real clinical data and structured around the concepts a doctor uses, so every output is auditable, every prediction is explainable, and every deployment is something the clinician can stand behind in court if it came to that.
General-purpose LLMs aren't clinical-grade.
Frontier LLMs pass medical board exams. They also fabricate citations, recommend contraindicated drugs, and produce reasoning that no clinician can verify. In an outpatient demo that's a curiosity. In a coding workflow, a diagnostic workflow, or a decision-support workflow, it's a liability.
The industry's response so far has been to wrap general models in longer prompts and call it healthcare AI. The architecture stays opaque. The errors stay hidden until they reach a patient.
Build clinical models from the ground up.
We train clinical encoders on clinical data, structure them against an explicit concept layer, and route every prediction through that layer. The bottleneck is the feature, not the bug. It forces the model to ground each output in the same concepts a doctor or coder would name out loud.
The result is an architecture that can't produce a prediction without also producing its evidence. Not a post-hoc rationalization. Not a chain-of-thought that may or may not reflect the actual computation. The concepts are the computation.
Our first product, built on the platform.
ShifaMind is the first thing we shipped. It reads a clinical note and returns ranked ICD-10 codes, each one paired with the concept evidence behind it. Today it's used by clinicians and researchers evaluating coding workflows; tomorrow it's an API any health system can integrate.
ShifaMind is not the company. It's a consumer of the Roshan AI platform. The same infrastructure stack will power the next product, and the one after that. See ShifaMind →
One platform. A family of clinical products.
Coding is one piece. Clinical reasoning runs deeper: risk stratification, decision support, longitudinal patient summaries, documentation. Every one of those workflows benefits from the same architectural commitment: evidence-first, concept-grounded, defensible by construction.
We're building toward a future where a clinician's AI tools share a common reasoning fabric, where the same concept activates a diagnosis code, surfaces a relevant guideline, and flags a contraindication, all with the same audit trail. That's the platform.
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