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Turning raw medical images into actionable diagnostic insights with a layered, signal-driven platform. Designed for clinical accuracy, scalability, and trusted decision-making.
Platform
DiagnoAI is organized as a layered infrastructure surface where medical signals move from foundational processing into domain intelligence and then human validation.
Input signals undergo immediate preprocessing, ensuring clean, standardized data before entering the domain models.
Data routes to highly specialized models trained for precise clinical modalities.
Final outputs are presented for human verification and secure, long-term clinical storage.
Platform
The workflow engine is the operational backbone: it keeps state progression deterministic, domain usage modular, and clinician oversight embedded into the full lifecycle.
Microbiology
TB Microscopy (Phase 1)
Pathology
H&E Grading (Phase 2)
Radiology
X-Ray Analysis (Phase 3)
Every diagnostic step is organized around state validation, domain-aware routing, and human confirmation before finalization.
The platform separates foundational image handling from domain intelligence so new modalities can be introduced without rewriting the core engine.
That architecture is what enables DiagnoAI to behave like an operating layer rather than a single-purpose detector.
Platform
From acquisition to archive, data moves through an accountable chain that preserves traceability, reviewability, and platform-level reliability.
End-to-end clinical data orchestration with cryptographic audit trails, strict state validation, and deterministic workflow progression.
Platform
The governance layer ensures safe operation through logging, validation, scalability controls, and operational trust mechanisms.
Every state transition is captured with accountable traceability so operational history remains inspectable.
Strict transition validation prevents illegal flow jumps and keeps the workflow engine deterministic.
Domain templates allow new use cases to scale without compromising the core architecture.
Use cases
DiagnoAI is designed for real medical image operations where detection alone is not enough and the system must support governance, review, explanation, and outcome definition.
TB microscopy detection, bacilli counting, stain quality assessment, and foundation AI pipeline support.
H&E stain grading, tumor region segmentation, whole-slide support, and advanced tissue interpretation workflows.
DICOM ingestion, X-ray analysis, scalable imaging review, and future multi-modal clinical expansion.
Problem
Raw images often arrive with uneven quality and inconsistent operational readiness.
Solution
Foundational preprocessing, quality checks, artifact detection, and standardized intake before domain routing.
Problem
Clinical AI tools frequently stop at isolated prediction rather than workflow integration.
Solution
DiagnoAI connects inference to governance, review, explanation, and archive-oriented operational flow.
Problem
Scaling across specialties usually requires rebuilding the product stack.
Solution
Domain templates make expansion possible without rewriting the core engine or workflow model.
Problem
Trust breaks when systems cannot explain, validate, or audit what happened.
Solution
Validation protocols, audit logging, state control, and structured review keep the platform inspectable.
Clearer review handoff between AI output and expert confirmation
Improved consistency in diagnostic workflow execution
Operational readiness for domain expansion without core redesign
Stronger trust posture through explainability, logging, and validation
Product
The product surface connects the branded frontend to the operational workspace. This section now shows the actual workspace shell so users can understand the review environment before entering it.
Three-pane shell
Navigation, focused review canvas, and contextual sidecar remain visible together.
Clear clinical hierarchy
The worklist surfaces urgency, stage, assignment, and AI confidence without visual clutter.
Trust signals built in
Explainability, audit trace, and archive integrity are visible rather than hidden in the backend.
Enter a clinically aware environment built around the actual review journey.
Modality-first data ingress with visible system health
Worklist-first review flow for daily operational use
Viewer and AI remain separate layers to preserve clinical judgment
Archive and analytics support traceability and validation
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Unified workspace
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Clinical panes
6
Workflow stages shown
Pricing
Pricing communicates with registration: once a user registers from the Login menu, this section becomes the next step for plan selection and onboarding.
Free tire
For early validation, product exploration, and initial workspace access.
Organization plan
For teams deploying governance, collaboration, validation workflows, and domain expansion.
Api usage model
For partners integrating DiagnoAI capabilities into larger diagnostic ecosystems.
Company
The company section collects the brand story, value and vision, trust framework, compatibility posture, contact paths, and guidelines needed for a production-facing medical software brand.
DiagnoAI was founded by Zelalem Tegene during his college years, driven by direct observation of healthcare environments and insights from clinical professionals. The platform originated as a focused prototype, a single-domain biological image prediction model, designed to validate the feasibility of AI-driven medical image analysis.
Early engagement with leading clinical laboratories and domain experts provided critical feedback, enabling iterative refinement through structured research and development. These insights shaped the evolution of DiagnoAI into a scalable, multi-domain platform aligned with real-world clinical workflows.
Today, DiagnoAI represents the outcome of sustained innovation, disciplined execution, and strategic guidance, successfully transitioning from concept to a fully realized, production-oriented platform.
DiagnoAI is built on a deep understanding of applied AI research, engineered to modernize and unify medical imaging into an intelligent operating system.
DiagnoAI aims to establish the foundational infrastructure where medical AI operates safely, reliably and continuously improves through real-world clinical integration.
Reach out for partnership conversations, product access, domain rollout, or brand inquiries around clinical platform deployment.
contact@diagnoai.com
Partnership
Clinical infrastructure and product collaboration
Zelalem Tegene
Founder & CEO
DiagnoAI
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DiagnoAI Quick Chat
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