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SOLUTION: AI RISK PROFILER

Understand the risk behind every AI health technology

ORCHA's AI Risk Profiler tells you which AI risks a health technology has, how serious they are given the clinical context, and exactly where the gaps are. Because in health, "we didn't know" isn't good enough.

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For AI developers
For AI deployers
Feature 2 _ light report (1)

Informed by an international advisory group with representatives from...

APA
MHCC
NHS
APTA1
UNICEF
Allspire
Mind
Ulster
UCL
IBM
DNV
APA Labs

How the Health AI Risk Profiler works

1

The AI developer completes questionnaire

A structured set of questions covering the product's AI features - what they do, how they work, what clinical context they operate in, and what governance and architecture controls are already in place.
2

The tool applies the six-step framework

The tool applies the AI Risk Profile methodology to every AI feature in the product. Each feature is assessed separately - clinical risk severity, computational function, inference technique, which of the 10 standard risks are activated, and what's actually mitigating them.
3

A structured AI risk profile report is generated

A clear, evidence-based output showing which risks are active in the product, how severe each is given the clinical context, where mitigations are working, and where the gaps are.
WHO IT'S FOR

Developed for both creators and deployers of health AI

Whether you're building AI into a health product or deciding which AI products to deploy, the AI Risk Profiler gives you a structured, evidence-based foundation for the decisions you're already being asked to make.
1

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For AI Suppliers & Developers

Understand your AI risk profile before your buyers ask for it

Learn more
2

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For Health Systems & Commissioners

Consistent AI risk intelligence across your entire tech portfolio

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THE METHODOLOGY

You get a complete picture of every AI risk in a product and exactly what to do about it.

Each AI feature is assessed separately. By the end, you know which risks are active, how severe they are, what's mitigating them, and where the gaps are.

Clinical Context — Clinical Risk Severity

The risk profile is anchored in what's actually at stake — who receives the AI output and what happens if it's wrong. A wellness chatbot and a clinical decision tool get very different profiles. That distinction matters, and it's built in from the start.

Computational Function

A product with multiple AI features gets each one assessed separately. You don't get a single blended risk score that obscures where the real problems are - you get a clear picture of each feature individually.

Inference Technique and Model Architecture

How the AI works determines which risks it carries. Suppliers declare this through a structured questionnaire. If the declaration is incomplete or withheld, that's flagged — because opacity in documentation is a risk in itself.

Risk Activation

You find out which of 10 defined AI risks are active in this product — and how serious each one is given the clinical stakes. Where two factors both trigger the same risk, you're told it's amplified. No guessing about what matters most.

Structural and Governance Mitigations

You see what's actually reducing the risk — in the product architecture, and in the governance programme. Assessed separately, so you can tell whether a product is genuinely safe or just well-documented.

Gap Analysis — Residual Risk

You leave knowing exactly where the unmitigated risks are and what they mean clinically. Not a pass or fail — a clear evidence base for making the decision in front of you, whether that's procurement, deployment, or regulatory submission.

Designed to support compliance across global AI frameworks - not replace them

Regulatory & Standards Alignment Designed to support compliance across these frameworks — not replace them
EU AI ACT Art. 9-17
EU AI Act
Articles 9–17 risk management & transparency obligations
European Union
DCB 0129 NHS England
DCB0129
Clinical risk management for health IT systems
United Kingdom
NICE ESF Evidence Stds
NICE ESF
Evidence Standards Framework for digital health technologies
United Kingdom
GMLP FDA / MHRA
GMLP
Good Machine Learning Practice for medical AI
US · UK · Canada
FDA SaMD Guidance
FDA SaMD
AI/ML Software as a Medical Device guidance
United States
ISO 14971 Risk Mgmt
ISO 14971
Medical device risk management standard
International
NHS AI Strategy England
NHS AI Strategy
NHS England AI adoption & governance framework
United Kingdom

Helping organisations...

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Understand AI risk with proportionate results

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Apply AI assurance more consistently and at scale

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Enable AI adoption and innovation with confidence

THE EVIDENCE BASE

Built on a foundation with AI and digital health experts

The AI Risk Profiler framework has been validated against 20+ AI feature assessments across real health products. Every risk category and mitigation type reflects what's actually in the market - which means when it finds a gap in your product, that finding is grounded in evidence, not theory.

Read more about the research group here.  

30+

Expert institutions in the Advisory Steering Group across clinical, academic and technical backgrounds

20+

Real AI health product features tested during framework development

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RESEARCH FOUNDATION

"Operationalising Trust in Health AI: A Participatory Framework for Contextual Risk Assessment in Digital Health"

A working paper co-authored with Ulster University and the American Psychological Association has been submitted to HHAI2026 - the Hybrid Human-Artificial Intelligence Conference in Brussels, July 2026. A peer-reviewed journal paper is planned for H2 2026.

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Why this matters now

35%

of people globally now use AI to manage their health, making AI health products a mainstream clinical touchpoint, not a fringe use case.

64%

of AI-fluent consumers believe they can perform at least one medical task as well as a trained professional. Perceived AI competence is outpacing governance.

-10

year-on-year drop in public confidence in finding reliable health information. Trust in the health information environment is deteriorating fast.

Source: 2026 Edelman Trust Barometer Special Report: Trust and Health. 16,009 respondents across 16 markets.

Without a consistent framework, AI risk in health stays invisible... until something goes wrong.

Regulators, health procurement teams, and clinical safety officers around the world are all asking for structured AI risk evidence in digital health technologies. Without a standard way to produce it, suppliers are guessing, health systems are accepting risk they can't see, and regulators have no consistent basis to work from.

The wrong framework can give false confidence.

A governance framework that treats a wellbeing chatbot the same as an AI influencing clinical decisions gives everyone a false sense of security. The AI Risk Profiler is built around clinical stakes from the start - so the risk profile reflects what actually matters for the patient and clinician involved.

Deals stall. Procurement slows. Patients wait.

When buyers and suppliers don't share a language for AI risk, procurement conversations go in circles. Suppliers produce documentation that doesn't answer the questions being asked. Health systems make deployment decisions on inadequate evidence. The AI Risk Profiler ends that stalemate.

One assessor, one answer. Every time.

If AI risk assessment produces different conclusions depending on who does it, it's not assessment - it's opinion. The AI Risk Profiler uses the same methodology, taxonomy, and risk activation logic on every product. Suppliers get predictable, actionable findings. Health systems get results they can actually compare.

See the AI Risk Profiler in action

Whether you're a supplier, a health system, a regulator, or a researcher - request a demo now and our team will follow up directly.