AI-Enabled Medical Device Software Development Services
Break free from the MedTech “Innovation Trap” without compromising safety-critical rigor.
Orthogonal bridges the gap between rapid AI advancement and strict compliance. We embed advanced AI engineering directly into rigorous design controls, helping you transform intelligent prototypes into cleared, revenue-generating medical devices.
Trusted by venture-backed startups to large medical device companies
Turn Compliance Into Your Speed-to-Market Advantage
When data scientists don’t understand design controls, and traditional QA teams don’t understand machine learning, projects stall. We bring these disciplines together under a single, unified engineering model.
“Applying modern engineering techniques to the development of SaMD is no easy feat. Orthogonal’s approach to blending these domains helped Tandem accelerate our product innovation cycle and outputs”
From AI Prototype to Regulated Product
Escaping the MedTech “Innovation Trap”
A working AI prototype is only a sandbox victory. Moving to a cleared product often stalls over “black box” regulatory friction.
Orthogonal bridges this gap, transforming AI models into compliant, market-ready medical software.
Conventional or AI-Enabled Development: Which You Need
Not every medical device product needs an AI program. If your software behaves the same way every time it runs, conventional development under design controls is the right fit, and our medical device software development services cover it end to end.
If your product learns from data, or you are adding generative or agentic features, the extra work described on this page applies: defining intended use and data boundaries before the capability ships, and planning for how the model changes after launch.
In-Product AI
Deploy Agentic AI and Generative Features with Clear Software Guardrails
An algorithm should not be treated as an isolated feature or an unpredictable black box. We design the entire supporting ecosystem, including user experience, data pipelines, cloud or edge architecture, and risk boundaries, to keep your product safe and compliant.
AI/ML-Enabled SaMD & Connected Devices
We help clients build AI-enabled medical device software for intelligent monitoring, predictive analytics, clinical decision support, signal processing, personalization, and data-driven patient or clinician workflows.
Generative AI & Agentic Workflows
We help MedTech teams explore and develop generative AI, LLM-powered, and agentic experiences within medical device software. We define intended use, user workflows, data boundaries, safety constraints, and product requirements before the capability reaches users.
AI Data, Infrastructure & Lifecycle Foundations
We help establish the infrastructure that guarantees your models perform reliably under real-world conditions, giving you total visibility into data quality and system health.
The AI Factory: Your Competitive Advantage
Shift from One-Off Experiments to a Regulatory-First Engineering Model
Have an AI capability you need to productize?
AI Under Design Controls
Ensure Traceability and Mitigate “Black Box” Risk
Innovation shouldn’t bypass your Quality Management System (QMS), it should be accelerated by it. Orthogonal embeds design controls directly into the AI engineering workflow from day one. We proactively evaluate and mitigate the unique risks introduced by AI systems, including model drift, demographic bias, cybersecurity vulnerabilities, and human factor friction.
Build for Product Evolution
Design for Post-Market Change, Not Just Initial Launch
AI models require tuning as real-world data flows in. We architect your data pipelines, testing loops, and Predetermined Change Control Plans (PCCP) so you can update and optimize algorithms post-launch without starting regulatory submissions from scratch.
Our AI-Enabled Development Process
Our Agile process for medical device software, adapted for products where an AI or ML model is part of the system. Each phase produces working software and the evidence that supports it.
Phase 0: Inception: Workshops that lay the technical, design and quality foundations of the work and get the team working together. For AI-enabled products, this is where we establish what the model needs to do, what data exists to support it, and where the regulatory boundaries sit.
Phase 1: Intended Use and Design Inputs: We define intended use, user workflows, data boundaries, safety constraints and product requirements before the capability reaches users. Requirements, risk controls and acceptance criteria are captured as linked design inputs rather than standalone documents.
Phase 2: Data and Infrastructure Foundations: We establish the data pipelines, cloud or edge architecture and version control the product will run on, built to prevent data drift, bias and security vulnerabilities, and instrumented to give visibility into data quality and system health.
Phase 3: Development Under Design Controls: Development runs in Agile cycles with design controls embedded in the engineering workflow from day one. Model behavior is wrapped in deterministic software guardrails, with strict data boundaries and runtime constraints, so outputs conform to the device’s defined intended use.
Phase 4: Verification, Traceability and Evidence: Formal verification runs iteratively alongside development. Test scenarios, regression coverage and traceability across requirements, risks, tests and documentation are maintained as the work proceeds rather than reassembled at the end.
Phase 5: Design Transfer and Post-Market Evolution: We support user acceptance testing and the transfer of source code, build files and quality documentation. For ongoing product evolution, we help establish the data pipelines, testing loops and change-control approach needed to support future model updates, including PCCPs where appropriate.
Build & Scale AI Medical Software with Confidence
Partner with us to bring generative AI, machine learning, and automation to market without compromising MedTech rigor.
Frequently Asked Questions
1. How do we manage “black box” risk and explainability with Generative AI in a regulated medical device?
By wrapping the model in deterministic software guardrails. We enforce strict data boundaries, real-time validation agents, and runtime constraints so dynamic AI outputs always conform to the device’s defined intended use and FDA safety standards.
2. Can we genuinely apply Agile methodologies to an FDA-regulated environment for AI development?
Yes, by decoupling rapid development cycles from formal release gates. Our automated toolchains continuously generate design controls, traceability, and test documentation as code is written—enabling true Agile velocity with continuous, auditable compliance.
3. How does the “AI Factory” pattern align with FDA guidance on Predetermined Change Control Plans (PCCP)?
The AI Factory provides the operational infrastructure a PCCP requires. It delivers the automated data pipelines, regression testing, version control, and telemetry needed to safely execute pre-approved post-market model updates without triggering new 510(k) submissions.
4. What is an AI-enabled medical device?
A medical device or SaMD product where part of the behavior comes from a model rather than from fixed, deterministic logic. In practice that covers AI/ML features such as intelligent monitoring, predictive analytics, clinical decision support, signal processing and personalization, as well as generative and agentic features built into the product experience. The distinction that matters for development is that the model’s behavior depends on data, so intended use, data boundaries and post-launch change all have to be planned for alongside the software itself.
5. How do FDA design controls apply to AI and machine learning features?
Orthogonal applies the same design-control discipline used for medical device software while accounting for the additional inputs and risks introduced by AI. Design controls are embedded into the engineering workflow from the start, with requirements, risk controls, tests and evidence maintained as linked artifacts. AI-specific risks such as model drift, demographic bias, cybersecurity vulnerabilities and human-factor friction are considered as part of that process.
6. How should generative or agentic AI features be validated in a regulated product?
Validation starts before the feature is built. We define intended use, user workflows, data boundaries, safety constraints and product requirements first, so there is a defined standard for the feature to be tested against. Verification then runs iteratively alongside development, with test scenarios, regression coverage and traceability across requirements, risks, tests and documentation maintained as the work proceeds rather than assembled at the end.
7. Can AI capabilities be added to an existing SaMD or connected medical device?
Potentially. The approach depends on the existing product architecture, intended use, data environment and change-control needs. Orthogonal treats the AI capability as part of the broader product system, including the user experience, data pipelines, cloud or edge architecture and risk boundaries, and brings the new capability into the product’s existing design-control and verification framework.
AI-Accelerated Compliance: Operational Transformation
We Use AI to Speed Up Your Regulated Development Lifecycle by 2-3x
We do not just build AI into medical devices. Orthogonal uses specialized multi-agent AI workflows and deep automation to remove manual friction from regulated software delivery.
Resources for MedTech AI Teams
Practical guidance from our team on building, validating and shipping AI-enabled medical device software.
- Agentic AI Playbook: How MedTech Teams Must Evolve
A role-by-role framework for engineering, product, SDET, quality and leadership teams moving from manual artifact generation to AI orchestration. - Automating Compliance with AI in the MedTech SDLC
How to make compliance, testing and traceability continuous outputs of development instead of a separate phase before submission. - Agentic AI for MedTech: From Requirements to Tested Software
A recorded webinar on using behavior-driven development and AI agents to connect product intent to working, tested software. - Agentic AI Insights
Articles, webinars and playbooks on applying agentic AI inside regulated MedTech development.
Case Studies
Case Study
EBT: External Nerve Stimulation for Overactive Bladder
Case Study
Neurostimulation Device for Dry Eye Disease
Case Study
Continuous ECG Monitoring with Nanosensors
Case Study
PhysIQ: Ambulatory Patient Monitoring
Build and Evolve AI-Enabled Medical Device Software with Confidence
If you are building AI-enabled SaMD, adding generative or agentic AI to a connected medical device platform, or looking to accelerate regulated software development with AI and automation, drop us a message.
