Guidance: AI Airlock Phase 2 Cohort

Phase 2 of the Airlock will include seven additional technologies spanning AI-powered clinical note taking, advanced cancer diagnostics, eye disease detection tools, and obesity treatment support systems. This expansion addresses three fundamental regulatory challenges around managing evolving AI applications, regulating AI-powered diagnostics effectively, and implementing robust post-market surveillance for AI medical devices.

Candidates will test in the Airlock until March 2026 when the second phase will be finalised.

The phase 2 sandbox structure

Phase 2 of the AI Airlock is structured around 3 key regulatory challenge areas. Within each challenge there are multi-environment candidates and simulation candidates.

The multi environment candidates will work with the AI Airlock team to test the regulatory challenge in more than one of the AI Airlock’s testing environments:

Simulation environment: a focused roundtable workshop bringing together multiple stakeholder perspectives to address simulated testing scenarios

Virtual or Research environment: testing of the AI product in a controlled data environment to address specific research questions and generate evidence

Real-world environment: deployment of a product in the intended use environment, separate from the clinical pathways and decision-making processes, to generate evidence of the model’s deployment without impacting the healthcare outcomes

The Simulation environment candidates will work with the Airlock at the Simulation level only, helping to bring multiple perspectives to the challenge by varying the product type, clinical area and development stage of the case studies under investigation.

Regulatory challenge 1: Scope of intended use extension and validation

Multi-environment candidate:

TORTUS

TORTUS is an evolving clinical AI assistant designed to support healthcare professionals by reducing administrative burden and improving quality of care. Today, TORTUS provides automated clinical documentation, integrated directly within electronic health records.

A central regulatory challenge for technologies like TORTUS is defining the boundary where features move from documentation support into diagnostic or decision-support functionality. Under the UK Medical Device Regulations, such a change in intended use could result in reclassification from Class I to Class IIa. While clear thresholds exist for traditional medical devices, there is no established benchmark for AI-based clinical documentation systems, creating uncertainty for both innovators and regulators.