• Laboratory and simulation testing that establishes design performance, safety margins, and failure modes before any exposure to human participants.
• For In Vitro Diagnostics, analytical performance typically includes precision/repeatability, limit of detection, linearity, and interference studies as part of the performance evaluation framework.
• Animal studies may be used when needed to demonstrate biological safety or functional performance prior to clinical use.

• For physical devices, bench testing spans mechanical, electrical, electromagnetic compatibility, environmental stress, and biological safety.
• For software in a medical device and standalone software as a medical device, “bench” work includes verification and validation, cybersecurity testing, and simulated-data validation in line with clinical evaluation requirements for software.

• Analytical performance is a distinct pillar alongside scientific validity and clinical performance; analytical studies precede or run in parallel with clinical performance studies.

• Always required to support technical documentation and risk management prior to human exposure.
• Not sufficient alone to support clinical claims; must be complemented by clinical evidence when claims rely on clinical performance.

Compared to feasibility or pivotal clinical investigations, bench/analytical studies are faster, fully controllable, and critical to de-risk design—but they cannot answer questions about patient outcomes or user interaction.

• Do analytical and bench tests map to risk controls and intended use?
• Are worst-case configurations tested?
• For software, are verification/validation and cybersecurity tests traceable to requirements?

• Summative (validation) testing with intended users, use environments, and representative tasks to confirm safe and effective use of the device.
• Focus on prevention of use-related hazards and confirmation that mitigations and labelling are adequate.

• Software interfaces require validation with intended users, including error messaging, alarms, cybersecurity-related tasks (e.g., login, updates), and critical workflows.

• Sample handling, workflow steps, and interpretation (e.g., reader displays) are part of critical tasks; training and labelling validation are expected.

• Expected for devices with meaningful human-device interaction; complements, but does not replace, clinical evidence.

Unlike pivotal trials that answer clinical outcome questions, human factors validation answers whether the user can operate the device safely and effectively under real-world conditions.

• Are intended users, environments, and critical tasks correctly identified?
• Do scenarios reflect realistic training and labelling?
• Are residual use-related risks acceptable?

• Initial clinical exposure to collect proof-of-principle and early safety data; allows design iteration under Investigational Device Exemption (IDE) oversight.
• Limited participants and sites; heightened monitoring and governance.

• Iterative updates to hardware or software can be prospectively managed via IDE supplements and change control during Early Feasibility Studies.

• First-in-human clinical use is uncommon for IVDs; most “feasibility” is analytical and small clinical pilot evaluations prior to pivotal clinical performance studies.