Why AI Software Needs Different Clinical Evaluation

Written by HATEM RABEH, MD, MSc Ing

Your Clinical Evaluation Expert And Partner

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AI-based medical device software operates fundamentally differently from traditional devices. It learns from data. It makes predictions. It can drift over time. These characteristics demand a different approach to clinical evaluation.

Under MDR, the clinical evaluation must demonstrate that your device meets the General Safety and Performance Requirements. For AI software, this means proving something more complex than device safety. You must demonstrate that the algorithm performs as claimed, across the populations you intend to serve, in the settings where it will be used.

The Three-Pillar Framework

MDCG 2020-1 establishes the evidence framework for medical device software. It requires you to demonstrate three distinct pillars:

Valid Clinical Association. The software’s output must be clinically meaningful for the intended condition. If your AI detects diabetic retinopathy, you must show that what it detects actually correlates with the clinical condition. This is not about accuracy. It is about clinical relevance.

Analytical Performance. The software must perform its technical function correctly. Processing inputs, producing outputs, meeting specifications for accuracy and repeatability. This is where traditional verification and validation live.

Clinical Performance. The software must work in real clinical settings with real users. Laboratory performance does not equal clinical performance. An algorithm that achieves 95% accuracy on curated test data may perform very differently when deployed in a busy clinic with varied image quality.

Why Traditional Approaches Fail

Traditional clinical evaluation focuses on demonstrating that a device performs its intended function safely. For most devices, this involves clinical studies, literature review, and post-market surveillance.

AI software adds layers of complexity. First, performance depends heavily on the data used for training and testing. If your training data does not represent your intended population, your performance claims may not hold. Second, AI can exhibit unexpected behavior on edge cases that were not represented in development. Third, performance can degrade over time as clinical practice or patient populations shift.

Manufacturers who apply traditional clinical evaluation to AI software typically fail in predictable ways. They demonstrate performance on internal data but lack external validation. They report aggregate metrics without subgroup analysis. They ignore the specific evidence requirements for software.

What This Series Will Cover

This series walks through the complete process of clinical evaluation for AI medical device software under MDR. We will cover the Clinical Evaluation Plan, acceptance criteria, evidence appraisal, the CER structure, and ongoing monitoring requirements.

By the end, you will understand exactly what evidence reviewers expect and how to generate it systematically.

In the next post, we start with the foundation: building your Clinical Evaluation Plan for AI software.

Peace,
Hatem
Your Clinical Evaluation Partner