AI/ML-Enabled Medical Device
Medical device that uses artificial intelligence or machine learning to perform its intended use.
Definition
An AI/ML-enabled device incorporates models that learn patterns from data to provide diagnostic, predictive, monitoring, or therapeutic functions. FDA maintains a public list of AI/ML-enabled devices that have been authorized.What this means in practice
Most authorized AI devices today are 'locked' - the model weights do not change post-deployment. Adaptive systems require a Predetermined Change Control Plan (PCCP) to enable continued learning while maintaining oversight.Examples
- An AI/ML-enabled diagnostic device uses a locked algorithm trained on a large dataset of medical images to identify anomalies, requiring no post-market model updates.
- An adaptive AI/ML therapeutic device for personalized drug dosing continuously learns from a patient's physiological data to adjust treatment, operating under an FDA-approved Predetermined Change Control Plan (PCCP).
- A predictive AI/ML monitoring device analyzes continuous patient data streams to forecast potential adverse events, necessitating regular performance monitoring and validation to ensure accuracy.
- •Failing to establish a robust data management plan for training and validation data can lead to biased or ineffective AI/ML models.
- •Assuming that an AI/ML model’s performance in a controlled development environment will perfectly translate to real-world clinical settings is a common misconception.
- •Neglecting to implement a post-market surveillance strategy for AI/ML performance can result in undetected degradation or errors over time.
- •Underestimating the cybersecurity risks associated with AI/ML models, including data poisoning or adversarial attacks, can compromise device safety and effectiveness.
- •Not adequately documenting the AI/ML model’s design, development, and validation processes can lead to difficulties in demonstrating regulatory compliance.
Frequently asked questions
Cross-references
Related terms
Grouped by themeEditor's picks
· Hand-selected related conceptsFDA mechanism to pre-authorize specific modifications to AI/ML-enabled devices.
Guiding principles for the development of AI/ML-enabled medical devices.
Software intended for medical purposes that performs without being part of a hardware device.
FDA framework integrating premarket and post-market oversight across a device's life.
AI/ML Devices Deep Dive
· From this learning pathMethods (SHAP, saliency maps, prototype explanations) that help users understand why a model made a given prediction.
Large pretrained model adaptable to many downstream clinical tasks via fine-tuning or prompting.
Devices that use LLMs or diffusion models to generate text, images, or recommendations within a clinical workflow.
Structured documentation of a machine learning model's intended use, performance, and limitations.
Where this term appears across MedTech Terms.
- AI/ML Devices Deep DiveLesson 1 of 9
Primary references
3 sources- 1FDA AI/ML-Enabled Device ListVerifiedFDAfda.gov
- 2MDCG Software GuidanceVerifiedMDCGhealth.ec.europa.eu
- 3FDA - Software as a Medical Device (SaMD)VerifiedFDAfda.gov
Inline markers like [1] jump to the matching reference above.