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Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations: Draft Guidance for Industry and Food and Drug Administration Staff

DraftCenter for Devices and Radiological Health Center for Biologics Evaluation and Research Center for Drug Evaluation and Research01/07/2025

Description

This draft guidance document provides recommendations regarding the contents of marketing submissions for devices that include artificial intelligence (AI)-enabled device software functions including documentation and information that will support FDA’s evaluation of safety and effectiveness. The recommendations reflect a comprehensive approach to the management of risk throughout the device total product life cycle (TPLC). To support the development of appropriate documentation for FDA’s assessment of the device, this draft guidance also proposes recommendations for the design, development, and implementation of AI-enabled devices that manufacturers may wish to consider using throughout the TPLC.

Scope & Applicability

Product Classes

5
AI-enabled device

Devices that include one or more AI-enabled device software functions; medical devices utilizing artificial intelligence; Devices incorporating artificial intelligence software functions.; The model is part of the mechanism of action for an AI-enabled device.; validation includes ensuring that the device will perform its intended use safely and effectively; devices particularly susceptible to unexpected differences in performance due to data reliance; Ongoing performance monitoring is important

Disease X Screening Model

Model Name: Disease X Screening Model

Prescription device

devices administered by a licensed practitioner

AI-enabled devices

The guidance provides recommendations on documentation for marketing submissions for AI-enabled devices.

Combination product

A product comprised of two or more regulated components.

Stakeholders

7
Sponsor

Entity responsible for submitting applications under section 524B

Sponsors

Assist sponsors in the nonclinical evaluation

Healthcare Professionals

Users whose characteristics impact user needs and communication format.; Intended users of the AI-enabled device; Intended User: Healthcare professionals

Patient

Way questions are framed is critical to collecting unbiased patient input

Clinician

Provides clinical judgment and conducts assessments for ClinROs.

Caregiver

It may be a hardship for patients and/or caregivers to attend in-person; Individuals who may assist patients or provide observations when patients cannot self-report.; conducted with caregivers to obtain similar feedback

Manufacturer

Entity responsible for submitting NDINs

Regulatory Context

Attributes

10
Transparency

Strategy to address transparency and bias throughout the TPLC

Confidence intervals

sponsors should include confidence intervals on all reported results

Positive Predictive Value

Performance metric achieved by the model

Explainability

Characteristic of AI models that impacts user understanding

Specificity

Ability to detect intended mechanism of action without interference; Performance characteristic to be validated

Sensitivity

Analysis by sex of clinical performance measures such as sensitivity

Statistical confidence level

Metric describing predictions and uncertainty in the model.

OUS data

If OUS data are used during validation, an explanation regarding how the data compares to the U.S. population

Representativeness

Consideration for study sampling in research protocols

Demographic distributions

Characteristics of the development data population.

Identified Hazards

Hazards

9
AI bias

Potential tendency to produce incorrect results in a systematic way.; AI bias is a potential tendency to produce incorrect results in a systematic way due to training data limitations.; Understanding the methods used to develop the model also helps FDA identify potential limitations, sources of AI bias; spurious learnings could impact performance differentially across patient demographics; Risk associated with patient populations not well represented in training

Cybersecurity risks

Inclusion of cybersecurity risks as part of informed consent form

Model Bias

manipulation of training data to introduce or accentuate biases; incorrect follow-up due to a false positive or false negative output, which can occur because of model bias

Model Evasion

Cyber attackers could modify input samples to deceive models

Model inversion

Cyber threats could intentionally use forged data to replicate models

Data Poisoning

Cyber threats could lead to data poisoning

Data leakage

Data leakage between validation and development datasets can create uncertainty regarding true performance.; exploit vulnerabilities to access sensitive training data

User error

Risks resulting from mistakes made by the operator.

Data drift

Occurs when systems that produce inputs for AI-enabled devices change over time.; consider the impact of factors (e.g., data drift) on the safety and effectiveness

Related CFR Sections (14)

Related Warning Letters (10)

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    2025-10-21
  • CGMP/QSR/Medical Devices/Adulterated

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  • Medical Device Reporting/Misbranded

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    2025-09-30
  • Medical Device/Adulterated/Misbranded/Lacks PMA and/or 510(k)

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See Also (8)