Best Practices for Validation of NDT Techniques Incorporating AI/ML

The paper presents a summary of best practices for validation of NDT techniques incorporating artificial intelligence (AI) / machine learning (ML). While current best-practices for evaluating probability of detection (POD) can be applied to NDT procedures incorporating AI/ML today, additional guidance is presented to ensure validation studies are rigorously performed.

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Publication Details

Published
2025-09-01
DOI
https://doi.org/10.32548/rs.2025.002
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
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article

Best Practices for Validation of NDT Techniques Incorporating AI/ML

David S. Forsyth, John C. Aldrin
Medical Imaging and Analysis
article

Best Practices for Validation of NDT Techniques Incorporating AI/ML

David S. Forsyth, John C. Aldrin
article en

Abstract

The paper presents a summary of best practices for validation of NDT techniques incorporating artificial intelligence (AI) / machine learning (ML). While current best-practices for evaluating probability of detection (POD) can be applied to NDT procedures incorporating AI/ML today, additional guidance is presented to ensure validation studies are rigorously performed.

Openalex Percentile: Top 77%
Medical Imaging and Analysis
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Best Practices for Validation of NDT Techniques Incorporating AI/ML — David S. Forsyth, John C. Aldrin · (2025) | TGRS Research Map | TGRS