Evaluating the Clinical Impact of Generative Inpainting on Bone Age Estimation

TL;DR

This study evaluates how generative inpainting affects the accuracy of bone age and gender prediction from pediatric hand radiographs, revealing significant performance degradation and structural alterations that question its clinical reliability.

Contribution

It provides a systematic assessment of generative inpainting's impact on medical image analysis, highlighting potential risks and the necessity for careful validation in clinical AI applications.

Findings

Inpainting increased bone age MAE from 6.26 to 30.11 months.

Gender classification AUC decreased from 0.955 to 0.704.

Inpainted images showed pixel-intensity shifts and structural inconsistencies.

Abstract

Generative foundation models can remove visual artifacts through realistic image inpainting, but their impact on medical AI performance remains uncertain. Pediatric hand radiographs often contain non-anatomical markers, and it is unclear whether inpainting these regions preserves features needed for bone age and gender prediction. To evaluate the clinical reliability of generative model-based inpainting for artifact removal, we used the RSNA Bone Age Challenge dataset, selecting 200 original radiographs and generating 600 inpainted versions with gpt-image-1 using natural language prompts to target non-anatomical artifacts. Downstream performance was assessed with deep learning ensembles for bone age estimation and gender classification, using mean absolute error (MAE) and area under the ROC curve (AUC) as metrics, and pixel intensity distributions to detect structural alterations. Inpainting markedly degraded model performance: bone age MAE increased from 6.26 to 30.11 months, and gender classification AUC decreased from 0.955 to 0.704. Inpainted images displayed pixel-intensity shifts and inconsistencies, indicating structural modifications not corrected by simple calibration. These findings show that, although visually realistic, foundation model-based inpainting can obscure subtle but clinically relevant features and introduce latent bias even when edits are confined to non-diagnostic regions, underscoring the need for rigorous, task-specific validation before integrating such generative tools into clinical AI workflows.