Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions

2026-07-23

AI Summary

Medical images are frequently compromised by artifacts, missing regions, or pathological alterations, which can undermine diagnostic reliability. This work presents a systematic review of diffusion model–based approaches for medical image inpainting and introduces the first taxonomy specifically tailored to this domain. The proposed framework encompasses prevailing architectures—such as Denoising Diffusion Probabilistic Models (DDPM) and Latent Diffusion Models (LDM)—alongside key clinical applications (e.g., MRI and CT), benchmark datasets, and evaluation protocols. Empirical analysis demonstrates that diffusion models excel at generating anatomically plausible reconstructions, yet critical challenges persist, notably the absence of standardized benchmarks and limited data diversity. By synthesizing current advances and identifying open problems, this study offers a structured foundation to guide future research in medical image restoration.

Abstract

Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. In medical imaging, this task is particularly important because artifacts, missing information, and pathological alterations can compromise diagnostic reliability and downstream clinical applications. Recently, diffusion models have emerged as state-of-the-art generative approaches for medical image inpainting due to their ability to generate anatomically consistent reconstructions. This survey presents a systematic review of diffusion-based methods for medical image inpainting, covering the main architectures, applications, datasets, and evaluation strategies reported across 60 studies. In addition, we propose a taxonomy for diffusion-based approaches. The analysis reveals a rapid growth of research interest in diffusion-based medical image inpainting, with denoising diffusion probabilistic models and latent diffusion models emerging as the dominant architectures. The reviewed studies mainly focus on artifact removal, data augmentation, pseudo-healthy tissue reconstruction, and anomaly detection, particularly in magnetic resonance imaging and computed tomography imaging. Overall, diffusion models demonstrate strong performance in producing anatomically plausible reconstructions and aiding downstream clinical tasks. However, the review also highlights important challenges, including the lack of standardized benchmarks, limited dataset diversity, and restricted validation procedures across diverse clinical applications and imaging scenarios.

Problem

• medical image inpainting
• artifact removal
• anatomical consistency
• diagnostic reliability
• missing information

Innovation

• diffusion models
• medical image inpainting
• taxonomy
• denoising diffusion probabilistic models
• latent diffusion models