Evaluating the Safety of Deep Learning-Based Brain MRI Reconstruction
Objective: Deep learning accelerates brain MRI four- to tenfold, but models can erase lesions or synthesize false tissue - failures pixel-averaged metrics like PSNR and SSIM miss. We review whether current evaluation practices detect this blind spot. Methods: Following PRISMA 2020, we searched seven databases without date limits, including 263 studies (1995-2026), appraised them using QUADAS-2 and matched instruments, and synthesized narratively. Categories were derived from titles, abstracts, and controlled vocabulary; reported prevalence figures represent floors. Duplicate screening achieved high agreement (Fleiss kappa = 0.877), as did appraisal (0.788; 0.390 where observable). Extract...
| Authors | Dat Tat Mai, Thai Viet Pham, Thu Nguyen Thi Dang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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