Background: Deep learning (DL) based image enhancement is widely used to improve suboptimal medical imaging. However, whether such techniques can extract additional diagnostically relevant information from already high quality, clinically acceptable MRI, and thereby improve downstream artificial intelligence (AI) model performance and data-efficiency, remains unclear. We investigated the impact of DL-based enhancement on AI-based Alzheimer’s disease (AD) classification from structural MRI. Methods: In this retrospective study, we included 2,293 MRI scans from the public Alzheimer’s Disease Neuroimaging Initiative dataset (training/validation/test 7:1:2), upon which multiple AI classification models were trained, first on the original images (standard-of-care [SOC]), then on the same images enhanced by SubtleHD (SHD), an FDA-cleared enhancement tool. Convolutional neural networks (ResNet34 and DenseNet121) were used to classify subjects as cognitively normal (CN), mild cognitive impairment (MCI), or AD. Model performance was assessed using accuracy and macro–area under the receiver operating characteristic curve (macro-AUC). Data efficiency was assessed by training on progressively smaller subsets of the full dataset. Findings: Models trained and evaluated on enhanced images consistently outperformed those using the original images. For ResNet34, enhancement improved classification accuracy from 85·2% to 88·7% and macro-AUC from 0·951 to 0·968 (both p<0·01). Similar gains were observed with DenseNet121. Notably, models trained on 75% of SHD-enhanced images achieved comparable performance to models trained on the full SOC dataset (accuracy 86·5%, macro-AUC 0·954), indicating improved data efficiency.Interpretations: DL-based enhancement of MRI image quality, even when images are already considered to be of diagnostic quality, improves downstream AI classification performance and reduces the amount of training data required. This has profound implications for radiologic imaging, suggesting that conventional definitions of image quality may underestimate the information content available for machine learning. DL-based enhancement strategies could also enable more efficient development and broader deployment of AI models in neuroimaging, particularly in data-limited settings.