Recent progress in artificial intelligence enabled NMR spectroscopy: Methodologies, implementations, quality assessments, and prospects

Nuclear magnetic resonance (NMR) spectroscopy is widely used across chemistry, applied physics, life sciences, and related disciplines. As NMR studies grow in complexity, artificial intelligence (AI) has emerged as a transformative tool to improve NMR data acquisition, processing, and analysis, fundamentally reshaping conventional NMR workflows. This review provides a comprehensive overview of recent advances in AI-enabled NMR reconstruction, tracing its methodological evolution from early artificial neural networks and evolutionary algorithms to contemporary deep learning (DL) frameworks. Main applications are examined in detail, including sparse reconstruction, noise filtering and artifact suppression, Laplace NMR inversion, pure shift NMR, chemical exchange saturation transfer NMR, RF pulse generation and pulse sequence design, and nanoscale NMR, among others. For each of these applications, AI methodologies, design choices, key innovations, and publicly available data repositories are highlighted. Moreover, we also summarize and compare the technical implementations and quality assessment behind these applications. Finally, we discuss current challenges, including trade-off between signal preservation and artifact suppression, limited model generalizability to unseen data, the absence of online and uniform quality assessment metrics, and the scarcity of high-quality experimental datasets, and outline future directions encompassing advanced network architectures and training strategies, the development of foundation models for NMR reconstruction, uncertainty-aware modeling and quality assessment benchmarking platforms, and the establishment of open-source datasets. Collectively, the integration of AI addresses long-standing limitations in NMR spectroscopy and improves the quality of NMR spectra, enabling automated analysis of experimental data and enhancing subsequent spectral interpretation, thus providing the stronger support for scientific research and practical applications.