Deep learning and its applications in nuclear magnetic resonance spectroscopy

[Display omitted] •Deep learning (DL) addresses key challenges in acquiring and analyzing NMR spectra.•Comprehensive summary of DL applications in traditional NMR and in vivo MRS.•DL applications in traditional NMR include spectral reconstruction, denoising, etc.•DL applications in MRS include frequency correction, disease detection, etc.•Future directions for DL in NMR and MRS are discussed for further improvement. Nuclear Magnetic Resonance (NMR), as an advanced technology, has widespread applications in various fields like chemistry, biology, and medicine. However, issues such as long acquisition times for multidimensional spectra and low sensitivity limit the broader application of NMR. Traditional algorithms aim to address these issues but have limitations in speed and accuracy. Deep Learning (DL), a branch of Artificial Intelligence (AI) technology, has shown remarkable success in many fields including NMR. This paper presents an overview of the basics of DL and current applications of DL in NMR, highlights existing challenges, and suggests potential directions for improvement.