• A review of deep learning in healthcare, along with challenges and opportunities
  • Milad Vazan,1,*
    1. Department of Computer Science, Faculty of Mathematics, Statistics, and Computer Science, University of Tabriz, Tabriz, Iran


  • Introduction: The impact of deep learning in real-world clinical environments has become increasingly evident over the last few years. Deep learning algorithms can consistently provide high-quality results when used in clinical settings. Deep learning is preparing to change healthcare in more ways than just purely clinical applications. Deep learning is able to help detect genetic diseases like Turner's syndrome, hemophilia, and sickle cell anemia through the study of genes. This leads to finding future treatments and future medications. While deep learning has the potential to transform medical care, it still faces many obstacles, including inadequate data and interpretability issues. The article discusses how deep learning can be applied to precision medicine and next-generation health care, as well as some of the challenges, opportunities, and potential applications of this method.
  • Methods: Pubmed, Google Scholar, and Scopus databases were searched for related studies in the literature.
  • Results: In the last decade, deep learning (DL) has received unprecedented attention for its applications in the diagnosis and analysis of biomedical problems. There are several challenges that remain unsolved regarding deep learning's application in health care, despite the promising results obtained using deep architectures. This type of learning is difficult to interpret and requires a large amount of data. Aside from the quantitative performance of algorithms, understanding why algorithms work is also important in health care. Providing the medical professionals with an interpretable model is crucial to convincing them of the predictive system's recommendations for action. The goal of deep learning is to learn from data. It requires large amounts of data, however, for this learning to occur. Medical datasets, on the other hand, are generally biased and limited in nature.
  • Conclusion: In the future, advancements in IoT and edge computing will bring about a new model of DL that will support these technologies. Furthermore, a deep learning model must also be able to be interpreted since the more interpretable the model, the easier it will be to comprehend its predictions. In the field of interpretable deep learning, there are a number of new approaches and techniques available. Research in this field is still needed to find new and reasonable solutions to the key challenges, however.
  • Keywords: Healthcare; Deep Learning; interpretability