Artificial intelligence in rare disease diagnosis and treatment.

Journal: Clinical and translational science
Published Date:

Abstract

Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient analysis of vast datasets, identifying patterns, and generating key insights. Predictions can then be made for medical diagnosis and personalized treatment recommendations. The use of AI can bypass some conventional limitations associated with rare diseases. Namely, it can optimize traditional randomized control trials, and may eventually reduce costs for drug research and development. Recent advancements have enabled researchers to train models based on large datasets and then fine-tune these models on smaller datasets typically associated with rare diseases. In this mini-review, we discuss recent advancements in AI and how AI can be applied to streamline rare disease diagnosis and optimize treatment.

Authors

  • Magda Wojtara
    Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA.
  • Emaan Rana
    Department of Science, University of Western Ontario, London, Ontario, Canada.
  • Taibia Rahman
    Department of Medicine, David Tvildiani Medical University, Tbilisi, Georgia.
  • Palak Khanna
    Department of Medicine, Ivane Javakhishvili Tbilisi State University, Tbilisi, Georgia.
  • Heshwin Singh
    Department of Biology, Stony Brook University, Stony Brook, New York, USA.