From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.

Journal: The Journal of clinical endocrinology and metabolism
PMID: 38679750

Abstract

CONTEXT: Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice.

Authors

  • Vivek R Sant
    Division of Endocrine Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA.
  • Ashwath Radhachandran
    Dascena, Inc., Houston, TX, United States.
  • Vedrana Ivezic
    Biomedical Artificial Intelligence Research Lab, UCLA Department of Bioengineering, Los Angeles, CA 90024, USA.
  • Denise T Lee
    Department of Surgery, Icahn School of Medicine at Mount Sinai Hospital, New York, NY 10029, USA.
  • Masha J Livhits
    Section of Endocrine Surgery, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA.
  • James X Wu
    Section of Endocrine Surgery, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA.
  • Rinat Masamed
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Corey W Arnold
    Department of Bioengineering; University of California, Los Angeles, CA.
  • Michael W Yeh
    Section of Endocrine Surgery, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA.
  • William Speier
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.

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