AI Advancements in Healthcare: How Patient Referrals Can Be Done for Rare Diseases?

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AI in Healthcare and Rare Disease Treatment

This talk discusses the application of AI and natural language processing to aid in better patient care and increased treatment outcomes.

  Date and Time




  • Date: 29 Mar 2024
  • Time: 04:00 PM to 04:45 PM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
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  • Co-sponsored by Mayo Clinic
  • Starts 05 March 2024 12:00 AM
  • Ends 29 March 2024 12:00 PM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
  • No Admission Charge


Yao Xiao of IEEE Women in Engineerig


AI Advancements in Healthcare: How Patient Referrals Can Be Done for Rare Diseases?

A rare disorder is a disease or condition that affects fewer than 200,000 Americans. Cumulatively, there are more than 10,000 rare diseases affecting more than 30 million Americans. The burden of rare diseases encompasses various challenges affecting individuals, their families, healthcare systems, and society. Especially identifying individuals with rare genetic disorders presents substantial challenges, constituting a diagnostic odyssey that typically spans an average of five years before patients receive their diagnosis. In this work, we proposed an AI approach integrating natural language processing (NLP) and machine learning (ML) techniques. By analyzing the electronic health records (HER), our goal is to identify the patients who may benefit from referrals to genetic tests to shorten the diagnostic journey, potentially leading to early intervention and treatment, informing family planning, optimizing disease management, and improving patient outcomes.


Dr Yao Xiao specializes in AI-driven research to advance medical technologies. Previously at MD Anderson Cancer Center, her work

on deep learning for radiation field aperture design received FDA approval in 2023. Dr. Xiao earned her Ph.D. in Biomedical Engineering from the University of Florida in 2020, with honors and as the commencement speaker. With a multidisciplinary background, including a Master's in Computer Science and a Bachelor's in Software Engineering, she is dedicated to developing more reliable, feasible, and cost-effective solutions focusing on AI solutions to support disease diagnosis, treatment planning, and preventive healthcare initiatives.