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TED-style Talks - Ellis Monk, Manolis Kellis and Byron Wallace

14 Mar 2024
Venue: MTCC
Meeting Room: Exhibit Hall FG
Clinical Genetics and Therapeutics , SELI, Public Health & Policy , Health Services & Implementation
  • Accredited:
    • Accredited
Dr. Monk has been researching skin tone stratification and colorism for over a decade and will be addressing skin tone equity in technology.  He has developed a ten point scale (the MST scale) that is revolutionizing the way artificial intelligence/machine learning and medical technology serves people of all skin tones.  His collaboration with Google serves to make technology more diverse and inclusive. 
 
Dr. Kellis will be discussing how artificial intelligence may help us to address challenges in healthcare and genomic medicine.  He presents a vision for transforming medicine and reversing human disease, by understanding the circuitry of the human genome, revealing the causal hallmarks of different disorders, and therapeutically targeting their unique combinations in each patient.

Dr. Wallace will present work on using large language models (LLMs) to summarize and synthesize medical evidence. This is critical because most clinical evidence—from notes in electronic health records to published reports of clinical trials—is stored as unstructured text and so not readily accessible. The body of such unstructured evidence is vast and continues to grow at breakneck pace, overwhelming healthcare providers and ultimately limiting the extent to which it can be used to inform patient care. LLMs offer a potential means of helping domain experts make better use of such data, and ultimately to improve patient care. But key challenges remain—not least of which is ensuring that LLM outputs are factually accurate and faithful to source material.
 
 

Learning Objectives

  1. Demonstrate the importance of measuring skin tone to improve health care via medical technology (eg, AI/ML, pulse ox, etc)
  2. Restate how we can use genomic, epigenomics, single-cell, and machine learning integration to understand the circuitry underlying complex traits and human disease
  3. Estimate the potential uses, opportunities, and risks of large language models (LLMs) for summarizing medical evidence

Agenda

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