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Add-On Workshops

The 2024 AI in Health Conference will have three in-person workshops happening on September 9 and 12 for an additional $25 that can be purchased alone or in combination with conference tickets.

Workshop 1: Unpacking Digital Twins in Oncology – Challenges and Perspectives

Monday, September 9, 2024

1:00 pm – 5:00 pm 

Digital twins have undergone a widespread development in multiple areas of engineering and medicine. In recent years, increasing success of computational models to predict the development of cancer and its response to treatments have demanding increasing attention to the translation of state-of-the-art digital twin techniques into clinical cancer healthcare. The overall goal of this workshop is to engage discussion about current challenges on the development and implementation of clinically practical digital twins in oncology, as well as to appreciate comprehensive perspectives from multiple stakeholders in cancer healthcare.

Chengyue Wu, PhD — Department of Imaging Physics, UT MD Anderson Cancer Center
Bissan Al-Lazikani, PhD, MBCS FRSB—Department of Genomic Medicine, UT MD Anderson Cancer Center
Heiko Enderling, PhD, FSMB — Department of Radiation Oncology, UT MD Anderson Cancer Center

Workshop 2: NVIDIA Omniverse, Digital Twins, and Intro to LLMs

Thursday, September 12, 2024

9:00 am – 11:00 am 

1) NVIDIA Omniverse and Digital Twins – Building a Smart Hospital
Speaker:  Robert Rios — Developer, Mark III Systems; Principal, Mark III Innovation

In this workshop, Mark III and NVIDIA will walk through the detailed step-by-step process of building a digital twin of rooms and sections of a smart hospital using NVIDIA’s Omniverse platform.  In addition to 3D modeling with Omniverse and Omniverse-compatible apps like Blender, Maya, and USD Composer, this session will also touch on how to build connectors in Omniverse to pipe in data and telemetry, in addition to how to think about constructing teams to enable your institution to build digital twins, whether it be for smart hospitals or for research, clinical, or operational purposes.

2) Intro to Large Language Models: LLM Tutorial and Disease Diagnosis LLM Lab
Speaker: Michaela Buchanan — Data Scientist, Mark III Systems

In this workshop we start by discussing what a large language model (LLM) is and some of the strengths and weaknesses of these models, looking at a handful of models and approaches. We cover the difference between pretraining and finetuning. Input processing is discussed by showing the steps of taking an input string and tokenizing it into input ids. QLoRa is presented as a means of greatly reducing computational requirements for LLM inference and finetuning. The concepts portion of the session concludes by discussing Hugging Face and their transformers library. The workshop starts with performing inference using the Hugging Face transformers library and the Falcon-7B-Instruct model. We then move to finetuning Falcon-7B-Instruct using the MedText dataset, where the goal is to take a prompt which describes symptoms of a medical issue and generate a diagnosis of the problem as well as steps to take to treat it.

Workshop 3: Patient Engagement and Equity in Health AI

Thursday, September 12, 2024

9:00 am – 11:30 am  



Rodrigo Ferreira, PhD (Moderator) — Assistant Teaching Professor of Computer Science, Rice University

Grace Wickerson, MS — Health Equity Policy Manager, Federation of American Scientists

Kirsten Ostherr, PhD — Gladys Louise Fox Professor of English, Director of Medical Humanities Research Institute, Rice University

Fred Oswald, PhD — Professor of Psychological Sciences, Herbert S. Autrey Chair in Social Sciences, Rice University

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