Health AI Central

AI Health Lab

University of Texas at Austin · Austin, TX, United States

Lab62 peopleAI in HealthGenerative AI in Health
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Lab profile

About AI Health Lab

The AI Health Lab is dedicated to revolutionizing healthcare by harnessing the transformative power of AI and Generative AI technologies. The lab unites scholars, students, and clinical professionals from various disciplines to advance AI in healthcare and leverage AI to reduce health disparities.

AI in HealthGenerative AI in HealthKnowledge Graph for HealthPrecision OncologyBehavioral HealthExplainable AIMulti-modal AI/MLYouth Mental Health

01 · Research focus

Research areas and highlights

Topics and recent work as stated on the lab’s own site.

  • PODS-PACK: Precision Oncology Decision Support – Protein AI Companion KnowledgeA MD Anderson – UT Austin collaboration pioneering a shift from genomic to proteomic cancer-targetable treatments, using AI to build a protein-informed digital learning companion for precision oncology.
  • Robust and Interpretable Multi-modal AI/ML for Precision MedicineDeveloping robust and interpretable AI/ML models for precision medicine applications.
  • NIH Bridge2AI Cell Maps For AI (CM4AI) Data Generation Project: Teaming ModuleMaps human cell architecture to enhance genotype-phenotype learning and develops a Talent Knowledge Graph to integrate diverse expertise and ethical AI considerations.
  • NIH Aim-Ahead: Use AI/ML to Address Behavioral Health Crises of Black YouthDevelops AI methods to reduce suicide rates among Black youth by identifying social risks and building trust in AI applications.
  • NSF I-Corps: Contextualization of Explainable Artificial Intelligence (AI) for Better HealthDevelops explainable AI methods for healthcare to improve trust and compliance, enhancing personalized care and meeting regulatory requirements.
  • NIH RO1: Closing the loop with an automatic referral population and summarization systemUses AI and NLP to automate and improve referral forms between primary care physicians and specialists, reducing errors and costs.
  • NSF-CSIRO: Graph Representation Learning for Fair Teaming in Crisis ResponseDevelops fair AI models for forming inclusive scientific teams during disease outbreaks, focusing on fairness and cross-disciplinary education.
  • NSF Convergence: DREAM-KG: Develop Dynamic, REsponsive, Adaptive, and Multifaceted Knowledge Graphs to address homelessness with Explainable AICreates a knowledge graph to understand and address homelessness, leveraging AI for better resource allocation and service delivery.

02 · People

Leadership and researchers

People listed on the lab’s team pages, by role.

Ying DingDirector · Bill & Lewis Suit Professor, School of Information & Dell Medical School
Jonathan I. TamirCo-Director · Assistant Professor, Jack Kilby/Texas Instruments Endowed Faculty Fellowship in Computer Engineering
Eunhye Grace KoSchool of Information, Postdoc Researcher, Ph.D. in Learning Technology

03 · Research intelligence

Research Intelligence evidence

Publications, grants and clinical trials linked to the lab’s researchers in Health AI Central’s research data.

Research

Publications & preprints

All 14 since 2024 →
Funding

Active NIH grants

All 2 active →

04 · Updates

Updates and lab news

News and events from the lab’s own site.

05 · Official presence

Lab sources and channels

The lab’s own pages and accounts.

Website aihealth.ischool.utexas.edu ↗Team ↗Research ↗Events & programs ↗

06 · Related

Related labs and centers

Labs at the same institution and labs working on similar topics.

Center for Computational Medicine University of Texas at AustinAI in Health research community University of BristolCentre for Healthcare AI University of EdinburghCentro de Inovação em Inteligência Artificial para a Saúde Universidade Federal de Minas Gerais (UFMG)