AI in Oncology
AI in Oncology

Artificial Intelligence in Oncology

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Program

Committee

As advances in artificial intelligence (AI) continue to reshape the landscape of healthcare, this workshop aims to explore the transformative potential of AI in the field of cancer research and treatment. The integration of AI technologies presents unprecedented opportunities to enhance early detection, precision medicine, and personalized treatment strategies for cancer patients. This session will delve into the machine learning (ML) based models identifying novel biomarkers, predicting cancer risk as well drug resistance using multi- modal data including EHR, multi-omics and imaging. By bringing together experts from both the AI and cancer research communities, this session aims to foster collaborative efforts, share insights, and pave the way for a future where AI plays a pivotal role in advancing cancer care.

Scope

In the rapidly evolving landscape of healthcare, the integration of artificial intelligence (AI) stands as a beacon of transformative potential, promising to advance patient care, diagnosis, and treatment methodologies. This workshop will include the AI applications in oncology, fostering innovation and improving patient outcomes. Participants will learn about the latest AI advancements and real-world applications in cancer diagnostics, treatment and prognosis. Through expert-led sessions, and collaborative learning, attendees will navigate the latest AI methods in oncology. The workshop is designed for a broad audience, including healthcare professionals, researchers, students, and anyone interested in the intersection of AI and oncology.

TOPICS

  • Machine learning based predictive models using genomic, EHR and imaging data in oncology
  • Natural language processing (NLP) techniques for mining information from real-world oncology data including EHR
  • Large language models (LLMs) applying real-world data in oncology research
  • AI applications in radiology
  • Image analysis for tumor detection and classification
  • Machine learning techniques for genomic data interpretation
  • AI-driven drug discovery and development in oncology
  

PROGRAM

Time

Format

Topic

Speakers

 

Podium Presentation

Z-Fusion: Uncertainty-Stabilized Feature Engineering Outperforms Model Selection in p ≫ n Rare-Disease Cohorts for Transcriptomic Predictions

Yves Lussier, University of Utah

 

Podium Presentation

FusionRank: A Learning-to-Rank Framework for Prioritizing Oncogenic Gene Fusions in Cancer

Kai Wang, University of Pennsylvania

 

 

 

Podium Presentation

 Multi-modal Deep Learning Models for Cancer Recurrence Prediction

Ece Uzun, Brown University

 

Podium Presentation

Network-based Analysis of Gliobastoma Recurrence Using Graph Neural Networks

Alper Uzun, Brown University

 

Committee

Zhongming Zhao
Zhongming Zhao, PhD
Chair, Professor for Precision Health UT Health Houston
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Qianqian Song, PhD
Assistant Professor, Health Outcomes & Biomedical Informatics University of Florida
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Guangyu Wang, Phd
Associate Professor, Cardiovascular Sciences Texas Methodist Hospital
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Aik Chan (AC) Tan, PhD
Professor The University of Utah Huntsman Cancer Center