Artificial Intelligence in Oncology

Artificial Intelligence in Oncology

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Call for Submissions

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

5 Minutes

Podium Presentation

Introduction & Keynote

 

30 Minutes

Podium Presentation

Opening Keynote

TBD

40 Minutes

Research presentations

Various research topics 

Communication author of 3 to 4 accepted abstract/papers

15-20 Minutes

Break

Connect and collaborate

N/A

 

30 Minutes

Podium Presentation

Middle Keynote

Ece Uzun

40 Minutes

Research presentations

Various research topics

Communication author of 3 to 4 accepted abstract/papers

30 Minutes

Podium Talk

Closing Keynote

TBD

 

Committee

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