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AI for Reliable and Equitable Real World Evidence Generation in Medicine

AI for Reliable and Equitable Real World Evidence Generation in Medicine

The "AI for Reliable and Equitable Real World Evidence Generation in Medicine" workshop is dedicated to advancing the understanding and exploring the transformative role of artificial intelligence (AI) in analyzing real-world data (RWD) for real-world evidence (RWE) generation, leading to evidence-based medicine (EBM). Focused on leading-edge research and innovation, the workshop will feature research papers and panel discussions that delve into key aspects of machine learning innovations and applications in RWE generation from EHRs and claims, including structured data, natural language processing (NLP) of clinical notes, medical imaging, and waveform data processing from wearable devices. The workshop will feature both innovative AI methodology as well as their applications to real-world problems and their impact on transforming evidence-based medicine. The workshop seeks to facilitate in-depth discussions on the integration of AI technologies to enhance the reliability and equity of RWE generation. The workshop serves as a platform for engaging multiple stakeholders across healthcare research, including researchers, clinicians, pharmaceutical and industry professionals to delve into the intricacies of these advanced methodologies, fostering dialogue and collaboration. Attendees can anticipate in-depth discussions, presentations, and networking opportunities, gaining valuable insights into the forefront of AI-driven strategies shaping the future of these discoveries.


AI encompasses statistical and computational machine learning, deep learning, and generative AI (e.g., Large Language Model, Diffusion Models, etc), all are welcomed approaches. We include innovative AI methods as well as application of AI methods to the field of evidence generation for real-world effectiveness, safety, and equity research.


  • Causal AI in Healthcare: Exploring methods and applications integrating causal inference and machine learning to address selection bias and confounding bias in observational studies using real-world data, with a focus on comparative effectiveness and safety research
  • Multi-modality data integration and analysis: Multi-modality data acquisition, standardization, integration, and analysis frameworks and methods to provide more comprehensive understanding of patient health and equity in healthcare systems
  • Federated Causal Inference in Health Care: Federated causal inference for comparative studies using observational data, with a focus on addressing confounding bias, violation of overlap, and patient population difference across multiple data sources
  • Real-World Evidence (RWE) Generation from Multi-modality Data: Advancements in AI for analyzing multi-modality data and the advantages of multi-modality data for improving the reliability of RWE
  • Personalized Treatment Effect Estimation: Utilizing heterogeneous and individualized treatment effect estimation for personalized treatment planning, leveraging machine learning algorithms to tailor healthcare to individual patient needs
  • Impact of RWE on Evidence-Based Medicine (EBM): Review and discuss how AI innovations and applications in RWE generation from EHRs and claims data have
    impacted EBM, especially during COVID-19, and how they will shape the future of health care.
  • Assessing Health Disparities and Inequities: Quantifying the impact of social and environmental determinants of health (SEDOH) on healthcare resource allocation and health outcome to identify root causes of disparities through Causal AI






5 minutes

Podium presentation 

Welcome address

Linying Zhang

30 Minutes

Podium presentation

Opening Keynote

George Hripcsak

60 minutes

Research presentation

Various research topics on using AI/ML methods for real-world evidence generation

Communication author of 3 to 4 accepted papers

15 minutes


Connect and collaborate


60 minutes


Opportunities and challenges in RWE across Health Data Networks

Linying Zhang (moderator), Adam Wilcox, George Hripcsak, Mattia Prosperi

60 minutes

Research presentation

Various research topics on using novel AI/ML methods for health equity and fairness

Communication author of 3 to 4 accepted papers

20 minutes

Podium presentation

Closing Keynote


Scientific Paper Program Committee

Peter Rijnbeek, PhD
Professor and Chair of Medical Informatics Erasmus University Medical Center, Netherlands
Mattia Prosperi, PhD
Professor And Associate Dean For AI And Innovation University of Florida
Xia Ning, PhD
Professor of Biomedical Informatics and Computer Science and Engineering Ohio State University
Yifan Peng, PhD
Assistant Professor of Population Health Sciences Weill Cornell Medicine
Xiaoqian Jiang, PhD
Associate Vice President of Medical AI, Chair of Department of Data Science and Artificial Intelligence The University of Texas Health Science Center at Houston
Larry Han, PhD
Assistant Professor of Health Sciences Northeastern University
Thomas Reese, PharmD, PhD
Assistant Professor of Biomedical Informatics Vanderbilt University