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.
SCOPE
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.
TOPICS
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PROGRAM FORMAT
Time |
Format |
Topic |
Speakers |
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 |
Break |
Connect and collaborate |
N/A |
60 minutes |
Panel |
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 |
TBN |
Scientific Paper Program Committee
Peter Rijnbeek, PhD
Mattia Prosperi, PhD
Xia Ning, PhD
Yifan Peng, PhD
Xiaoqian Jiang, PhD
Larry Han, PhD
Thomas Reese, PharmD, PhD