Pragmatic and Observational Research

Real World Data and AI/Machine Learning for Drug Development and Drug Evaluations

This Article Collection will focus on studies that use real world data (RWD) and artificial intelligence (AI) and machine learning (ML) to conduct drug development and drug evaluation research. Our goal is to highlight the integration of RWD with AI/ML to promote pragmatic and observational research.

RWD, such as electronic health records (EHRs) and insurance claims, when combined with AI/ML, offer a unique opportunity to develop innovative approaches to conduct drug development and evaluation research. By combining AI/ML and RWD, drug development and evaluation can become more data-driven, efficient, and patient-centric, ultimately leading to faster discovery, development, and delivery of safe and effective drugs.

Manuscripts should be written for a broad target audience within the field of pragmatic and observational research. Manuscripts addressing the spectrum of the integration of RWD with AI/ML for drug development and evaluation research will be considered, with the following being of interest:

• Illustration of best practices of RWD with use cases demonstrating improved drug development and evaluation and health outcomes
• The integration of drug development and evaluation with RWD and AI/ML, including EHRs, insurance claims, patient registries, and other data sources with linkage to public health entities
• The development and applications of novel approaches (e.g., novel AI/ML and causal principled models) to study drug development and evaluation
• Opportunities and challenges for digital health technologies to transform drug development and evaluation and improve patient care
• The improvement of clinical research enrollment diversity for drug development and evaluation
• Use cases of studying drug development and evaluation and enabling learning health systems and learning health communities
• Infrastructure, including governance, IT support from EHRs, and key implementation best practices to support RWD and AI/ML to conduct research for drug development and evaluation

All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisors for this Collection will not be handling the manuscripts (unless they are an Editorial Board member). Please review the journal scope and author submission instructions prior to submitting a manuscript.

Please submit your manuscript on our website, using the promo code LIMJS for 20% off the advertised article processing charge and to indicate that your manuscript will be considered for the “Real world data and AI/machine learning for drug development and drug evaluations” Collection.

The deadline for submitting manuscripts is 31 December 2025. For questions about this Article Collection, including inquiries regarding discounts off of the article publishing charges, please contact Commissioning Editor Haoyang Yi at Haoyang.Yi@taylorandfrancis.com.

Guest Advisors

Jiang Bian, Professor & Chief Data Scientist, University of Florida

bianjiang@ufl.edu

Dr. Bian is currently a Professor and Division Chief of Biomedical Informatics in the Department of Health Outcomes & Biomedical Informatics, College of Medicine, at the University of Florida (UF) and the Chief Data Scientist & Chief Research Information Officer (CRIO) for the UF Health system. He also serves as the Chief Data Scientist for the OneFlorida+ Clinical Research Consortium, Director of Biomedical Informatics program of the Clinical and Translational Science Institute (CTSI) at UF, and the Director of Cancer Informatics Shared Resource at the University of Florida Health Cancer Center (UFHCC). Dr. Bian has a diverse yet strong multi-disciplinary background and extensive expertise in machine learning, natural language processing, network science, ontology development and evaluation, semantic web technology and software engineering. He has extensive experience in developing informatics tools and systems, as well as expertise in data science methods for the analysis and interpretation of biomedical and textural data. Especially, he has a track record of building data infrastructure and using electronic health records (EHRs) for research and natural language processing (NLP) tools.

Serena Guo, Assistant Professor, University of Florida

Dr. Guo is an Assistant Professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida (UF) College of Pharmacy. She received her MD from Peking University in Beijing, China and her PhD in Epidemiology from the University of Pittsburgh. Dr. Guo conducts research in pharmacoepidemiology and pharmacoinformatics, primarily focused on cardiometabolic diseases and neurodegenerative conditions (e.g., dementia) with the goals of promoting precision health and health equity. Her research draws on large real-world data (e.g., electronic health records and insurance claims data) and advanced analytics (e.g., AI/machine learning, causal-principled modeling, and geospatial analyses).

Lixia Yao, CEO, Polygon Health Analytics LLC

lixia@polygonhealthanalytics.com

Dr. Lixia Yao is the founder and CEO of Polygon Health Analytics LLC, which develops high-quality real-world data (RWD) and rigorous real-world evidence (RWE) in disease areas with pressing unmet medical needs. With two decades of experience in this field and by leading a team of data scientists and healthcare professionals and collaborating with world-renowned scholars and clinicians, she has published over 60 peer-reviewed scientific articles, including several high-impact publications in prestigious journals such as Nature Biotechnology, Genome Research, and Drug Discovery Today. Her H-index is 20. She is also the recipient of a Career Development Award in Biomedical Informatics (K01) from the National Library of Medicine for 2016-2019, a Fellow of American Medical Informatics Association (FAMIA), the Chair of the AMIA KDDM working group from 2020-2022, and the Member Engagement Co-Chair for The Professional Society for Health Economics and Outcomes Research (ISPOR) Oncology Special Interest Group for 2023-2024. Additionally, she serves as an adjunct associate professor in Department of Health Services Administration and Policy at Temple University.