Invited Sessions
Invited sessions at the 2025 Informatics Summit are curated by the Scientific Program Committee (SPC) for their timeliness, significance, and relevance to the conference audience. Unlike submitted sessions, which undergo peer review, these sessions are selected to explore emerging topics, policy developments, critical challenges, and innovative advancements in translational bioinformatics, clinical research informatics, and AI/data science.
Featuring nationally recognized experts, the sessions offer engaging presentations followed by interactive discussions to enhance the conference experience with high-impact invited content.
S18: Bioinformatics and Genome Analysis
The generation of massive omics and phenotypic data has enabled investigators to study the genetic architecture and markers in many complex diseases; however, it poses a significant challenge in efficiently uncovering valuable knowledge. Here, we introduce GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging ChatGPT, we aim to make GENEVIC a biologist’s ‘copilot’. It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer’s disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. The implementation of BrainGeneBot is set to transform genomic research for AD and other brain diseases by improving data accessibility, accelerating discovery processes, and refining the precision of genetic insights.
S31: AI in Oncology
HemOnc.org is a free, collaborative wiki resource for hematology and oncology professionals. It provides detailed information on anticancer drugs, treatment regimens, guidelines, and patient resources. In the rapidly evolving field of cancer treatment, staying abreast of the latest research and advancements is crucial for both healthcare professionals and researchers. Machine learning has emerged as a powerful tool with the potential to revolutionize cancer research and improve patient outcomes. By applying machine learning techniques to analyze complex datasets and extract meaningful insights, we can enhance our understanding of cancer biology, optimize treatment strategies, and personalize patient care. This proposal outlines the potential of applying machine learning techniques to HemOnc.org data for three specific use cases: information-theoretic network meta-analysis, social network analysis, and ground truth for real-world evidence studies. Each use case will be described in terms of its methodology, potential benefits and challenges, and expected outcomes.
S32: Ethics in AI: Principles and Practices for Healthcare
As artificial intelligence (AI) shapes society, scholars, practitioners, and the public need to address ethics of applications in healthcare. Panelists from settings ranging from healthcare organizations to universities to industry will describe progress and challenges in ethics for AI in healthcare. Topics for discussion include but are not limited to confabulation in large language models, bias and fairness for predictive analytics, organizational issues, scientific reproducibility, and emerging best practices for implementation of ethical AI in healthcare. By addressing ethical issues with respect to AI system developers, care team members, and patients among other groups, the panel will provide a comprehensive overview of progress and challenges in ethics in AI in healthcare.
S36: Increasing Patient-Centered Research at Scale: PCORnet Technology Enhancements
PCORnet® aims to conduct national-scale, patient-centered comparative outcomes research through collaboration among 78 health systems within 8 clinical research networks (CRNs). These CRNs are currently engaged in a coordinated technology enhancement effort designed to support increasing analytic complexity across diverse computing environments, each presenting clinical data harmonized according to the common data model (CDM). This panel brings together informatics leaders from CRNs and the PCORnet ® coordinating center to discuss requirements and progress in implementing solutions as part of this program. Panelists will discuss a variety of different approaches adopted across CRNs and associated technical and governance implications as well as efforts aimed at maintaining interoperability and balancing resource demands on participating health systems with the necessity of conducting analyses that meet current research needs. This discussion will be valuable for participants in, or potential members of, research collaborations that involve the sharing of clinical data via distributed querying.
S37: Evaluating Artificial Intelligence to Enable Patient Care
As artificial intelligence (AI) continues to shape the future of healthcare, rigorous evaluation is critical to ensure its safety, efficacy, and equity in patient care. This panel brings together leaders implementing healthcare AI in their institutions to explore key frameworks for evaluating AI in real-world settings. Panelists will discuss best practices for evaluating AI models, measuring clinical impact, and addressing challenges such as bias, transparency, and regulatory compliance. The session will highlight case studies of AI deployment in hospitals and healthcare systems, offering insights into how institutions can integrate AI responsibly to enhance patient outcomes. Attendees will gain a deeper understanding of how to critically evaluate AI-driven solutions, ensuring they meet the highest standards of clinical excellence, ethical integrity, and regulatory compliance.
S41: NIH CTSA Enterprise Committee Evolution: From Informatics to Biostatistics, Biomedical Informatics and Data Science (BIDS)
The National Institutes of Health (NIH) has recognized biomedical informatics (BMI) as a critical component of the Clinical and Translational Science Awards (CTSA) Program within the National Center for Advancing Translational Sciences (NCATS). To foster communities of scholarship and practice, the CTSA Program has sponsored multiple Enterprise Committee (ECs), including an Informatics Enterprise Committee (iEC) comprised of scientists and professionals providing BMI functions at CTSA hubs. In 2024, NIH guided BMI and Biostatistics, Epidemiology, and Research Design (BERD) communities to align more closely to enable translational science through the CTSA Program. In this panel, informatics leaders from CTSA hubs will discuss the evolution of the iEC to become the Biostatistics, Biomedical Informatics, and Data Science (BIDS) EC.