Upcoming Events
Advancing Multimodal Clinical SI Through Federated Fine-Tuning of Foundation Models
November 4, 2026 | 11:00 a.m. - 12:00 p.m., ET
Ghulam Rasool, Ph.D., Moffitt Cancer Center
Discover how NCI and NCI-designated cancer centers are working together to advance multimodal clinical oncology via federated learning-based approaches.
Past Recordings
- Pathology AI Foundation Models Across Modalities and Spatial Scales
Explore how AI foundation models are expanding the possibilities of computational pathology. - AI-Driven Spatial Proteomics to Better Understand Prostate Cancer Tumors | June 3, 2026
Discover how a team structures prostate cancer data sets to be suitable for AI and foundational models. - Validating AI in Digital Pathology for Clinical Use Beyond the Algorithm | April 15, 2026
Discover the challenges and successes of developing standardized artificial intelligence models in digital pathology for cancer diagnostics. - Multi-modal Modeling in Precision Medicine: From Data Imputation to Synthetic Data | March 18, 2026
Learn about “cross-modal data modeling”—a methodology that uses foundation models (i.e., large, pre-trained, artificial intelligence models) to ascribe missing biomedical research data and generate realistic synthetic samples. - Robotics, Agents, and World (RAW) Models to Target Cancer | February 18, 2026
Discover how artificial intelligence (AI), robotics, and predictive models could transform cancer drug development. - Theranostics Digital Twins: A Path to Personalized Medicine | January 14, 2026
Explore the intersection of digital twins and theranostics and how these two critical data science concepts work in practice in liver, prostate, and metastatic cancer. - Developing Safe AI Models for Clinical and Multi-institutional Research Studies | September 3, 2025
Explore approaches and use cases for developing safe AI in clinical and multi-institutional research studies. - Ctrl+Alt+Cure: Driving Smarter Cancer Care | June 11, 2025
Discover how AI transforms cancer care, covering key terminology, history, clinical developments, diagnostics, and drug discovery in oncology. - AI in Cancer: Bridging Context from Design to Delivery | April 2, 2025
Learn how AI will revolutionize healthcare by enhancing diagnostic precision, optimizing treatment plans, and improving patient outcomes. - AI-Augmented Pathology: Enhancing Real-Time Cancer Diagnosis and Discovery | March 5, 2025
Explore how AI is impacting cancer research and clinical diagnosis by transforming the field of pathology. - Integrating AI and Generative AI in Operations and Research: From Creating the Team and Infrastructure to Supporting 2,000+ Users | November 13, 2024
Hear from a team of multidisciplinary experts who bridge the gap between big tech, operations, and research by leveraging cutting-edge AI technologies to enhance cancer research. - Biomedical Data Fusion Lab | July 17, 2024
Learn how to leverage various data types in medicine, including computational methods for improving decision-supported models for personalized diagnosis, prognosis, and therapy. - Docker Tech Talk | June 26, 2024
Discover how Docker simplifies and accelerates the app lifecycle workflow, providing freedom to innovate your choice of tools, application stacks, and deployment environments for projects. - The Current State of Transparency for AI Models and Datasets | June 12, 2024
Find out about tools improving AI model transparency, the provision of data set metadata, and recent initiatives to implement these tools in NIH's Bridge2AI program. - Machine Learning Dynamics in the Tumor Microenvironment | May 22, 2024
Learn about a set of statistical machine learning methods developed to infer the temporal and spatial dynamics of cells in the tumor microenvironment. - Data Quality for LLMs: Building a Reliable Data Foundation | April 24, 2024
Discover how data qualify affects the performance of large language models. - Identifying Recurrent Non-Hodgkin Lymphoma in Structured and Unstructured Electronic Health Data | April 3, 2024
Explore algorithms to better identifying patients experiencing non-Hodgkin lymphoma recurrence.