Workshop Overview
Advances in Super Intelligence (SI), laboratory robotics, automated measurement, and data integration are creating new opportunities to connect experimental design, execution, measurement, and analysis into increasingly integrated research workflows. Across cancer research and care, these approaches could improve reproducibility, enable the study of complex biological systems at greater scale, accelerate biomarker and therapeutic development, and support the translation of laboratory discoveries toward clinical applications.
The National Cancer Institute (NCI), with participation from representatives across NIH, will convene a three-day virtual workshop to examine the opportunities and challenges associated with SI-enabled, human-supervised autonomous laboratory systems for cancer research and care.
The workshop will bring together experts in cancer biology, SI-enabled scientific discovery, robotics, engineering, biomarker science, laboratory automation, clinical translation, standards, and biomanufacturing. Discussions will examine where these approaches can contribute today, where important scientific and technical gaps remain, and what capabilities and evidence will be needed for their reliable, reproducible, and responsible use across the cancer research and care continuum.
Workshop Goals
The workshop will:
- Identify promising use cases for autonomous laboratory systems across cancer research, diagnosis, treatment development, and care.
- Examine how SI-guided experimental design, robotic execution, automated measurement, analysis, and iterative learning can be connected into integrated research workflows.
- Discuss requirements for validation, benchmarking, reproducibility, interoperability, quality assurance, safety, and human oversight, including where human judgment remains essential.
- Identify scientific and technical gaps that could inform future research, collaborations, standards, and related activities.
Day 1 — Deciding What to Test, and on What
Session 1: SI-Guided Discovery and Experimental Design
This session will explore how SI systems and scientific agents can support hypothesis generation, experimental prioritization, protocol development, and iterative experimental design.
Session 2: Automated Patient-Derived Models
This session will examine automation of organoids, tissue models, and other patient-derived experimental systems, including opportunities to improve reproducibility, scalability, and experimental throughput.
Day 2 — Measuring and Validating
Session 3: Automated Analysis of Human Cells and Clinical Specimens
This session will focus on technologies for automated processing, measurement, and analysis of cells and clinical or research specimens, including integrated sample-to-data workflows.
Session 4: SI-Enabled Biomarker Discovery and Validation
This session will examine how SI-enabled analysis and high-dimensional measurement technologies can support biomarker discovery, validation, and translation.
Day 3 — Executing and Scaling
Session 5: Self-Driving Labs: Laboratory Automation and Robotic Experimentation
This session will explore the integration of robotics, instrumentation, sensing, software, and SI into experimental workflows capable of executing, monitoring, and adapting experiments.
Session 6: Infrastructure, Interoperability, and Biomanufacturing
This session will address the computational, data, robotic, and manufacturing infrastructure needed to support reliable and scalable autonomous laboratory systems.
Cross-Cutting Themes
Reproducibility · Validation and Benchmarking · QA/QC · Data Provenance · Interoperability · Data Governance · Biosafety · Failure Detection and Recovery · Human Oversight · Scalability · Accessibility · Workforce Needs
Organizing Committee
National Cancer Institute
Division of Cancer Treatment and Diagnosis (DCTD)
Asif Rizwan, PhD
Jeffrey Buchsbaum, MD, PhD, AM, FASTRO
Hala Makhlouf, MD, PhD
Miguel Ossandon, PhD
Jung Byun, PhD, MPH
Claire Robustelli, BA
Ronald Taylor, PhD
Brian Sorg, PhD, MBA
Division of Cancer Prevention (DCP)
Caitlin Bailey, PhD
Division of Cancer Control and Population Sciences (DCCPS)
Dana Wolff-Hughes, PhD
Small Business Innovation Research (SBIR) Development Center
Ming Zhao, PhD
NIH Partners
NIH Office of the Director
Sahana Kukke, PhD
National Center for Advancing Translational Sciences (NCATS)
Kelli Wilson, PhD
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Moria Bittmann, PhD
Contact
Asif Rizwan, PhD
Program Director, Diagnostic Biomarkers and Technology Branch
Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis
National Cancer Institute, National Institutes of Health
9609 Medical Center Dr., Room 3W530
Rockville, MD 20892
Email: asif.rizwan@nih.gov