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Keep up with the latest news from the NCI Center for Biomedical Informatics and Information Technology (CBIIT) and the data science communities.

MRI image of an enlarged prostate.

NCI Uses AI to Take the Guesswork Out of Assessing Prostate Cancer Images

NCI researchers are using artificial intelligence (AI) to uncover additional information from medical images. This helps them not only to diagnose cancer but also to predict how it will progress and if it will re-occur.

A strand of DNA is shown inside a brightly colored circle. A second circle highlights a portion of the DNA and is designated by an “+”. Depicts how high-tech tools are being used to better understand genetics.

Machine Learning Approach Helps Interpret Genome-Wide Dynamics

A new, scalable, machine learning model is helping scientists model thousands of transcription factors and genes in the human genome, providing new information on these genes and how they work/change over time.

Data center full of rack servers and super computers in rows.

NCI-DOE and BridgeBio Researchers Leverage HPC to Power Cancer Drug Discovery

Learn how supercomputing and cancer research expertise helped transform a previously complex, ‘undruggable’ target into a promising, first-in-class medication. Researchers are now testing it in human trials.

Magnifying glass with the word “Bias” written on it hovers over a group of figures. Depicts the importance of searching for (and correcting) bias when developing a machine learning model.

Algorithm Helps Address Bias in Machine Learning

Are you developing machine learning and looking for ways to make your model generalizable to a diverse population? A recent study describes an algorithm that centers on transforming your data rather than tweaking your model.

Scan of the human body with AI logo

NCI Study Shows AI-Assisted Contouring Improves Prostate Cancer Treatment Efficacy

In the study, artificial intelligence (AI)-assisted contours outperformed manual contours, indicating the technology’s potential to enhance treatment strategies and improve outcomes for patients.

two people, one in a lab coat one in regular clothes, looking at a tablet. Data themed icons in background.

Predicting Cancer Treatment Responses with AI Tool PERCEPTION

Discover a new AI-driven tool that uses single-cell RNA data to help predict patient responses to cancer treatments.

Graphic depicts the evaluation of AI. A scale is shown at the center of the graphic. The scale weighs the accuracy of AI (“accuracy” positioned to the left of the scale and depicted as the letters “A-I” within cogwheels) and the results/what’s learned of AI (“results” positioned to the right of the scale and portrayed as a human brain). Arrows encircle the top and bottom of the scale, illustrating a cycle.

Evaluating Diversity in an Artificial Intelligence (AI) Model for Breast Cancer

Can you develop an algorithm that applies to every patient, in every situation? A recent study shows the importance of adding diversity to your data when developing AI models.

The letters "AI" appear in a box. Brightly colored lines feed into the box from all sides, showing how data flows into the box, where the information is then processed by AI.

Interpretable AI Helps Predict the Effectiveness of Immunotherapy

Learn about NCI’s LORIS, a new artificial intelligence-approach to help you predict how a patient will respond to immunotherapy.

A person wearing a stethoscope sits at a lap top computer. Above the keyboard are a number of small icons and dotted lines, signifying artificial intelligence. The letters "AI" appear just above the keyboard.

Artificial Intelligence Aids in Predicting Prostate Cancer

See how NCI researchers are using artificial intelligence to develop tools that may someday help oncologists make more informed decisions when caring for people with prostate cancer.

An illustrated colon is shown, cradled in the outstretched hands of a doctor wearing a white lab coat.

Machine Learning Helps Refine a New Approach for Early Colorectal Cancer Screening

NCI-funded researchers are using machine learning to help identify an early-warning screening approach for colorectal cancer.

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