Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 155

Ancestry-Associated Performance Variability of Open-Source AI Models for EGFR Prediction in Lung Cancer

Open-source AI models for LungCancer EGFR mutation prediction showed high accuracy overall but reduced performance in Asian patients and pleural samples, indicating the need for broader validation.

Importance Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from routine pathology slides. Although these approaches could accelerate treatment decisions in lung cancer, little is known about whether their performance is consistent across diverse patient populations and tissue contexts.

Objective To evaluate the performance and generalizability of 2 open-source AI pathology models for predicting EGFR mutation status in lung adenocarcinoma (LUAD) across independent cohorts and ancestral subgroups.

Design, Setting, and Participants This cohort study included patients with LUAD from 2 cohorts: Dana-Farber Cancer Institute (DFCI) from June 2013 to November 2023, and a European-based trial (TNM-I) from August 2016 to February 2022. All patients had paired next-generation sequencing data and hematoxylin-eosin–stained whole-slide images. In the DFCI cohort, genetic ancestry was inferred using germline genotype data. Data analyses were performed from July 2025 to September 2025.

Sebastien Bubeck — A Combinatorics Problem — IPAM at UCLA

Recorded 10 February 2026. Sebastien Bubeck of OpenAI presents “A Combinatorics Problem” at IPAM’s AI for Science Kickoff. Learn more online at: https://www.ipam.ucla.edu/programs/sp… AI for Science Kickoff 2026: This inaugural event brings together the pioneers who are defining how AI will accelerate scientific discovery — from Nobel and Fields Medal laureates to the leaders shaping AI innovation across academia, research labs, and industry. The event features keynote talks by leading AI Scientists and Mathematicians, as well as panel discussions focusing on perspectives on AI from three sides: Mathematics, Higher Education, and Industry. This event is organized jointly by IPAM, the UCLA Division of Physical Sciences, the SAIR Foundation and the World Leading Scientists Institute.

AI model flags insulin resistance as a risk factor for 12 cancers

Insulin resistance—when the body doesn’t properly respond to insulin, a hormone that helps control blood glucose levels—is one of the fundamental causes of diabetes. In addition to diabetes, it is widely known that insulin resistance can lead to cardiovascular, kidney and liver diseases. While insulin resistance is tightly associated with obesity, it has been difficult to evaluate insulin resistance itself in the clinic. For the first time, researchers, including those from the University of Tokyo, applied a machine learning-based prediction model of insulin resistance to half a million participants from the UK Biobank and demonstrated that insulin resistance is a risk factor for 12 types of cancer.

The research is published in Nature Communications.

Diabetes is a common cause for concern around the world. Its connection to insulin resistance is a familiar concept to many, but what is less well known is that resistance to insulin is also suggested to be a risk factor for several cancers. However, the human body is a complex thing, and ascertaining causal connections between diseases and issues within the body is far from easy.

/* */