Tag: Cancer Diagnostics
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AI Detects Cancer on Pathology Slides but Biases Across Demographics Raise Questions
Overview: AI in pathology reveals promising accuracy with troubling disparities Artificial intelligence systems trained to diagnose cancer from pathology slides have shown impressive overall accuracy, offering the promise of faster, more consistent readings. Yet a growing body of research indicates that these systems do not perform equally well for all patients. In some cases, diagnostic…
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New Blood Test Enables Real-Time Detection and Monitoring of Lung Cancer
Groundbreaking Step in Lung Cancer Care A new blood test developed by researchers in the United Kingdom could change how lung cancer is diagnosed and tracked. The test, described by scientists as a pioneering approach, aims to detect lung cancer and monitor its progression in real time using a simple blood sample. If validated in…
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Real-Time Lung Cancer Blood Test: Detect and Monitor in Real Time
New Blood Test Offers Real-Time Detection and Monitoring of Lung Cancer A team of researchers in the United Kingdom has unveiled a pioneering blood test that could change how lung cancer is diagnosed and tracked. By analyzing a specific panel of biomarkers in a patient’s blood, the test promises to detect the disease earlier and…
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Oncomarkers Awareness Day: Advancing Precision Oncology Through Biomarker Science
Introduction: A Global Day for Biomarker Science Oncomarkers Awareness Day, first observed on November 13, 2025, marks a milestone in the cancer care landscape. This international observance highlights how biomarkers—the measurable indicators of biological processes—are shaping precision oncology. By providing richer diagnostic insights, guiding therapy choices, and monitoring treatment response, biomarkers are helping clinicians tailor…
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Google Maps for Lung Cancer: A New AI-Driven Path to Precision Treatment
Revolutionizing Lung Cancer Treatment with a Google Maps Approach Researchers are adopting a groundbreaking method likened to a “Google maps” of cancer to guide treatment decisions for non-small cell lung cancer (NSCLC). In a multi-institution study led by Associate Professor Arutha Kulasinghe from the Frazer Institute at the University of Queensland, alongside teams at Yale…
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Unpacking the black box of AI: Explainable AI in cancer detection with Dr. Sakib Mostafa
Understanding the black box: why explainable AI matters Artificial intelligence has advanced rapidly, but one of its most consequential challenges remains the so-called “black box” problem. Dr. Sakib Mostafa, a Bangladeshi-Canadian researcher, frames this not as a sci‑fi nightmare but as a practical hurdle: the results that AI models produce can be correct yet inexplicable.…