Abbott is set to acquire Exact Sciences for approximately $21 billion, gaining access to cancer screening tests such as Cologuard and multi-cancer early detection blood tests. The acquisition, expected to close in the second quarter of 2026, is projected to boost Abbott's diagnostics sales to over $12 billion annually.

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Researchers at the University of Basel have engineered a bone marrow model using human cells, a first in bioengineering. The model, detailed in the journal Cell Stem Cell, replicates the complex microenvironments of human bone marrow, including the endosteal niche, which is crucial for blood formation and cancer resistance. This advancement could lead to personalized treatments for blood cancers and reduce reliance on animal testing.

The Ichan School of Medicine at Mount Sinai and Israel's Sheba Medical Center have partnered with NVIDIA to use AI technology to explore the human genome. This three-year collaboration aims to decode the majority of the genome, unlocking new pathways for disease prevention, diagnosis and treatment. The initiative will leverage extensive genome datasets and advanced computational tools to create an AI-driven research engine, potentially transforming personalized medicine and health care outcomes globally.

"By bringing advanced AI into genomic research, we're moving closer to making personalized, precision medicine a reality for all," said Dr. Alexander Charney, the director of the Charles Bronfman Institute for Personalized Medicine and vice chair of the Windreich Department of Artificial Intelligence and Human Health at Mount Sinai.

A study used machine learning to identify inflammation-related diagnostic biomarkers for primary myelofibrosis. Researchers used transcriptomic data from several sources to identify differentially expressed genes between patients with primary myelofibrosis and healthy controls.

A study involving 199 participants found that the analysis of serum volatile organic compounds using gas chromatography-ion mobility spectrometry, coupled with machine learning, can effectively distinguish between patients with metabolic dysfunction-associated fatty liver disease and healthy controls.

Artificial intelligence is transforming the pharmaceutical industry by enhancing biomarker analysis, which is crucial for understanding disease mechanisms and patient stratification. However, challenges such as regulatory alignment and data standardization must be addressed to ensure the reliability and clinical applicability of AI-driven biomarkers.