In 2026, two thirds of men undergoing a first prostate biopsy underwent an MRI beforehand

Any man facing a biopsy should be asking whether an MRI is appropriate for him, because it changes both whether a biopsy is needed and where the needles go

Researchers saw MRI use fall off in men with very high PSA levels and in men over 80, which may reflect reasonable clinical judgment in some cases, but is worth examining rather than assuming

MRI use increasing is a good thing, but there are still real gaps and issues with MRI quality

A rare inherited mutation in the Epidermal Growth Factor Receptor (EGFR) gene is associated with a 25-fold increased risk of lung cancer, according to a study by investigators at Dana-Farber Cancer Institute and 23andMe Research Institute. The findings included de-identified, aggregated genetic data from more than 3.3 million 23andMe research-consented participants.

Researchers present the Virtual Biotech, a multi-agent AI system to inform drug-development decisions. By combining diverse biomedical and clinical evidence in a unified platform, the system may help identify therapeutic opportunities that would otherwise be missed. Drug discovery is a costly process – in both time and resources – yet about 90% of drug candidates entering clinical trials fail, usually due to inadequate efficacy or safety. Although researchers can now draw on vast amounts of genetic, genomic, molecular, and clinical evidence to improve these efforts, these data are fragmented across disciplines, making them difficult to integrate and interpret at the scale required for modern drug development. To help with this, Harrison Zhang and colleagues introduce the Virtual Biotech, a multi-agent AI platform designed to address bottlenecks by coordinating specialized AI “scientist” agents under the direction of a virtual chief scientific officer. According to the authors, unlike earlier AI systems that typically focus on individual biological analyses or rely on a single agent, the Virtual Biotech is designed to retrieve and analyze diverse biomedical and clinical evidence to support early-stage drug development decisions.

Zhang et al. tested the platform at three key points in drug development. Across 55,984 clinical trials, more than 37,000 AI agents found that drugs targeting genes specific to particular cell types were 48% more likely to reach the market and associated with 32% fewer adverse events. The system also combined diverse biological evidence to suggest a therapeutic strategy for lung cancer and analyzed a failed ulcerative colitis trial to identify possible reasons for its failure, demonstrating how human-guided AI teams could provide transparent, large-scale analysis to inform drug-development decisions. However, the authors note that Virtual Biotech’s conclusions are limited by the quality and breadth of available data, are not yet suitable for poorly studied diseases or targets, and still must be experimentally validated. “The Virtual Biotech illustrates a shift from isolated AI tools toward coordinated systems that reason across biological scales and stages of translation,” write the authors. “Rather than replacing scientists, agentic systems could expand the scope and speed of therapeutic hypothesis exploration while making the reasoning process more transparent and reproducible.”