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.”

An artificial intelligence (AI) model called RADAR, designed to provide broad diagnostic interpretation of abdominal CT scans, substantially outperformed existing AI systems across a wide range of diseases and clinical settings, researchers report. The findings demonstrate the potential of vision-language models to advance expert-level generalist AI tools for radiology. The goal of AI in radiology is to create an expert-level generalist system capable of providing accurate, comprehensive diagnostic support across a wide range of real-world clinical situations. This, however, remains particularly challenging for contrast-enhanced abdominal computed tomography (CT), which often involves the evaluation of dozens of organs and the identification of hundreds of potential diseases, a task that requires physicians with specialized expertise and training. Although vision-language models offer a promising alternative by learning from associations between medical images and their accompanying text, abdominal CT presents unique challenges because relevant diagnostic information is sparse and highly dependent on complex anatomical context.

Here, Qi Zhang and colleagues present Rapid Abdominal Diagnosis with AI and Radiology (RADAR) – a vision-language AI model designed to provide broad diagnostic interpretation of contrast-enhanced abdominal CT scans. Trained on a dataset of 424,911 examinations, containing 1.5 million image-text pairs and more than 15 million anatomy-specific pairs, RADAR breaks CT volumes into individual anatomical structures and links those structures to corresponding descriptions in radiology reports, using a large-scale contrastive-learning approach to improve the connection between imaging findings and diagnoses. Throughout internal and external evaluations across multiple medical centers and varied clinical scenarios, Zhang et al. found that RADAR substantially outperformed existing specialist AI models, achieving a mean AUC (the average probability that a model can correctly separate positive and negative cases across evaluations) of 0.913 across 146 abdominal CT findings, compared with 0.776 for the best competing vision-language model. The model also performed well in challenging emergency settings, despite not being specifically trained on emergency data, achieving an AUC of 0.904 across more than 27,000 emergency CT cases. According to the authors, its performance remained strong across organs, diseases, and emergency cases, including uncommon findings. Moreover, in testing in cohorts at eight external centers, RADAR maintained high accuracy (AUC 0.895), demonstrating robust generalization across diverse patients, clinical settings, and imaging protocols.