TLDR: Health technology company Briya has launched Briya AIRE, the world’s first clinical-grade AI research assistant. Designed to interpret medical, clinical research, and epidemiological language, AIRE transforms months of manual data analysis into actionable insights within minutes, significantly accelerating discovery across biopharma and academic research sectors.
Health technology innovator Briya has announced the launch of Briya AIRE, a groundbreaking clinical-grade AI research assistant poised to transform biomedical and clinical decision-making. Unveiled on August 20, 2025, Briya AIRE is engineered to ‘think’ in the intricate languages of medicine, clinical research, and epidemiology, converting extensive manual data analysis into critical insights in a fraction of the time.
This innovative platform promises faster, more cost-effective discovery across both biopharmaceutical and academic research landscapes. Robert Brown, Vincent Astor distinguished professor of medicine at Weill Cornell Medicine, emphasized the profound impact, stating, ‘Incorporating Briya AIRE into research and clinical practice has the potential to revolutionise medical care.’ He added that by leveraging AI to collate patient data and clinical evidence, physicians can make more efficient and timely decisions, reduce administrative burdens, and dedicate more time to patient interaction and critical thinking.
Briya AIRE functions as a virtual epidemiologist and data analyst, empowering researchers to formulate more incisive questions and generate answers with unprecedented speed. Its capabilities include designing patient cohorts, testing hypotheses, refining study criteria, and uncovering data patterns that might otherwise remain undetected. By streamlining these crucial steps, AIRE optimizes study design and accelerates discoveries vital for drug development and patient care.
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Gadi Lalazar, head of the Liver Unit at Shaare Zedek Medical Center, highlighted a specific application: ‘Using the advanced NLP capabilities, we can now accurately identify patients with metabolic dysfunction-associated steatohepatitis (MASH) at significant risk of progressing to severe liver damage or cirrhosis, directly from unstructured clinical data across the healthcare ecosystem.’ This ability to automatically analyze imaging reports paves the way for earlier interventions for high-risk patients and demonstrates the platform’s broad potential across various medical indications.
