From Multiomics to Clinical Intelligence: Volv Global at Frontiers Health 2026
Volv Global at Frontiers Health's panel on AI & biomarker science translating complex biological signals into cli...
Volv Global expands the boundaries of medical understanding and builds awareness of difficult-to-diagnose diseases among physicians and KOLs with credible real-world evidence for scientific exchange.
Detect novel biomarker sets for more complete understanding of disease and faster diagnosis of difficult-to-diagnose patients.
Use advanced machine learning techniques to overcome issues of longitudinality, unstructured data, data sparsity and bias in data sources.
Identify cohorts more precisely and at an early stage for better longitudinal assessment of patient health outcomes.
Understand how disease progresses across populations, including early presentations and fast-progressing sub-groups, and bring that evidence to KOLs.
Medical affairs in the face of complexity
Limited understanding of underexplored disease areas or newly identified phenotypes.
Rapid advancements in disease understanding create challenges in conveying evolving disease knowledge to healthcare professionals.
Challenge in generating real-world evidence (RWE) for understanding of diseases, esp. for rare diseases or newly emerging conditions.
Variability in disease presentation across diverse patient populations.
Identifying and characterising rare adverse events or long-term outcomes.
Supporting Medical Affairs
Growing the real-world evidence on a disease, and a clearer view of how many people it affects.
Studying the early biomarker patterns that precede a condition, sometimes years before conventional signs appear.
Modelling how disease progresses across diagnosed populations, using real-world evidence.
Sizing the diagnosed population not yet on treatment, and showing where the gap to care opens.
Leading AI/ML methodology to support Medical Affairs
Medical affairs teams benefit from our machine learning methodology’s ability to reveal crucial patient indicators and predict treatment response, drawing on advanced AI and machine learning.
By identifying subtle disease biomarkers, phenotypes, or other clinical features, our methodology informs scientific exchange and shapes evidence-based recommendations.
Coupled with real-world insights on disease progression, medical affairs professionals can counsel healthcare providers on the most effective interventions.
Shaping the future today
Volv Global at Frontiers Health's panel on AI & biomarker science translating complex biological signals into cli...
Volv Global CEO Christopher Rudolf will speak at the DREAMS Neuromuscular Disease AI Webinar
Volv Global at the 25th Annual NEALS Meeting, presenting new research on early identification of ALS using advanced M...
Presenting new research on whether a machine learning model can flag which patients with CAP are most likely to progr...
The next useful thing AI does in solid tumour care is about timing, not detection. The patient must reach the right t...
Results for the model flagging early ARDS risk in pneumonia will be presented at ERS Congress 2026.
Volv Global at ERS Congress 2026, presenting new research in collaboration with CSL Behring: Can We Catch ARDS Before...
Volv Global applied machine learning to 24M UK primary care records, surfacing ~1,200 undiagnosed GEP-NET patients, 5...
GEP-NETs are among the most diagnostically elusive tumours in oncology. This case study shows how machine learning ap...
ASCO 2026 produced landmark oncology data. The question no one asked from the podium: can clinical infrastructure get...
Machine learning applied to 3,549 confirmed Pompe disease patients – the largest machine learning study of the diseas...
New research demonstrates that machine learning can map clinician-defined endpoints to real-world claims data in Pomp...