How is AI actually being applied to rare neuromuscular diseases today?
Artificial intelligence is increasingly discussed across rare disease research. But what does its application look like in practice — and specifically in rare neuromuscular diseases (NMDs)?
On 15 October 2026, the Horizon Europe DREAMS project will bring together experts working on concrete applications of AI, machine learning and data across the rare neuromuscular disease field.
The webinar will explore different parts of the rare NMD landscape: from earlier identification and diagnosis, to machine learning for target and drug discovery, and the use of population-scale real-world data and machine learning to address diagnostic gaps and identify patients who may be eligible for existing therapies.
Rather than discussing AI in the abstract, the session will focus on the approaches being applied by organisations working directly in this field, the challenges they are addressing and their perspectives on the practical role of AI in rare neuromuscular diseases.
What will the webinar explore?
Rare neuromuscular diseases present challenges across multiple stages of the research and healthcare landscape. Data and computational approaches are increasingly being investigated and applied at different points along this pathway.
During this 75-minute webinar, three expert perspectives will explore how AI, machine learning and data are being used in the rare NMD field.
🔎 Earlier identification and diagnosis
How can computational approaches support the identification and diagnosis of people living with rare neuromuscular diseases?
Gohun Seo from 3billion will present an approach focused on early screening and diagnosis of rare neuromuscular diseases.
🧬 Machine learning for target and drug discovery
How can machine learning contribute to the search for biological targets and potential drug candidates?
Rubal Ravinder from KANTIFY, a DREAMS project partner, will discuss the application of machine learning to target and drug discovery in rare neuromuscular diseases.
Within DREAMS, AI approaches are being investigated alongside biological data and phenotypic screening to support research into shared drug targets and potential therapeutic candidates across rare NMDs.
📊 Real-world data and the diagnostic gap
How can population-scale real-world data help identify people who may otherwise remain difficult to detect within healthcare systems?
Christopher Rudolf from Volv Global will discuss how Volv applies population-scale real-world data and proprietary machine learning to address the diagnostic gap in rare neuromuscular disease, including approaches to surface patients who remain invisible to the health system and connect already diagnosed patients with therapies for which they may be eligible.
Program
