Leveraging Real-World Data and Artificial Intelligence in Medical Research: The MES-CoBraD Approach

The MES-CoBraD Horizon 2020 project aims to improve the diagnosis and treatment of Complex Brain Disorders by leveraging Real-World Data and Artificial Intelligence. This interdisciplinary project will contribute to advancing scientific knowledge and innovation, while fostering collaboration among various stakeholders in the field.

Leveraging Real-World Data and Artificial Intelligence in Medical Research

Complex Brain Disorders (CoBraD) encompass a variety of conditions that impact the brain's structure and function, including dementia, epilepsy, and sleep disorders. These disorders significantly affect the quality of life for millions of people globally. Diagnosing and treating CoBraD, however, is challenging due to their heterogeneity, comorbidity, and variability.

Clinical trials, the gold standard for evaluating the safety and efficacy of new interventions, present limitations that restrict their generalizability and applicability to real-world settings. For instance, clinical trials often enforce strict inclusion and exclusion criteria, which exclude many patients with CoBraD who may have multiple or rare conditions. Furthermore, the high costs and extended durations of clinical trials impede the development and adoption of novel therapies. Additionally, clinical trials may not account for the full range of outcomes and experiences that matter to CoBraD patients in their everyday lives.

Real-World Data (RWD) collected from sources beyond controlled clinical trials, such as electronic health records, wearable devices, mobile applications, patient registries, and social media, can provide valuable insights into the epidemiology, diagnosis, treatment patterns, outcomes, and costs of CoBraD across diverse populations. RWD can also capture patient preferences, values, and perspectives on their conditions.

Artificial Intelligence (AI), a branch of computer science focusing on creating systems capable of performing tasks requiring human intelligence, can facilitate the analysis and integration of large-scale, complex, and heterogeneous RWD from various sources. Advanced techniques such as machine learning, natural language processing, and computer vision enable AI to generate Real-World Evidence (RWE), which is evidence derived from RWD using rigorous methodologies. RWE can supplement and augment clinical trial evidence by addressing some limitations.

The MES-CoBraD Horizon 2020 project, an interdisciplinary initiative, seeks to develop a Multidisciplinary Expert System for the Assessment & Management of CoBraD based on RWD and AI. The project collects RWD from numerous clinical and consumer sources throughout Europe using comprehensive, cost-efficient, and rapid protocols. It also applies AI methods to analyse and integrate RWD into a unified framework, supporting diagnosis, prognosis, treatment selection, monitoring, and evaluation for CoBraD. The project will validate and compare its approach with existing clinical guidelines and standards using real-world scenarios involving CoBraD patients.

The MES-CoBraD project has the potential to enhance diagnostic accuracy and therapeutic outcomes for CoBraD patients by utilizing RWD and AI in medical research. The project will also contribute to advancing scientific knowledge and innovation within the CoBraD field. Moreover, the project will promote collaboration and communication among various stakeholders involved in CoBraD care, including clinicians, researchers, patients, caregivers, policy makers, and industry partners.