The Innovative Medicines Initiative (IMI) 2 sponsored public-private research initiative OPTIMA (Optimal Treatment for Patients with Solid Tumours in Europe Through Artificial intelligence) aims to employ artificial intelligence (AI) to enhance patient care for prostate, breast, and lung cancer. Creating the first interoperable, GDPR-compliant real-world data (RWD) and guidelines-driven evidence-generation platform in Europe was OPTIMA’s main objective.
The interdisciplinary collaboration of OPTIMA is concentrated on: establishing a safe, substantial, GDPR-compliant data platform with RWD from more than 200 million individuals. The federated learning tools, datasets, data analysis tools, and AI algorithms will all be hosted under the interoperable platform; creating clinical decision support toolkits that are based on global and national treatment recommendations and supported by data from cutting-edge AI models; using massive RWD to drive knowledge production, especially when the present evidence supporting clinical recommendations is insufficient or unreliable.
In the disciplines of clinical, academic, patient, regulatory, data sciences, legal, ethical, and pharmaceutical, the interdisciplinary pan-European cooperation comprised 36 partners from 13 different countries. It has been given €21.3 million in IMI financing, a strong project management methodology, a Scientific Governance Board, and three independent advisory boards—Multistakeholder, Ethical/Legal; and Public/Private—are in place to offer prompt, objective guidance from subject matter experts. With plans to grow, OPTIMA has obtained access to EHR and European registries encompass millions of data sets. The collaboration will also provide central and federated models for data sources to share information.
With the goal of integrating RWD into clinical practice, OPTIMA was a cutting-edge interdisciplinary public-private collaboration that is focused on developing methods that generate healthcare evidence that were guided by clinical guidelines. The tools and algorithms created aim to analyze high-dimensional data to uncover elements that assist personalized treatment choices for cancer patients.
Reference: annalsofoncology.org/article/S0923-7534(22)02893-9/fulltext
