Roller, Roland, Chatterjea, Supriyo, Pickering, Brian, Hemsen, Holmer, Vogiatzis, Dimitrios, Martinez Martinez, Ricard, Langs, Georg, Rabinovici-Cohen, Simona, Duettmann, Wiebke, Sangers, Alex, Vidal, Maria-Esther, Menasalvas Ruiz, Ernestina, Sanchez, Marga Martin, Redon, Josep, Ferrer-Albero, Ana, Muñoz-Oliver, Alexandra, Noordergraaf, Gerrit-Jan, Paulussen, Igor, Vincent, Per Henrik, IJpma, Arne, Navarro-Cerdán, José-Ramón and Gálvez-Settier, Santiago (2023) General Learnings from the Horizon 2020 Project BigMedilytics. In, Handbook on Big Data for Healthcare - from theory to practice. (In Press)
Abstract
Big Data, in combination with Artificial Intelligence (AI), has the potential to change and improve processes in medicine. However, these activities/technologies must be developed to promote the trust of all stakeholders: patients, healthcare professionals, private and public providers and businesses. Providing a Trustworthy AI - lawful, ethical and robust - requires significant efforts. Although technological development is moving quickly, testing, validation and integration of such innovation may take many years. The reasons which slow down this process are manifold. However, some barriers and pitfalls are foreseeable and, therefore, can be taken into account or avoided. In order to support future development and integration of AI and Big Data technologies, we present technical challenges and lessons learnt from our previous project, BigMedilytics, involving clinicians and data scientists. This chapter considers the challenges data scientists providing advanced technology in the healthcare domain may face, along with some suggestions to address any related issues.f applicable
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