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© 2024 Roland Roller | Supriyo Chatterjea | Holmer Hemsen | Dimitrios Vogiatzis | Ricard Martínez Martínez | Langs Georg | Simona Rabinovici-Cohen | Wiebke Duettmann | Alex Sangers | Maria-Esther Vidal | Ernestina Menasalvas | Martin Sanchez Marga | Josep Redon | Ana Ferrer-Albero
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 that 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 BigData technologies, we present technical challenges and lessons learned 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 if applicable.