Martinez-Villaseñor, LourdesLourdesMartinez-VillaseñorPonce, HiramHiramPonce2024-05-092024-05-092024-01-01Martínez-Villaseñor, L., Ponce, H. (2024). Ethical Design Framework for Artificial Intelligence Healthcare Technologies. In: Ponce, H., Brieva, J., Lozada-Flores, O., Martínez-Villaseñor, L., Moya-Albor, E. (eds) Data-Driven Innovation for Intelligent Technology. Studies in Big Data, vol 148. Springer, Cham. https://doi.org/10.1007/978-3-031-54277-0_997830315427639783031542770https://scripta.up.edu.mx/handle/20.500.12552/1039410.1007/978-3-031-54277-0_92-s2.0-85191762836The healthcare industry has undergone a profound transformation with the integration of artificial intelligence(AI) and emerging technologies, leading in a new era of personalized treatments and healthcare solutions. However, this technological advancement has not been without its ethical and practical challenges, which have hindered the real-world application of intelligent systems in clinical settings. Numerous international entities have published principles, guidelines, and regulations to tackle these issues, yet a significant gap persists between theoretical initiatives and the practical incorporation of ethical design in intelligent systems. Within this study, we delineate the substantial transformation taking place in the healthcare landscape due to artificial intelligence. We provide a condensed overview of the opportunities and challenges that accompany this disruptive shift. The main goal of this work is introducing an ethical design framework tailored to healthcare technologies and delineating the ethical design process for a machine learning-based support tool designed for Age-related Macular Degeneration risk assessment. To the best of our knowledge, this work represents one of the few documented instances of the practical implementation of ethical design principles in intelligent healthcare systems. © 2024 Springer NatureenAcceso RestringidoData Science for IndustryArtificial Intelligence for IndustryTechnology TrendsMachine Learning for IndustryMachine Learning ApplicationsData Science in Latin AmericaApplied Artificial IntelligenceData-driven InnovationResource Types::text::book::book part