Akademik Veri Yönetim Sistemi
Embedded Systems for Biomedical Applications, Malviya,Karakus,roy, Editör, Chapman & Hall/Crc Press, London, ss.190-210, 2025
The utilization of artificial intelligence in the analysis of biomedical data has garnered significant interest among researchers globally, as this technology can aid healthcare professionals in diverse decision-making scenarios. Moreover, as data assumes growing significance across various domains, encompassing the biomedical healthcare sector, substantial volumes of biomedical data have become accessible to data scientists. As a result, effective artificial intelligence models that cater to a wide range of applications have been developed. The applications listed above include a wide range of diagnostic and disease-detection duties. They include, but are not limited to, cancer prognosis, monitoring, and treatment; anatomical and physiological investigations; molecular imaging; drug development; clinical trials; and biomedical research. The ongoing expansion of knowledge regarding artificial intelligence methods has the capacity to significantly enhance the impact of biomedical breakthroughs on healthcare. This chapter, within this framework, offers a thorough review of the most recent academic literature, presenting examples of artificial intelligence-driven embedded systems used in biomedical settings utilized in biomedical contexts. In conclusion, while machine learning-based artificial intelligence models will continue to be useful in many applications, deep learning models are predicted to gain even more traction in the years to come, mainly because large amounts of biomedical data will become available. This section aims to shed light on the efforts of researchers working in this dynamic field by explaining the importance of artificial intelligence in the field of embedded systems for biomedical applications