Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Stress neuropeptide levels in adults with chest pain due to coronary artery disease: potential implications for clinical assessment

: Substance P (SP) and neuropeptide Y (NPY) are neuropeptides involved in nociception. The study of biochemical markers of pain in communicating critically ill coronary patients may provide insight for pain assessment and management in critical care. Purpose of the study was to to explore potential associations between plasma neuropeptide levels and reported pain intensity in coronary critical care adults, in order to test the reliability of SP measurements for objective pain assessment in critical care