Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle highly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on user preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. This special issue comes after the successful organization of the 3rd IFIP Conference on Artificial Intelligence Applications & Innovations-AIAI 2006 (http://www.icsd.aegean.gr/aiai2006/), which was held from 7th till 9th of June 2006, in Athens, Greece.The general focus of the AIAI conferences and consequently the aim of this special issue is to provide insights on how AI can be implemented in real world applications. During the conference, papers describing advanced prototypes, systems, tools and techniques and general survey papers indicating future directions were presented. In this special issue we have tried to include extended in-depth analysis of the best work announced in the AIAI 2006 conference, presenting novel computational analysis techniques, methods, practices and systems, also taking into account the target I. Maglogiannis ( ) audience of the Applied Intelligence journal. This special issue, while of course not comprehensive in addressing all the possible fields of Emerging Artificial Intelligence Applications, is indicative of the explosive nature of interdisciplinary research going on in this area. As a result the special issue includes papers from several thematic areas, such as artificial intelligence in medicine [1, 2], pervasive and ubiquitous computing [3,4], emerging multimodal interfaces [5] and personalized human computer interaction [6].As already mentioned, one major area of accelerated growth is the field of computational analysis and artificial intelligence in medicine where a lot of research is done involving the development of diagnostic tools, designed to support the work of medical professionals. Kyriacou et al. in [1] investigate the use of several multilevel binary and gray scale morphological analysis algorithms in the assessment of atherosclerotic carotid plaques. This is carried out by analyzing ultrasound images recorded from asymptomatic and symptomatic plaques (Stroke, transient Ischaemic Attack (TIA), Amaurosis Fugax (AF)). The authors are developing the clinical motivation behind their approach. The use of multilevel morphological analysis into Low-images, Medium-images and High-images is motivated in terms of the clinically established hypoechoic, isoechoic and hyperechoic classification. Normalized pattern spectra were computed for both a structural, multilevel binary morphological model, and a direct gray scale morphology model. The derived pattern spectra were used as classification features with two different classifiers, the Probabilistic...