Abstract
Background:Amyotrophic lateral sclerosis (ALS) is a fatal progressive motor neuron disease. People with ALS demonstrate various speech problems.Summary:We aim to provide an overview of studies concerning the diagnosis of ALS based on the analysis of voice samples. The main focus is on the feasibility of the use of voice and speech assessment as an effective method to diagnose the disease, either in clinical or pre-clinical conditions, and to monitor the disease progression. Specifically, we aim to examine current knowledge on: (a) voice parameters and the data models that can, most effectively, provide robust results; (b) the feasibility of a semi-automatic or automatic diagnosis and outcomes; and (c) the factors that can improve or restrict the use of such systems in a real-world context.Key Messages:The studies already carried out on the possibility of diagnosis of ALS using the voice signal are still sparse but all point to the importance, feasibility and simplicity of this approach. Most cohorts are small which limits the statistical relevance and makes it difficult to infer broader conclusions. The set of features used, although diverse, is quite circumscribed. ALS is difficult to diagnose early because it may mimic several other neurological diseases. Promising results were found for the automatic detection of ALS from speech samples and this can be a feasible process even in pre-symptomatic stages. Improved guidelines must be set in order to establish a robust decision model.

Abstract
In a multimodal world the contact time between the teacher and the students is not always sufficient to ensure the effectiveness of the learning process. For the assimilation of concepts, students often endeavor on a search for the materials that best suit their learning needs. With the application of new technologies in teaching, study materials and support platforms are increasingly abundant and diverse. Additionally, recommendation algorithms overwhelm students with several options, sometimes hard to resist and select, especially after the COVID-19 restrictions, where the amount of connected time as increased. In this context, it is important for the teacher, to know which methods and materials the students use when they are autonomously developing their knowledge and skills. A survey was conducted within a group of engineering students at a Portuguese higher education institution with the main goal of characterizing the study habits and the materials that students. The obtained results are here reported and analyzed and compared with previous results from pre-pandemic study.

Abstract
To provide a safe and fair-value health service that ensures quality, hospitals must provide efficient processes, trained and committed personnel, appropriate technology and a strategic platform which integrates these aspects effectively. At present, a broad set of tools and methodologies are available, associated to the reconfiguration of processes for enhancing efficiency and enabling excellence and sustainability. Of these, the most noteworthy are Lean and Six-Sigma methodologies. A literature review was performed covering the implementation of these methodologies in health services over the last 5 years. The aim was to determine the current approach in this sector and propose guidelines aligned with the future challenges and the needs of healthcare managers. The influence of team management strategies in the final project outcomes has also been addressed representing a novelty.

Abstract
Background: Alzheimer's disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer's Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.

Abstract
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Abstract
Artificial Intelligence (AI) has evolved rapidly since its inception in the 1950s, from simple rule-based systems to today's advanced deep learning models. AI has impacted society in many ways, ranging from revolutionizing the way we live, work, and interact with technology, to creating new job opportunities, improving decision-making and automating tasks, and solving complex problems in fields like healthcare, finance, and transportation. However, it has also raised concerns about job displacement, privacy and security, and ethical considerations. The evolution of AI is ongoing, and it is expected to continue to shape and transform society in new and profound ways. The impact of AI in education has also been substantial, offering new and innovative ways to personalize learning, enhance educational resources, and improve educational outcomes. In this chapter we will cover the most important aspects related with the teaching-learning process, from a physiological perspective to the different strategies. © 2023, IGI Global. All rights reserved.