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.

Abstract
Background: Virtual reality (VR) has the potential to be a powerful tool in promoting empathy towards inclusion, particularly for individuals with impairments such as mobility difficulties, vision deficits, or autism but also about pregnancy, which can create temporary difficulties. By immersing users in simulated environments that replicate the experiences of those with different abilities, VR can create a sense of understanding and empathy for those who face challenges in their daily lives. For example, VR experiences can simulate the experience of navigating space as someone with a mobility impairment, providing a new perspective and appreciation for the difficulties that others face. Similarly, VR experiences can simulate the experience of vision impairment, pregnancy, or autism, providing a window into the challenges faced by those with these conditions and fostering empathy and understanding. Material and Methods: During the development of this study, field experts were consulted to ensure the robustness of the methods employed. Then, questionnaires were specifically developed to explore disabilities and challenges related to inclusion and were administered to a large population. Additionally, guided interviews were conducted with individuals who possess specific impairments to gather first-hand insights. Results: The results obtained from the questionnaires and interviews provide a comprehensive overview of the inclusion challenges that necessitate attention and resolution. By drawing on the expertise of both experts and individuals with lived experiences, a holistic landscape of inclusion challenges has been established. Conclusions: The VR emerges as a powerful tool for promoting inclusion and fostering understanding among individuals. Its capacity to create immersive experiences that facilitate empathy has the potential to reshape society into a more compassionate and empathetic one. By leveraging the unique capabilities of VR, we can bridge the gap between different perspectives, fostering greater understanding, acceptance, and inclusivity.

Abstract
The assessment of differences between skeletal age and chronological age in childhood is often based on the comparison of the patient's left hand x-ray with a reference atlas, performed by a experienced professional. This procedure involves a manual image analysis, that can be subject to inter rater variability posing several problems for clinical applications. In this paper a new methodology for skeleton maturation estimation based on automatic hand X-ray assessment for pediatric applications on a low resource devices (e.g. mobile device) is proposed. The pipeline covers hand-area estimation and bone-area estimation to achieve maturation scores which are then indexed with references images, separately for male and female. The proposed approach is based on simple image processing functions always bearing in mind the application on a mobile context. The involved steps are thoroughly presented and all the used functions are explained. The performance of the system was then evaluated using the complete pipeline. The obtained results pointed to an average error rate of 15,38 +/- 3,31%, which is subject to improvements. In particular, contrast enhancement in some lower quality images still offers some challenges.

Abstract
The outbreak of the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been a watershed moment in human history, causing a profound shift in the global landscape that has affected every aspect of our lives [...]

Abstract
Vine phenology modelling is increasingly important for winegrowers and viticulturists. Model calibration is often required before practical applications. However, when multiple models and optimization methods are applied for different varieties, it is rarely known which factor tends to mostly affect the calibration results. We mainly aim to investigate the main source of the variability in the modelling errors for the flowering timings of two important varieties of vine in the Douro Demarcated Region (DDR) of Portugal; this is based on five phenology model simulations that use optimal parameters and that are estimated by three optimization algorithms (MLE, SA and SCE-UA). Our results indicate that the main source of the variability in calibration can be affected by the initially assumed parameter boundary. Restricting the initial parameter distribution to a narrow range impedes the algorithm from exploring the full parameter space and searching for optimal parameters. This can lead to the largest variation in different models. At an identified appropriate boundary, the difference between the two varieties represents the largest source of uncertainty, while the choice of algorithm for calibration contributes least to the overall uncertainty. The smaller variability among different models or algorithms (tools for analysis) compared to between different varieties could indicate the overall reliability of the calibration. All optimization algorithms show similar results in terms of the obtained goodness-of-fit: the RMSE (MAE) is 5-6 (4-5) days with a negligible mean bias and moderately good R-2 (0.5-0.6) for the ensemble median predictor. Nevertheless, a similar predictive performance can result from differently estimated parameter values, due to the equifinality or multi-modal issue in which different parameter combinations give similar results. This mainly occurs for models with a non-linear structure compared to those with a near-linear one. Yet, the former models are found to outperform the latter ones in predicting the flowering timing of the two varieties in the DDR. Overall, our findings highlight the importance of carefully defining the initial parameter boundary and decomposing the total variance of prediction errors. This study is expected to bring new insights that will help to better inform users about the importance of choice when these factors are involved in calibration. Nonetheless, the importance of each factor can change depending on the specific situation. Details of how the optimization methods are applied and of the continuous model improvement are important.