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Recently, the role of AI in the development of eHealth is becoming increasingly ambitious since AI is allowing the development of whole new healthcare areas. In many cases, AI offers the possibility to support patient screening and monitoring through low-cost, non-invasive tests. One of the most relevant sectors in which a great contribution from AI is expected is that of neurodegenerative diseases, which represent one of the most important pathologies in Western countries with very serious follow up not only clinical, but also social and economic.

1. To understand the critical role that proper patient preparation plays in image-guided percutaneous spine or rib biopsy 2. To optimize patient screening practices 3. To improve patient safety by the appropriate application of key patient safety initiatives such as “time-out,” hand hygiene, and patient education

Within developing countries, there is a realistic need for technologies that can assist medical practitioners in meeting the increasing demand for patient screening and monitoring. To this end, computer aided diagnosis (CAD) based approaches to chest radiograph screening can be utilised in areas where there is a high burden of diseases such as tuberculosis and pneumonia. In this work, we investigate the efficacy of a purely data-driven approach to chest radiograph classification through the use of fine-tuned convolutional neural networks (CNN). We use two popular CNN models that are pre-trained on a large natural image dataset and two distinct datasets containing paediatric and adult radiographs respectively. Evaluation is performed using a 5-fold cross-validation analysis at an image level. The promising results, with top AUC metrics of 0.87 and 0.84 for the respective datasets, along with several characteristics of our data-driven approach motivate for the use of fine-tuned CNN models within this application of CAD.

Falls are common throughout a life span; however, they become particularly dangerous as people age. Serious injuries, such as hip fractures, traumatic brain injuries, and death, frequently occur to older adults as a result of falls. As such, the importance of screening, recognizing, and managing risk factors for falls is paramount for clinicians and their patients. This chapter delves into the evidence-based fall evaluation recommendations and provides insights on modifiable risk factors that may be overlooked. Although the busy clinician may find it difficult to add yet another item on the to-do list of patient screening and prevention, a simple fall evaluation once a year, directed toward the at-risk population, can result in large patient benefits.