The healthcare sector has been confronted with rapidly rising health care costs and a shortage of medical staff. At the same time, the field of Artificial Intelligence (AI) has emerged as a promising area of research, offering poten tial benefits for healthcare. Despite the potential of AI to support …

Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation …

This research reviews the current literature on the impact of Artificial Intelligence (AI) in the operation of autonomous Unmanned Aerial Vehicles (UAVs). This paper examines three key aspects in developing the future of Unmanned Aircraft Systems (UAS) and UAV operations: (i) design, (ii) human factors, …

This study addresses the burgeoning global shortage of healthcare workers and the consequential overburdening of medical professionals, a challenge that is anticipated to intensify by 2030 [1]. It explores the adoption and perceptions of AI-powered mobile medical applications (MMAs) by physicians in …

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential …

From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, …

The profiles from data-driven profiling applications are a model of the reality. The interpretability of these profiles for end users, e.g. policymakers, is often far from trivial. How and why these models are obtained by the applications are often regarded as a black box. In recent years several profiling …

Artificially intelligent agents increasingly collaborate with humans in human-agent teams. Timely proactive sharing of relevant information within the team contributes to the overall team performance. This paper presents a machine learning approach to proactive communication in AI-agents using contextual …

In machine learning, people stay responsible for defining both the available data, and the desired outcomes. The growth of knowledge and insights remains both critical and constantly dependent on developments, and therefore on professionals. Although the quality of our work can increase through the use …