相关论文: Contrasting Attitudes Towards Current and Future A…
Explainable AI (XAI) holds the promise of advancing the implementation and adoption of AI-based tools in practice, especially in high-stakes environments like healthcare. However, most of the current research lacks input from end users, and…
There has been a surge in the number and type of AI tools being tested and deployed within both national and local government in the UK, including within the social care sector. Given the many ongoing and planned future developments, the…
Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need…
There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human-interpretable. This complexity raises concerns…
Artificial Intelligence (AI) has demonstrated potential in healthcare, particularly in enhancing diagnostic accuracy and decision-making through Clinical Decision Support Systems (CDSSs). However, the successful implementation of these…
Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how machines "see" and "understand" language, are now fueling…
Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier. We developed a novel decision support interface that provides interpretable treatment…
In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and…
Artificial intelligence (AI)-based computer perception (CP) technologies use mobile sensors to collect behavioral and physiological data for clinical decision-making. These tools can reshape how clinical knowledge is generated and…
Advances in machine learning have created new opportunities to develop artificial intelligence (AI)-based clinical decision support systems using past clinical data and improve diagnosis decisions in life-threatening illnesses such breast…
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI and its promise to render AI devices more…
The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. This study aimed to explore psychiatrists' opinions about the potential impact of innovations in artificial…
In recent years, AI systems in the medical domain have advanced significantly. However, despite outperforming humans, they are rarely used in practice since it is often not clear how they make their decisions. Optimal explanation and…
Artificial Intelligence (AI) is poised to transform healthcare delivery through revolutionary advances in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered…
AI applications are increasingly being introduced into digital health. While technical performance has advanced rapidly, successful deployment mainly depends on consumer attitudes, especially to patient-facing applications. However, most…
Aim: provide a methodological framework for the process of clinical tests, clinical acceptance, and scientific assessment of algorithms and software based on the artificial intelligence (AI) technologies. Clinical tests are considered as a…
Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexity of AI models, however, concerns regarding their opacity,…
Explainable AI (XAI) techniques are necessary to help clinicians make sense of AI predictions and integrate predictions into their decision-making workflow. In this work, we conduct a survey study to understand clinician preference among…
The integration of artificial intelligence (AI) into medicine is remarkable, offering advanced diagnostic and therapeutic possibilities. However, the inherent opacity of complex AI models presents significant challenges to their clinical…
Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner. Especially in clinical decision-making, it has the potential to improve treatment quality by…