Related papers: Trust, Professional Vision and Diagnostic Work
The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate amongst its proponents, detractors, and skeptics. While the practices draw on a common set of tools,…
Large language models (LLMs) have shown considerable potential in supporting medical diagnosis. However, their effective integration into clinical workflows is hindered by physicians' difficulties in perceiving and trusting LLM…
Health informatics can inform decisions that practitioners, patients, policymakers, and researchers need to make about health and disease. Health informatics is built upon patient health data leading to the need to codify patient health…
A substantial administrative burden is placed on healthcare professionals as they manage and progress through their careers. Identity verification, pre-employment screening and appraisals: the bureaucracy associated with each of these…
In this paper, we focus on an emerging strand of IT-oriented research, namely Human-Data Interaction (HDI) and how this can be applied to healthcare. HDI regards both how humans create and use data by means of interactive systems, which can…
Early diagnosis of diseases holds the potential for deep transformation in healthcare by enabling better treatment options, improving long-term survival and quality of life, and reducing overall cost. With the advent of medical big data,…
This article analyzes trust and security in computing and communications systems. While in human-life, trust usually has some kind of commonly understood meaning, in the realm of computing and communications systems, it could be interpreted…
Without trust, patients may hesitate to engage with AI systems, significantly limiting the technology's potential in mental healthcare. This paper focuses specifically on the trust that mental health patients, as direct users, must have in…
The ever-rising complexity of computer systems presents challenges for maintaining security and trust throughout their lifetime. As hardware forms the foundation of a secure system, we need tools and techniques that support computer…
Artificial Intelligence began as a field probing some of the most fundamental questions of science - the nature of intelligence and the design of intelligent artifacts. But it has grown into a discipline that is deeply entwined with…
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score. We show that this score has statistically significant racial associations, and that by modeling trust directly we find stronger…
The synchronic and diachronic study of the evolution of Artificial Intelligence (AI) unveils one prominent fact that its effect can be traced in almost all fields such as healthcare industry. The growth is perceived holistically in…
The abundance of data has transformed the world in every aspect. It has become the core element in decision making, problem solving, and innovation in almost all areas of life, including business, science, healthcare, education, and many…
In this paper the current status and open challenges of synthetic speech detection are addressed. The work comprises an initial analysis of available open datasets and of existing detection methods, a description of the requirements for new…
This paper presents an argument for why we are not measuring trust sufficiently in explainability, interpretability, and transparency research. Most studies ask participants to complete a trust scale to rate their trust of a model that has…
This chapter discusses the existing and future use of robotics and intelligent sensing technology in mental health care. While the use of this technology is nascent in mental health care, it represents a potentially useful tool in the…
Modern science increasingly relies on ever-growing observational datasets and automated inference pipelines, under the implicit belief that accumulating more data makes scientific conclusions more reliable. Here we show that this belief can…
Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In medicine, trust must be engineered as a measurable system property grounded in evidence,…
Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a…
Computer methods in medical devices are frequently imperfect and are known to produce errors in clinical or diagnostic tasks. However, when deep learning and data-based approaches yield output that exhibit errors, the devices are frequently…