A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion
In the last few years, the trend in health care of embracing artificial intelligence (AI) has
dramatically changed the medical landscape. Medical centres have adopted AI applications …
dramatically changed the medical landscape. Medical centres have adopted AI applications …
Explainability and causability in digital pathology
The current move towards digital pathology enables pathologists to use artificial intelligence
(AI)‐based computer programmes for the advanced analysis of whole slide images …
(AI)‐based computer programmes for the advanced analysis of whole slide images …
[HTML][HTML] AI for life: Trends in artificial intelligence for biotechnology
Due to popular successes (eg, ChatGPT) Artificial Intelligence (AI) is on everyone's lips
today. When advances in biotechnology are combined with advances in AI unprecedented …
today. When advances in biotechnology are combined with advances in AI unprecedented …
Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade
Background Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the
practice of pathology. However, clinical integration remains challenging, with no AI …
practice of pathology. However, clinical integration remains challenging, with no AI …
Modeling adoption of intelligent agents in medical imaging
Artificial intelligence has the potential to transform many application domains fundamentally.
One notable example is clinical radiology. A growing number of decision-making support …
One notable example is clinical radiology. A growing number of decision-making support …
[HTML][HTML] Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making
This paper contributes with a pragmatic evaluation framework for explainable Machine
Learning (ML) models for clinical decision support. The study revealed a more nuanced role …
Learning (ML) models for clinical decision support. The study revealed a more nuanced role …
Explainability pitfalls: Beyond dark patterns in explainable AI
To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful
effects is important. In this paper, we address an important yet unarticulated type of negative …
effects is important. In this paper, we address an important yet unarticulated type of negative …
[HTML][HTML] Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach
The purpose of this paper is to propose a novel hybrid framework for evaluating and
benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi …
benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi …
[HTML][HTML] Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
The limited ability of Convolutional Neural Networks to generalize to images from previously
unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as …
unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as …
[HTML][HTML] CLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks
JM Metsch, A Saranti, A Angerschmid, B Pfeifer… - Journal of Biomedical …, 2024 - Elsevier
Background: Lack of trust in artificial intelligence (AI) models in medicine is still the key
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …