[HTML][HTML] Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …
respect to the quantity of high-performing solutions reported in the literature. End users are …
Artificial intelligence in thyroidology: a narrative review of the current applications, associated challenges, and future directions
Background: The use of artificial intelligence (AI) in health care has grown exponentially with
the promise of facilitating biomedical research and enhancing diagnosis, treatment …
the promise of facilitating biomedical research and enhancing diagnosis, treatment …
[HTML][HTML] Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosis
In this paper, we study human–AI collaboration protocols, a design-oriented construct aimed
at establishing and evaluating how humans and AI can collaborate in cognitive tasks. We …
at establishing and evaluating how humans and AI can collaborate in cognitive tasks. We …
Explainable machine-learning models for COVID-19 prognosis prediction using clinical, laboratory and radiomic features
The SARS-CoV-2 virus pandemic had devastating effects on various aspects of life: clinical
cases, ranging from mild to severe, can lead to lung failure and to death. Due to the high …
cases, ranging from mild to severe, can lead to lung failure and to death. Due to the high …
[HTML][HTML] Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions
K Ofosu-Ampong - Telematics and Informatics Reports, 2024 - Elsevier
This article presents an analysis of artificial intelligence (AI) in information systems and
innovation-related journals to determine the current issues and stock of knowledge in AI …
innovation-related journals to determine the current issues and stock of knowledge in AI …
[HTML][HTML] The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board
Objective The digital revolution in pathology represents an invaluable resource fto optimise
costs, reduce the risk of error and improve patient care, even though it is still adopted in a …
costs, reduce the risk of error and improve patient care, even though it is still adopted in a …
[HTML][HTML] Why did AI get this one wrong?—Tree-based explanations of machine learning model predictions
Increasingly complex learning methods such as boosting, bagging and deep learning have
made ML models more accurate, but harder to interpret and explain, culminating in black …
made ML models more accurate, but harder to interpret and explain, culminating in black …
[HTML][HTML] Breast cancer classification through multivariate radiomic time series analysis in DCE-MRI sequences
Breast cancer is the most prevalent disease that poses a significant threat to women's
health. Despite the Dynamic Contrast-Enhanced MRI (DCE-MRI) has been widely used for …
health. Despite the Dynamic Contrast-Enhanced MRI (DCE-MRI) has been widely used for …
Xai transformer based approach for interpreting depressed and suicidal user behavior on online social networks
Online social networks can be used for mental healthcare monitoring using Artificial
Intelligence and Machine Learning techniques for detecting various mental health disorders …
Intelligence and Machine Learning techniques for detecting various mental health disorders …
Recommendations for using artificial intelligence in clinical flow cytometry
Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and
traditionally requires close inspection of digital data by hematopathologists with expert …
traditionally requires close inspection of digital data by hematopathologists with expert …