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 …
A manifesto on explainability for artificial intelligence in medicine
The rapid increase of interest in, and use of, artificial intelligence (AI) in computer
applications has raised a parallel concern about its ability (or lack thereof) to provide …
applications has raised a parallel concern about its ability (or lack thereof) to provide …
Artificial intelligence and machine learning applications in forest management and biodiversity conservation
A Raihan - Natural Resources Conservation and …, 2023 - systems.enpress-publisher.com
The recent progress in data science, along with the transformation in digital and satellite
technology, has enhanced the capacity for artificial intelligence (AI) applications in the …
technology, has enhanced the capacity for artificial intelligence (AI) applications in the …
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Abstract Machine learning is increasingly applied to Earth Observation (EO) data to obtain
datasets that contribute towards international accords. However, these datasets contain …
datasets that contribute towards international accords. However, these datasets contain …
[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] 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 …
Machine learning for prediction of adverse cardiovascular events in adults with repaired tetralogy of fallot using clinical and cardiovascular magnetic resonance …
A Ishikita, C McIntosh, K Hanneman… - Circulation …, 2023 - Am Heart Assoc
Background: Existing models for prediction of major adverse cardiovascular events (MACE)
after repair of tetralogy of Fallot have been limited by modest predictive capacity and limited …
after repair of tetralogy of Fallot have been limited by modest predictive capacity and limited …
Reliable anti-cancer drug sensitivity prediction and prioritization
The application of machine learning (ML) to solve real-world problems does not only bear
great potential but also high risk. One fundamental challenge in risk mitigation is to ensure …
great potential but also high risk. One fundamental challenge in risk mitigation is to ensure …
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks
associated with their use has become one of the most urgent scientific and societal issues …
associated with their use has become one of the most urgent scientific and societal issues …
A Retrospective cohort study: predicting 90-day mortality for ICU trauma patients with a machine learning algorithm using XGBoost using MIMIC-III database
S Yang, L Cao, Y Zhou, C Hu - Journal of Multidisciplinary …, 2023 - Taylor & Francis
Objective The aim of this study was to develop and validate a machine learning-based
predictive model that predicts 90-day mortality in ICU trauma patients. Methods Data of …
predictive model that predicts 90-day mortality in ICU trauma patients. Methods Data of …