[HTML][HTML] A review of Explainable Artificial Intelligence in healthcare

Z Sadeghi, R Alizadehsani, MA Cifci, S Kausar… - Computers and …, 2024 - Elsevier
Abstract Explainable Artificial Intelligence (XAI) encompasses the strategies and
methodologies used in constructing AI systems that enable end-users to comprehend and …

[HTML][HTML] A review on brain tumor segmentation based on deep learning methods with federated learning techniques

MF Ahamed, MM Hossain, M Nahiduzzaman… - … Medical Imaging and …, 2023 - Elsevier
Brain tumors have become a severe medical complication in recent years due to their high
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …

Interpretability research of deep learning: A literature survey

B Xua, G Yang - Information Fusion, 2024 - Elsevier
Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …

Artificial intelligence and explanation: How, why, and when to explain black boxes

E Marcus, J Teuwen - European Journal of Radiology, 2024 - Elsevier
Artificial intelligence (AI) is infiltrating nearly all fields of science by storm. One notorious
property that AI algorithms bring is their so-called black box character. In particular, they are …

[HTML][HTML] A sco** review of interpretability and explainability concerning artificial intelligence methods in medical imaging

M Champendal, H Müller, JO Prior… - European journal of …, 2023 - Elsevier
Abstract Purpose To review eXplainable Artificial Intelligence/(XAI) methods available for
medical imaging/(MI). Method A sco** review was conducted following the Joanna Briggs …

Explainable artificial intelligence: importance, use domains, stages, output shapes, and challenges

N Ullah, JA Khan, I De Falco, G Sannino - ACM Computing Surveys, 2024 - dl.acm.org
There is an urgent need in many application areas for eXplainable ArtificiaI Intelligence
(XAI) approaches to boost people's confidence and trust in Artificial Intelligence methods …

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

B Koçak, A Ponsiglione, A Stanzione… - Diagnostic and …, 2024 - zora.uzh.ch
Although artificial intelligence (AI) methods hold promise for medical imaging-based
prediction tasks, their integration into medical practice may present a double-edged sword …

From admission to discharge: a systematic review of clinical natural language processing along the patient journey

K Klug, K Beckh, D Antweiler, N Chakraborty… - BMC Medical Informatics …, 2024 - Springer
Background Medical text, as part of an electronic health record, is an essential information
source in healthcare. Although natural language processing (NLP) techniques for medical …

Pitfalls in interpretive applications of artificial intelligence in radiology

S Behzad, SMH Tabatabaei, MY Lu… - American Journal of …, 2024 - ajronline.org
Interpretive artificial intelligence (AI) tools are poised to change the future of radiology.
However, certain pitfalls may pose particular challenges for optimal AI interpretative …

[HTML][HTML] An explainable artificial intelligence model proposed for the prediction of myalgic encephalomyelitis/chronic fatigue syndrome and the identification of …

FH Yagin, A Alkhateeb, A Raza, NA Samee… - Diagnostics, 2023 - mdpi.com
Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex
and debilitating illness with a significant global prevalence, affecting over 65 million …