Cultural Competence

Humans inherit artificial intelligence biases.

Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypothesized that people who perform a (simulated) medical diagnostic task assisted by a biased AI system will reproduce the model's bias in their own decisions, even when they move to a context without AI support. In three experiments, participants completed a medical-themed classification task with or...

Machine Learning: A New Approach for Dose Individualization.

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different dr...

New deep learning-based methods for visualizing ecosystem properties using environmental DNA metabarcoding data.

Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The ...

AI pitfalls and what not to do: mitigating bias in AI.

Various forms of artificial intelligence (AI) applications are being deployed and used in many healthcare systems. As the use of these applications in...

Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model.

Deep neural networks have become increasingly significant in our daily lives due to their remarkable performance. The issue of adversarial examples, w...

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data.

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack o...

Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology.

In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go...

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T.

At ultrahigh field strengths images of the body are hampered by B -field inhomogeneities. These present themselves as inhomogeneous signal intensity a...

Advancing AI in healthcare: A comprehensive review of best practices.

Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools shaping the healthcare sector. This review considers twelve key aspects of A...

Ethical Considerations of Using ChatGPT in Health Care.

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents pot...

Spatial oblivion channel attention targeting intra-class diversity feature learning.

Convolutional neural networks (CNNs) have successfully driven many visual recognition tasks including image classification. However, when dealing with...

Revolutionising Impacts of Artificial Intelligence on Health Care System and Its Related Medical In-Transparencies.

The application of artificial intelligence (AI) in the field of medicine has revolutionised various sectors of the health care system, including robot...

Bias in artificial intelligence in vascular surgery.

Application of artificial intelligence (AI) has revolutionized the utilization of big data, especially in patient care. The potential of deep learning...

Hierarchical Bias Mitigation for Semi-Supervised Medical Image Classification.

Semi-supervised learning (SSL) has demonstrated remarkable advances on medical image classification, by harvesting beneficial knowledge from abundant ...

[Big hype about ChapGPT in medicine : Is it something for rhythmologists? What must be taken into consideration?].

ChatGPT, a chatbot based on a large language model, is currently attracting much attention. Modern machine learning (ML) architectures enable the prog...

"Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation.

Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with di...

SAF: Stakeholders' Agreement on Fairness in the Practice of Machine Learning Development.

This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical ...

Intrinsic neural diversity quenches the dynamic volatility of neural networks.

Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, ex...

Predictive Uncertainty Estimation for Camouflaged Object Detection.

Uncertainty is inherent in machine learning methods, especially those for camouflaged object detection aiming to finely segment the objects concealed ...

Identifying, Measuring, and Ranking Social Determinants of Health for Health Promotion Interventions Targeting Informal Settlement Residents.

OBJECTIVES: Considering the importance of social determinants of health (SDHs) in promoting the health of residents of informal settlements and their ...