Medperf: open benchmarking platform for medical artificial intelligence using federated evaluation
A Karargyris, R Umeton, MJ Sheller… - arXiv preprint arXiv …, 2021 - arxiv.org
Medical AI has tremendous potential to advance healthcare by supporting the evidence-
based practice of medicine, personalizing patient treatment, reducing costs, and improving …
based practice of medicine, personalizing patient treatment, reducing costs, and improving …
Federated benchmarking of medical artificial intelligence with MedPerf
A Karargyris, R Umeton, MJ Sheller… - Nature machine …, 2023 - nature.com
Medical artificial intelligence (AI) has tremendous potential to advance healthcare by
supporting and contributing to the evidence-based practice of medicine, personalizing …
supporting and contributing to the evidence-based practice of medicine, personalizing …
Post-processing fairness evaluation of federated models: An unsupervised approach in healthcare
I Siniosoglou, V Argyriou… - IEEE/ACM …, 2023 - ieeexplore.ieee.org
Modern Healthcare cyberphysical systems have begun to rely more and more on distributed
AI leveraging the power of Federated Learning (FL). Its ability to train Machine Learning …
AI leveraging the power of Federated Learning (FL). Its ability to train Machine Learning …
Improving fairness in ai models on electronic health records: The case for federated learning methods
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes
applications such as those in healthcare. However, health AI models' overall prediction …
applications such as those in healthcare. However, health AI models' overall prediction …
Unified fair federated learning for digital healthcare
Federated learning (FL) is a promising approach for healthcare institutions to train high-
quality medical models collaboratively while protecting sensitive data privacy. However, FL …
quality medical models collaboratively while protecting sensitive data privacy. However, FL …
Federated Random Forests can improve local performance of predictive models for various healthcare applications
Motivation Limited data access has hindered the field of precision medicine from exploring
its full potential, eg concerning machine learning and privacy and data protection rules. Our …
its full potential, eg concerning machine learning and privacy and data protection rules. Our …
Privacy preservation for federated learning in health care
Artificial intelligence (AI) shows potential to improve health care by leveraging data to build
models that can inform clinical workflows. However, access to large quantities of diverse …
models that can inform clinical workflows. However, access to large quantities of diverse …
Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
Developing robust artificial intelligence (AI) models that generalize well to unseen datasets
is challenging and usually requires large and variable datasets, preferably from multiple …
is challenging and usually requires large and variable datasets, preferably from multiple …
Contribution-aware federated learning for smart healthcare
Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due
to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to …
to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to …