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Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
[HTML][HTML] Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer
Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic
potential and poor prognosis, and has limited treatment options. The current standard of …
potential and poor prognosis, and has limited treatment options. The current standard of …
Federated learning for smart healthcare: A survey
Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …
Federated learning enables big data for rare cancer boundary detection
Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
Federated learning for medical image analysis: A survey
Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …
the small sample size problem. Many recent studies suggest using multi-domain data …
Federated learning and differential privacy for medical image analysis
The artificial intelligence revolution has been spurred forward by the availability of large-
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Using large, multi-national datasets for high-performance medical imaging AI systems
requires innovation in privacy-preserving machine learning so models can train on sensitive …
requires innovation in privacy-preserving machine learning so models can train on sensitive …