Algorithmic fairness in artificial intelligence for medicine and healthcare

RJ Chen, JJ Wang, DFK Williamson, TY Chen… - Nature biomedical …, 2023‏ - nature.com
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 …

Artificial intelligence for digital and computational pathology

AH Song, G Jaume, DFK Williamson, MY Lu… - Nature Reviews …, 2023‏ - nature.com
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 …

Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

A Shmatko, N Ghaffari Laleh, M Gerstung, JN Kather - Nature cancer, 2022‏ - nature.com
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
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

S Rani, A Kataria, S Kumar, P Tiwari - Knowledge-based systems, 2023‏ - Elsevier
Recent developments in the Internet of Things (IoT) and various communication
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

J Ogier du Terrail, A Leopold, C Joly, C Béguier… - Nature medicine, 2023‏ - nature.com
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 …

Federated learning for smart healthcare: A survey

DC Nguyen, QV Pham, PN Pathirana, M Ding… - ACM Computing …, 2022‏ - dl.acm.org
Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

Federated learning enables big data for rare cancer boundary detection

S Pati, U Baid, B Edwards, M Sheller, SH Wang… - Nature …, 2022‏ - nature.com
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 …

Federated learning for medical image analysis: A survey

H Guan, PT Yap, A Bozoki, M Liu - Pattern Recognition, 2024‏ - Elsevier
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 …

Federated learning and differential privacy for medical image analysis

M Adnan, S Kalra, JC Cresswell, GW Taylor… - Scientific reports, 2022‏ - nature.com
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 …

End-to-end privacy preserving deep learning on multi-institutional medical imaging

G Kaissis, A Ziller, J Passerat-Palmbach… - Nature Machine …, 2021‏ - nature.com
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 …