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 multimodal data integration in oncology

J Lipkova, RJ Chen, B Chen, MY Lu, M Barbieri… - Cancer cell, 2022 - cell.com
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

CA Gao, FM Howard, NS Markov, EC Dyer… - NPJ Digital …, 2023 - nature.com
Large language models such as ChatGPT can produce increasingly realistic text, with
unknown information on the accuracy and integrity of using these models in scientific writing …

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human …

CA Gao, FM Howard, NS Markov, EC Dyer, S Ramesh… - BioRxiv, 2022 - biorxiv.org
Background Large language models such as ChatGPT can produce increasingly realistic
text, with unknown information on the accuracy and integrity of using these models in …

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 …

Towards a general-purpose foundation model for computational pathology

RJ Chen, T Ding, MY Lu, DFK Williamson, G Jaume… - Nature Medicine, 2024 - nature.com
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

Addressing fairness in artificial intelligence for medical imaging

MA Ricci Lara, R Echeveste, E Ferrante - nature communications, 2022 - nature.com
A plethora of work has shown that AI systems can systematically and unfairly be biased
against certain populations in multiple scenarios. The field of medical imaging, where AI …

Swarm learning for decentralized artificial intelligence in cancer histopathology

OL Saldanha, P Quirke, NP West, JA James… - Nature medicine, 2022 - nature.com
Artificial intelligence (AI) can predict the presence of molecular alterations directly from
routine histopathology slides. However, training robust AI systems requires large datasets …

Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings

J Ogier du Terrail, SS Ayed, E Cyffers… - Advances in …, 2022 - proceedings.neurips.cc
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …

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 …