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 …

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 …

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 …

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 (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

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 …

Artificial intelligence (AI) can predict the presence of molecular alterations directly from
routine histopathology slides. However, training robust AI systems requires large datasets …

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 …

Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of
metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative …