Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a
machine learning algorithm without sharing their data. Organizations instead share model …

Quantum computing holds substantial potential for applications in biology and medicine,
spanning from the simulation of biomolecules to machine learning methods for subtyping …

Large-language models like ChatGPT have recently received a great deal of attention. One
area of interest pertains to how these models could be used in biomedical contexts …

Aim To develop a consensus paper on the central points of an international invitational think‐
tank on nursing and artificial intelligence (AI). Methods We established the Nursing and …

This paper investigates how Artificial Intelligence (AI) is changing the field of cancer
medicine. It is organized into nine major sections that illustrate the profound effects of AI on …

Emerging machine learning (ML) technologies have the potential to significantly improve the
research and treatment of rare diseases, which constitute a vast set of diseases that affect a …

Patients with rare diseases are a major challenge for healthcare systems. These patients
face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to …

Although individually rare, collectively more than 7,000 rare diseases affect about 10% of
patients. Each of the rare diseases impacts the quality of life for patients and their families …

A medical analysis of diagnosing rare genetic diseases has rapidly become the most
expensive and time-consuming component for doctors. By combining predictive methods …

Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic
complexities, present significant challenges to healthcare systems. This article explores the …