Privacy and ethical considerations limit access to large-scale clinical datasets, particularly
clinical text data, which contain extensive and diverse information and serve as the …

Artificial intelligence (AI) is revolutionizing nearly every aspect of modern life. In the medical
field, robotic surgery is the sector with some of the most innovative and impactful …

Large language models (LLMs) hold promise to serve complex health information needs but
also have the potential to introduce harm and exacerbate health disparities. Reliably …

As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …

The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …

Foundation models represent a paradigm shift in artificial intelligence (AI), evolving from
narrow models designed for specific tasks to versatile, generalisable models adaptable to a …

Abstract Machine learning and artificial intelligence (AI/ML) models in healthcare may
exacerbate health biases. Regulatory oversight is critical in evaluating the safety and …

Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with
considerable potential to improve early cancer detection and risk assessment, and to enable …

Digital health technologies can generate data that can be used to train artificial intelligence
(AI) algorithms, which have been particularly transformative in cardiovascular health-care …

Without careful dissection of the ways in which biases can be encoded into artificial
intelligence (AI) health technologies, there is a risk of perpetuating existing health …