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 …

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

The utilization of large language models (LLMs) for Healthcare has generated both
excitement and concern due to their ability to effectively respond to free-text queries with …

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

In the complex and multidimensional field of medicine, multimodal data are prevalent and
crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types …

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer
screening has led to the increasing detection of pulmonary nodules. However, precisely …

Artificial intelligence (AI) is rapidly transforming healthcare, and one of its most promising
applications is in the diagnosis and treatment of diseases. AI algorithms can analyze vast …

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) has made significant strides in revolutionizing cancer care,
encompassing various aspects from diagnosis to prevention and beyond. With its ability to …

Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance …