Large language models (LLMs) are being integrated into healthcare systems; but these
models may recapitulate harmful, race-based medicine. The objective of this study is to …

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its
inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable …

Protein language models (pLMs) trained on large protein sequence databases have been
used to understand disease and design novel proteins. In design tasks, the likelihood of a …

Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to
understand who is at risk and what interventions improve outcomes. However, current …

In recent years the development of artificial intelligence (AI) for medical image analysis has
gained enormous momentum. At the same time, a large body of work has shown that AI …

Data quality is a critical aspect of data analytics since it directly influences the accuracy and
effectiveness of insights and predictions generated from data. Artificial Intelligence (AI) …

Background Due to the lower reliability of laboratory tests, skin diseases are more suitable
for diagnosis with AI models. There are limited AI dermatology diagnostic models combining …

Previous work has highlighted that existing post-hoc explanation methods exhibit disparities
in explanation fidelity (across “race” and “gender” as sensitive attributes), and while a large …

Importance Health datasets from clinical sources do not reflect the breadth and diversity of
disease, impacting research, medical education, and artificial intelligence tool development …

The use of high-fidelity synthetic data for data augmentation is an area of growing interest in
data science. In this chapter, the concept of synthetic data is introduced, and different types …