Browse Articles

This study investigates skin tone bias in artificial intelligence models used for dermatological disease classification and evaluates a CycleGAN-based data augmentation approach to improve diagnostic performance on darker skin types. We generated synthetic dark-skinned images to enhance dataset diversity and compared model performance before and after augmentation. The results demonstrate that augmentation with synthetic dermatological images can help reduce disparities in diagnostic performance across skin tones, highlighting a practical strategy for improving fairness in dermatology AI systems.

This manuscript explores the use of Spanish language translators in an outpatient clinic in New Jersey. The authors surveyed patients before and after in person, video, and telephone appointments to determine which modality was most acceptable to the patients. The authors found that the three modalities did not differ in patient satisfaction, but that patients were grateful for translations services and that patient trust may be expanded by the use of these services and by focuses of translator soft skills.

The authors looked at how different blends of synthetic and non-synthetic fabrics protected against UV radiation.

The authors investigated adoption rates of solar photovoltaic power at K-12 schools in California.

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.

Here the authors evaluate the relationship between state-level abortion policies and maternal mortality across the United States from 2018 to 2021. Their findings reveal that states with restrictive abortion laws consistently experienced higher maternal mortality ratios than those with protective policies, culminating in a 72% increase in deaths within restrictive states by the end of the study period.

This study analyzes genetic alterations and expression patterns of protein kinases involved in phagocytosis across multiple cancers using TCGA data.

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.

The authors analyzed racial and ethnic representation in studies on PFAS and neurological health outcomes.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.