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In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.

Asthma is one of the most prevalent chronic conditions in the United States. But not all people experience asthma equally, with factors like healthcare access and environmental pollution impacting whether children are likely to be hospitalized for asthma's effects. Li, Li, and Ruffolo investigate what demographic and environmental factors are predictive of childhood asthma hospitalization rates across Pennsylvania.

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

Cystic fibrosis is a genetic disease caused by mutations in the CFTR gene. In this paper, the authors attempt to identify variations in stretches of up to 8 nucleotides in the protein-coding portions of the CFTR gene that are associated with disease development. This would allow screening of newborns or even fetuses in utero to determine the likelihood they develop cystic fibrosis.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.