Automating Digital Health

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone1. The ultimate goal of this research area is to develop AI systems that automate the entire computational workflows of today’s data scientists. Doing will accelerate the analysis of complex data in the biomedical and health care domains.

Overview of the PennAI GUI
Accessible, Automatic Data Science:

See my post talking about our Bioinformatics paper.

Related Publications

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling
Mayourian, J., La Cava, W. G., Vaid, A., Nadkarni, G. N., Ghelani, S. J., Mannix, R., Geva, T., Dionne, A., Alexander, M. E., Duong, S. Q., & Triedman, J. K. (2024)
Accuracy of deep learning models in interpreting intrapartum fetal monitoring to predict fetal acidemia
McCoy, J. A., Wan, G., Levine, L. D., Teel, J., Holmes, J., & La Cava, W. (2024)
American Journal of Obstetrics and Gynecology (AJOG)
Evaluating recommender systems for AI-driven biomedical informatics
La Cava, W., Williams, H., Fu, W., Vitale, S., Srivatsan, D., Moore, J. H. (2020)
A System for Accessible Artificial Intelligence
Olson, R. S., Sipper, M., La Cava, W., Tartarone, S., Vitale, S., Fu, W., Holmes, J. H., & Moore, J. H. (2017)
Genetic Programming Theory and Practice XIV.
Data-driven Advice for Applying Machine Learning to Bioinformatics Problems
Olson*, R. S., La Cava*, William, Mustahsan, Z., Varik, A., and Moore, J. H. (2017)
Pacific Symposium on Biocomputing (PSB)


  1. Pearson, J. (2017). These Researchers Want the People to Seize the Means of AI Production. Motherboard. link