Artificial Intelligence: The Next Revolution in Healthcare and Precision Medicine

Autoimmune diseases, infectious disease and cancer have become increasingly difficult to treat using conventional methods that do not take into account individual genetic, environmental, and lifestyle differences. The “one-size-fits-all” approach to healthcare no longer works. Developing new personalized treatments is like trying to work a vast, multidimensional jigsaw puzzle with pieces that are constantly changing shape.

The Nutritional Immunology and Molecular Medicine Laboratory (NIMML), a leading lab at the Biocomplexity Institute of Virginia Tech and Biotherapeutics, a biotech startup, are applying artificial intelligence methods to accelerate the path to cures for complex human diseases. These efforts are aligned with the Precision Medicine Initiative which gives researchers and medical practitioners tools to cure people, but it is also empowers individuals to monitor and take a more active role in their own health.

Artificial intelligence algorithms are used to create synthetic patient populations with the properties of actual patient cohorts, build personalized predictive models of drug combinations and unravel complex relationships between diet, microbiome and genetic lineup to determine the comparative treatment response. The use of AI inspired machine learning methods leverages the volume and exponential growth of clinical data from electronic health records to translate clinical information into new unforeseen insights for safer, more effective and cost-efficient personalized healthcare.

NIMMLab used Artificial intelligence inspired machine learning technologies to build upon an existing interaction model of Clostridium difficile infection. NIMML’s new computational pipeline, translates preclinical results in animal models to accurate clinical outcomes and is capable of identifying effective treatments, predicting optimal doses of drug product and drug responses. The pipeline published in Artificial Intelligence in Medicine, incorporated mechanistic ordinary differential equation-based models with stochastic simulation and advanced machine-learning methods to test and predict the efficacy of existing and novel treatments for infectious diseases. In another study, the NIMML team developed new computational methods to stratify stroke patients in an emergency setting, paving the way to a personalized data-driven triage process with higher fidelity.

“The ability to seamlessly integrate big data and theory across complex information processing architectures using artificial intelligence is revolutionizing Precision Medicine. The convergence of advanced data analytics, modeling, and high performance artificial intelligence systems with high resolution, large-scale patient data creates an opportunity to fundamentally transform how medicine is practiced,” said Josep Bassaganya-Riera, Director of NIMML and CEO of BioTherapeutics.