Artificial intelligence (AI) is breaking into the precision medicine by assisting doctors to make complicated decisions faster and more efficiently. Computational methods allow for the development and implementation of novel predictive genetic tools. We aim to facilitate the clinical adoption of AI technologies to improve the lives of our patients.
In collaboration with WCM's Institute for Computational Biomedicine, the EIPM recently launched the Artificial Intelligence (AI)-Extended Reality (XR) lab. The mission of the new lab is to leverage recent advances in Augmented Reality, Virtual Reality and Mixed Reality with Artificial Intelligence approaches to enable our clinicians, researchers, and students to visualize, interact and collaborate in real-time regardless of distance on complex medical datasets.
The development of innovative AI tools helps to personalize medicine - Because genomic reports generate massive amounts data, only AI can identify patterns that lead to actionable information. The future of medicine depends on patient centered AI-driven transformation. For example, we use new techniques to predict whether a pre-diabetic patient is at risk of progressing to diabetes based on past Electronic Health Record
(EHR) data and multi-omic profiles, and we may also be able to tailor fertility treatment to individual patients. Select AI projects at the Englander Institute include:
- Precision Medicine Knowledgebase (PMKB): In conjunction with Microsoft developers, the EIPM created PMKB Bot, a chatbot that supports both text and voice interactions with the for clinical cancer variants and interpretations.
- BANDIT (Bayesian ANalysis to determine Drug Interaction Targets): a machine-learning algorithm designed to make use of virtually all available data on any prospective drug compound, in order to predict what enzyme or receptor or other target it interacts with in cells to have its therapeutic effect.
- PrOCTOR: Rather than exclusively examining molecular structure to determine viability, this new computational method combines a host of structural features and features related to how the drug binds to molecules in the body.
"By 2025, global estimates indicate 450+ exabytes of personal health data will be created each day.
This volume...is why AI is so critical to precision medicine.