The management of cancer in the modern era is both and art and a science. While randomized trials and standard of care treatment algorithms can guide many aspects of cancer care the process also has to be personalized to the individual patient. We aim to utilize AI to help synthesize large amounts of available literature as well as provide evidence based individualized treatments plans. Ongoing work includes predictive modeling and building systems for analyzing radiology images to identify cancer subtypes non-invasively.

Our team has extensive experience working with institutional and national large structured and semi-structured databases to improve surgical outcomes. As the size of these databases grows, we aim to utilize AI to supplement traditional research methodologies. This has the potential to process large amounts of data much faster than traditional mechanisms and can identify patterns that are otherwise missed.

Traditional surgical outcomes research has relied on structured, semi-structured, and unstructured databases. Our team aims to build on the existing mechanisms and databases available for surgical research by combining them with other large datasets like imaging, videos, semi-structured patient data that has previously not been able to be efficiently utilized. Patient-centered outcomes are important because they focus on the perspectives, preferences, and experiences of patients, which are essential to delivering high-quality care. Our team has extensive experience working with patient-centered outcomes, including patient experience and QOL data and combining this with traditional surgical outcomes measures. As the amount of data available for analysis grows traditional research methodologies are insufficient and therefore our team leverage the power of AI to improve patient-centered outcomes.