The Ochsner BioDesign Lab is applying evidence-based artificial intelligence (AI) engineering to integrate Machine Learning (ML) approaches in a variety of healthcare applications. The Lab is currently integrating ML into its advanced visualization services for clinical applications like intracranial aneurysms and liver transplant. Machine Learning approaches have allowed the lab to efficiently deliver and expand our visualization services while maintaining the quality required for procedural planning, clinical training, and patient education. Preliminary investigations in liver transplant have demonstrated a 40% reduction in time required to create 3D Models by our biomedical engineers. Our engineering corp is actively developing better and more effective ML-models to further augment our advanced visualization services.
Rajpurkar, P., E. Chen, O. Banerjee, and E.J. Topol, AI in health and medicine. Nat Med, 2022. 28(1): p. 31-38.
Aung, Y.Y.M., D.C.S. Wong, and D.S.W. Ting, The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull, 2021. 139(1): p. 4-15.
Mongan, J., L. Moy, and C.E. Kahn, Jr., Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell, 2020. 2(2): p. e200029.
