Machine learning (ML) & artificial intelligence (AI) frameworks are expected to play a significant role in driving innovation and discovery in healthcare research. Examples of ML & AI applications in health care include medical imaging and diagnostics, robot-assisted surgeries, and remote care assistance.

To foster research collaborations using ML & AI, we must bridge the knowledge gap between healthcare decision-makers and engineers/data scientists. Specifically, both groups should understand ML & AI methods and how algorithms are validated for healthcare applications.

The Malone team will develop an online course to equip both Hopkins clinicians and engineers with the skills to design, analyze, interpret, and report research on ML & AI in health care.

Course page: