Five Key Challenges that are Slowing the Progress of AI in Healthcare

Five Key Challenges AI Models_Preview
LUCEM HEALTH WHITE PAPER

Bridging the gap between people, process, and AI models

Several difficult challenges slow organizations’ efforts to integrate and use AI in ways that create measurable, practical value for their businesses. These include:

  • Acquiring and normalizing the right data from the right sources

  • Integrating AI models naturally into clinical workflows

  • Scaling AI models to support hundreds of healthcare organizations and thousands of clinicians

  • Building continuous improvement into every AI deployment

  • Earning the trust of frontline clinicians

For data scientists in organizations who develop clinical AI models, it’s important to understand these headwinds, how they apply specifically to healthcare, and what it will take to overcome them to create successful AI solutions that improve patient outcomes in real-world clinical environments.

Download the White Paper