Along with technology such as cloud computing and telehealth, healthcare leaders have embraced artificial intelligence since the start of the pandemic. A survey conducted by Intel in late 2020 found that 84 percent of senior decision-makers were using or planning to use AI, up from 37 percent just two years earlier.

The top AI applications among those surveyed come as no surprise: predictive analytics, clinical decision support and collaboration across care teams. However, forward-thinking organizations are looking beyond these typical use cases and finding ways to use AI in areas such as robotics, image and voice analysis, and collaborative research.

What makes these new applications possible? Optimizing AI frameworks — such as the open-source Caffe and TensorFlow — to run as fast as possible on underlying hardware, says Chris Gough, worldwide general manager of health and life sciences at Intel.

“This speeds up algorithm training and inference, which reduces the cost of development and deployment,” he says.