pg68 Harvesting Computing Brainpower

Harvesting Computing Brainpower to Improve Healthcare

5 Key Steps in Leveraging AI in Healthcare

A fundamental aspect of today’s artificial intelligence (AI) applications is the strategic leverage it gives users. While all business sectors will benefit from AI, the healthcare industry will see widespread adoption as Administrators and CEOs realize its potential. This is an emerging technology, and, as such, businesses that operate within the healthcare industry who begin using AI will gain a competitive advantage. The following five key steps will help define how to leverage AI in healthcare.

First, users must understand what AI is and what is does. AI applications use the same data other systems use. Although the common perception is that AI simply replaces human ability, the key point is that it does some things “better” or “more accurately” and/or some things humans want to do but cannot. For instance, Finnish company Fimmic Oy, developed a deep learning AI application (deep learning means that software attempts to mimic human thinking activity. The software learns to recognize patterns, such as those in digital representations of sounds, images, and other data). that helps pathologists identify abnormalities the human eye cannot see. (1) The key is to remember AI’s ability to leverage data and information at levels humans cannot. It is not a magic robot! But it does have powerful information processing capabilities. With baseline training, existing healthcare workers should be able to manage and control new AI applications.

Second, AI can use sophisticated algorithms to “learn” features from a large volume of healthcare data, and then use the obtained insights to assist clinical practice. (2) Much of today’s AI literature uses the term “deep learning” to describe what AI does behind the scenes.

Third, given AI’s learning capacity, the long-identified issues of injury and death caused by medical errors (the third leading cause of death in the U.S.) can be addressed at a micro level. The resulting data then becomes a point of leverage for funding elements such as Medicare reimbursements. Deep learning promotes “self-correcting abilities to improve system accuracy based on feedback.” (3)

Fourth, AI can assist with evidence-based practice (EBP) protocols by monitoring the hundreds of accessible information databases, enabling real-time EBP. This physician/AI partnership adds to the benefits of meaningful use and other US federal healthcare requirements.

Finally, AI will contribute to public health initiatives. By linking preventative medicine routines with elements such as diabetes risk factors, healthcare organizations can begin to project, “healthy communities,” that ultimately contribute to public health initiatives that are equitable. It certainly costs less money to provide preventative healthcare services than to perform surgeries or to administer extreme treatments in an acute care setting.



[1] TIBBETTS, J. H. 2018. The Frontiers of Artificial Intelligence. BioScience, 68(1), 5–10.

[2] Jiang, Fei, et al. 2017. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology 2(4), 230-243.

[3] Ibid.

IGW Staff

IGW Staff

InfoGov Thought Leaders

Leave a Reply

Digital Editions

Read Our Latest Edition

Scroll to Top