AI Adoption for Healthcare

Health care, the world’s largest data source, is behind almost all other industries in its AI adoption journey—making it a prime investment opportunity.

Although the foundation of IVF is grounded and one of the greatest inventions of our time, there has been very little digital adoption in the actual full stack clinical treatment process.

Health spending in the U.S. reached $4.5 trillion in 2022, and billions upon billions are wasted each year. And experts say that health care is behind almost all other industries in its AI adoption journey. A big issue is data, as in, there’s too much of it and it isn’t connected across a highly fragmented industry. Health care is the world’s largest data source, at 30% of annual production, but 80% of that data is unstructured.

The health care industry is excellent in generating reams of data. But as an industry, we’re not surprisingly that good at analyzing and drawing correlations out of that data.

The health sector’s AI investments focused on three buckets: early stage clinical trials, clinical diagnosis, and back-office tasks.

Natural language processing and machine learning have gained traction across health care, with use cases ranging from virtual physician assistants to clinical trial patient recruitment. Three out of every four leading health care companies are experimenting with generative AI or attempting to scale it across their business.

But a lot of barriers remain. The cost of health care has marched steadily upward even as life expectancy in the U.S. has declined. A vast majority of clinicians have reported burnout and labor shortages are persisting for both doctors and nurses. Profits are under pressure from rising operational costs and shrinking reimbursement rates.

What we should be doing as a society is invest in preventative health care, because it is cheaper for all of us, including the insurance companies.

GE HealthCare worked with Vanderbilt University Medical Center to scour medical record data and use AI-powered applications to help predict how a patient would respond to more precise cancer immunotherapies. This could help avoid potentially damaging and ineffective treatments, while also saving costs.

AI has become a tool that will continue to be leveraged to identify those disparities and to be more responsive to patients and health care providers to reduce those disparities.