AI is beginning to work miracles in the world of healthcare—and not just by decoding doctors’ handwriting.

Compared to our digital counterparts, human analysis is slow and subjective. People can only calculate at the speed of the human brain, and our conclusions are influenced by a slew of nonscientific biases. In a medical context, speed and accuracy (or lack thereof) have life-or-death consequences.

Fortunately, humans have begun to build AI systems for speedy, objective analysis.

Nowhere is this more consequential than in the world of healthcare.

Healthcare AI tools use:

• Machine Learning (ML)—processing large datasets and using the results to inform decisions
• Deep Learning (DL)—a form of ML using an artificial neural network to learn by ultra-high-speed trial and error
• Natural Language Processing (NLP)—understanding and interpreting human speech to inform treatment options

The combination is having profound effects on healthcare.

1. ANALYZING OCEANS OF PATIENT DATA

The same way companies have struggled to standardize data from many different sources, healthcare providers have struggled to unify the many different forms of patient data. Background documents, medical history, treatment reports, and even data from wearables like Apple Watches come in different forms and live in different places. Humans don’t do well with that.

But AI does just fine. Like companies have ETL tools to extract data from many sources and store it in a common data warehouses, AI can now do the same for the many sources of patient data.

More accurate insights=more impactful care. AI patient analytics are fueling better predictive care, optimized cancer radiation treatment, kidney disease forecasting, and more.

2. FAST-TRACKING DRUG DEVELOPMENT

Medications work on a molecular level. External molecules (e.g., Xanax) interact with internal molecules (e.g., neurons) to prevent or cure an ailment.

This has already sped up the development of high-urgency medications, as in 2015, when scientists used it to develop a treatment for Ebola.

3. PROVIDING FASTER, UNBIASED ANALYSIS

“Health inequity” is defined as “unfair and avoidable inequalities that are not inevitable or natural but the product of human behavior.” A few key examples of health inequities include:

• Socioeconomic disadvantage reduces life expectancy
• LGBTQ individuals have higher risk of mental illness due to bullying
• Black babies have a higher mortality rate than white babies (for no biological reason)

Not all inequities are the result of HCP bias, but some are. The algorithms driving AI analysis can be programmed to exclude the cultural biases that humans cannot shake. This could mean more accurate information, more equitable treatment, and leveling the field of healthcare across races, ethnicities, and genders.

4. ASSISTING EMERGENCY MEDICAL SERVICES (EMS) CARE

AI can now listen in on emergency calls. It analyzes both what patients are saying and how they’re saying it, instantaneously comparing the information to a reservoir of past data points. If a patient calling about chest pains displays vocal patterns to common to people suffering from cardiac arrest, it can dispatch emergency care before the condition worsens (which is good, because cardiac arrest only takes 10 minutes to be fatal).

That emergency care also takes the form of AI-equipped devices. Earlier this year, a drone carrying a defibrillator was dispatched to a Swedish man who collapsed from cardiac arrest while shoveling his driveway. The drone arrived before the ambulance, enabling a nearby doctor to save the man’s life.

The human brain evolved to handle environmental, social, and linguistic information, which it does very well. It doesn’t do nearly as well with numerical information (Hindus invented the numerals 0 to 9 only about 1,500 years ago; our species has been evolving for more than 2.5 million years).

Which is to say, we need a little help. The exponential advance of AI is impacting every realm of healthcare, from the most urgent emergency care to long-term predictive and therapeutic care.

This article originally appeared in Minutes magazine and is reprinted with permission.