There’s a lot of speculation about the impact of artificial intelligence (AI) on the medical field. Could it be the new surgical assistant or medical records clerk? In some cases it already is. Might AI someday replace doctors altogether? Probably not anytime soon. For now, patients can take comfort knowing they’ll still get their exam – and post-visit lollipop – from an actual person.

But make no mistake; AI services are permeating the healthcare sector. Business Insider Intelligence reports that spending on AI in healthcare is projected to grow at an annualized 48% between 2017 and 2023. And by 2021 experts predict Healthcare AI will reach $6.6 billion in value.

For now, AI is streamlining many medical processes in its wake. Whether managing and analyzing patient data or diagnosing and treating the big C, AI tools powered by machine learning are improving healthcare across the board.

Let’s jump in for a closer look at how AI is modernizing modern medicine, but first, a vocabulary lesson:

Important Healthcare Artificial Intelligence Terms

Artificial Intelligence (AI): At its core, AI is a computer or software which can analyze, learn and change based on the data it receives.

Machine learning: The algorithm used by AI that can identify patterns in large data sets and learn independent of human programming.

Deep learning: A cousin of machine learning, deep learning performs all the functions stated above and goes a step further, drawing conclusions from the previously categorized information in its database.

Now that you know more about what it is, here are seven scenarios that demonstrate how AI is currently shaping and improving the healthcare market.

1. Making more accurate diagnoses using predictive technology

AI is highly adept at recognizing patterns, and excels when fed large amounts of data. With enough images and case files, over time these technologies have grown successful in identifying specific diseases at the same rate of experts and specialists. Some AI software has also been able to make accurate predictions about the length of a patient’s hospital stay based on similar cases.

So how much data are we talking? One study published in the journal Nature Medicine details an AI system in Guangzhou, China that used 567,000 electronic health records as training to help it diagnose the area’s most common pediatric illnesses.

No clinician or medical tech has the time or mental bandwidth to pore over a few thousand records. Even if they did, it could be easy for human eyes to miss the more subtle patterns these sophisticated systems are able to detect.

2. Bettering patient data management and documentation

AI tools can also be programmed as diligent, organized note takers. Think of this type of machine learning as Alexa on steroids.

To help document and manage patient data, AI assistants trained in notation can record and transcribe a conversation directly into a patient’s electronic medical records. In some preliminary trials, AI tools could easily distinguish between and categorize multiple voices. These technologies can also flag pertinent pieces of a patient’s medical history for physician review.

Supposedly Google is currently getting in on the AI action, teaming up with Stanford University to prototype these and other medical “digital scribe” technologies.

3. Helping pinpoint appropriate cancer treatments

Advances in cancer treatments are one of the most promising ways AI is improving healthcare. As expert diagnosticians, these technologies have the ability to do double duty: analyzing and comparing massive patient data sets with other cancer cases, and doing a deep dive into the patients’ individual medical histories to assist clinicians in charting the appropriate course.

One specific report from Business Insider Intelligence chronicles the use of a product to map data-informed cancer treatments for thousands of patients at the Lineberger Comprehensive Cancer Center. By analyzing the facility’s big data inputs, it was able to specify individualized treatment options for those suffering from genetically abnormal tumors.

4. Creating advanced neural networks to assist medical researchers

More precise diagnostic systems. Better image analysis. Drug development and biochemical breakthroughs. These are the benefits of the sophisticated neural networks being created by medical AI programs.

An artificial neural network is created by machine learning algorithms that respond in specific ways depending on the data received. The process is similar to the way neurons in our human brain wire and fire together based on certain stimuli.

As a neural network grows, it learns with each individual outcome, thus fine tuning its predictions. The goal is to decrease the margin of error between the two. In time, the analyzed data outputs are translated into usable insights that can help medical researchers solve important healthcare challenges.

5. Identifying abnormalities through computer vision imaging

When it comes to imaging, AI software is quickly becoming an invaluable part of the team. Apps from health tech companies like Zebra Medical Vision allow physicians to scan images into the system, where an algorithm is automatically programmed to monitor them for anomalies. It will even give diagnostic recommendations or make predicted health outcomes as requested.

6. Improving medical data organization via natural language processing

Computers read and think in numbers, not narratives. So in order for an AI system to accurately comb through things like examination notes, lab reports, medical journal reviews or discharge paperwork, it needs to use a method called Natural language processing (NLP) to decipher them.

NLP turns text, known as unstructured data, into machine-readable structured data. This allows things like a patient’s full chart to be analyzed by machine learning algorithms so they can assist clinicians in providing more informed care.

7. Increasing operational efficiency through automation

More often than not, you’ll find AI hard at work in the back office. From sending billing reminders and processing claims to pre-authorizing insurance and digitizing records, the healthcare industry has fully embraced AI’s automation abilities.

For an industry with tons of tedious of paperwork to contend with, it’s no hard sell: Streamlining administrative functions can save huge swaths of time and money.

Conclusion

By and large, smaller clinics have been slow to implement new AI-driven tools and technologies. Which is understandable, given that many just underwent the demanding process of switching from paper-based to electronic medical records. However, early adopters are spearheading the shift with great success.

No matter how you compute it, AI is already an important partner in healthcare. As it evolves, AI is poised to continue improving the medical system in the process. But don’t worry, it’s unlikely to fully replace humans entirely – the bedside manner still needs a lot of work.