AI is changing the way that medical researchers and medical device manufacturers approach healthcare. The tech is accelerating existing processes and even making some new approaches to healthcare possible for the first time.

Here are 10 ways AI is being used for health and wellness

1. Providing Clinical Support

For doctors with time, there is a wealth of medical literature out there that they can use to inform treatment. However, doctors don't usually have this opportunity, which can impact their ability to treat patients with unique conditions. For example, dermatology patients often have complex medical histories resulting in multiple symptoms. Treatment for these symptoms needs to be managed carefully. One unique strength of AI is its ability to quickly scan and parse information in plain written language — like the information found in clinical literature. New AI-powered clinical support models take advantage of this strength and can provide clinical support by scanning through massive volumes of articles and studies.

2. Drug Repurposing

New drugs are difficult to develop. Typically, it takes more than 10 years and $2 billion to move a drug from development to federal approval. As a result, many pharmaceutical companies focus their R&D efforts on repurposing already-approved drugs. The search for new applications of existing drugs is also slow, however. By comparing the structure and effects of unused drugs to existing treatments, an AI algorithm can effectively identify candidates for drug repurposing. This allows researchers to focus on drugs that are more likely to be effective treatments.

3. Virtual Nurses

Hospitals often find themselves under a range of intense pressures, like time and money. This means patients may sometimes be left in the dark about their condition simply because the staff around them are too busy to answer questions in depth. Virtual AI nurses being pioneered by medtech companies can provide 24/7 bedside support and even build a personalized relationship with a patient. These nurses use information from a patient's medical records to provide recommendations for treatment options and inform patients about their health.

4. Developing New Vaccines

Some modern flu vaccines include adjuvants, which are compounds that help the immune system fight off disease. Discovering new adjuvants can make these vaccines even more valuable. However, like discovering new drugs, it is a time-consuming process. AI is helping here as well. A team of Australian researchers used AI to analyze a massive database of unused compounds and discover a new adjuvant that is even more effective than those currently on the market. Right now, the vaccine — the first designed by an AI — is on its way to trial.

5. Extracting Data From Medical Records

Electronic health records (EHRs) are dense and often unwieldy documents that cover a patient's medical history and treatment plan. These EHRs offer advantages over hand-written notes but are notoriously difficult to scan through quickly. This means critical information, like blood test history and allergies, can get lost. New AI tech helps doctors and hospitals by parsing these EHRs for them. These AI models are capable of "reading" through records and extracting the most important information. The model can then present the data in an easy-to-read format.

6. Improving Pathology

Pathology is a key practice in the diagnosis of cancer, but the methods haven't changed much in the 150 years pathology has been around. Clinical pathologists still spend much of their time looking at tissue samples that don't yield much useful information. With AI, it's possible to significantly speed up this analysis process. New AI models are capable of flagging only tissue samples that show signs of malignant growth or other irregularities. These samples can then be passed on to pathologists, greatly streamlining analysis.

7. Screening for Lung Cancer

The standard screening method for lung cancer requires a CT scan of the patient's lungs. This scanning method is effective, but it takes time and exposes patients to serious levels of radiation. Recently, researchers developed an AI that can speed up the scanning process and clean up the results of completed scans. The AI allows doctors to use low-dose CT scans rather than more extensive scanning. As a result, doctors can reduce both the amount of time needed for scanning and the radiation each patient receives.

8. Uncovering Other Types of Cancer

Similar tech is also being applied to other types of cancer. For example, one new AI that detects signs of breast cancer in mammography proved more accurate than radiologists in diagnosing the disease. Soon, this tech could be used to streamline the diagnosing process and improve accuracy while freeing up doctors' time.

Diabetic retinopathy is a diabetes complication and the leading cause of blindness among American adults. Caught early, however, the condition can be treated and may even go away with simple changes to the diet. IDx-DR is an FDA-approved, AI-powered screening technology that is able to screen for diabetic retinopathy using pictures of a patient's eyes. If the tool detects signs of what may be retinopathy, it refers the patient to a specialist — potentially saving their sight.

10. Predicting Adverse Reactions to Drugs

Adverse drug events (ADEs) are injuries caused by or related to a medication. Typically, hospital staff should catch potential ADEs when reviewing a patient's medical history — but mistakes still happen. ADEs typically extend hospital stays by two to three days and are fairly frequent, costing hospitals an estimated $4 billion annually. With new AI tech, it's possible to automatically flag a treatment plan if it may conflict with another drug a patient is on, or some aspect of their medical history. Parkland Memorial Hospital in Dallas, Texas, was able to prevent more than 2,000 ADEs over two years by adopting a customized AI model built to catch these events.

We're watching AI tech reshape how doctors and researchers approach healthcare. These AI models are already accelerating diagnostic processes and making it easier for doctors to access information. This technology is likely to become even more important in the near future as the body of available medical data continues to grow.