It’s no exaggeration to say that data analytics have made a huge impact on the healthcare industry. Advances in AI, machine learning, natural language processing, and deep learning are making it easier for healthcare practitioners and other stakeholders to identify healthcare needs and their respective treatments faster, with greater accuracy, and with optimized resources.
The following are just a few examples of how AI is driving change in the healthcare sector:
1. Decreasing the Cost to Develop Medicines
AI can analyze images, clinical research trials, medical claims, and other relevant data sources to identify insights that would typically take humans months, if not years. One such example is reducing the time and cost involved in developing medicines. Supercomputers can predict from databases of molecular structures whether or not potential medicines and treatments would be effective for various diseases.
2. Analyzing Unstructured Data
Historically, health data and patients’ medical records have been stored in unstructured formats, making them difficult to access and interpret. AI can identify, collect, store, and standardize this information regardless of the format. As a result, clinicians can access fast, accurate, and tailored treatment plans for their patients without having to manually sift through paper health records or research prior medical history.
3. Using Data for Predictive Analytics
Natural language processing and machine learning can read a patient’s entire medical history in real-time and connect it with symptoms, chronic affections, or a condition that affects other members of their family. This can then be used as a predictive tool to identify and treat diseases before they become life-threatening.
4. Providing Critical Assistance to Emergency Medical Staff
Another important AI use case is the ability to arm 911 dispatchers and first responders with more accurate insights. Let’s take a potential cardiac arrest as an example; in these circumstances, the time between the initial 911 call and the ambulance arrival is crucial to recovery. AI can analyze both verbal and nonverbal cues, combine this information with the patient’s medical history, and help establish a diagnosis from a distance.
5. Building Complex and Consolidated Platforms for Drug Discovery
AI algorithms can identify new drug applications and trace their toxic potential as well as their mechanism of action. In addition, AI and automation enable pharmaceutical companies to significantly accelerate their testing pipeline, with some organizations running 1.5 million experiments every week.
You can read more on these and other emerging AI use cases in healthcare here.