AI in Healthcare: Improving Patient Outcomes with Three Key Processes

Artificial intelligence is not a new phenomenon, but its applications continue to grow. In healthcare, AI is making a difference in everything from patient care and interaction to disease progression tracking and drug discovery. By pairing the abilities of modern computers with the skill of physicians, the healthcare world is seeing numerous benefits, including:

• Reduced costs

•  Improved patient outcomes

• Streamlined internal processes

Here are three specific areas where AI is making an impact in today’s healthcare industry.

1. Machine Learning

Machine learning systems send data sets through a series of algorithms to make predictions or create outcomes using that data. They aren’t always very good or efficient at first, but these systems “learn” from their mistakes or negative outcomes, getting better and better at processes over time.

Essentially, the more often the artificial intelligence processes the data, the more efficient and effective it becomes.

In healthcare, machine learning software can collect and analyze data more quickly than people, which helps in several ways.

First, it can help in developing new drugs and treatments for conditions. AI-powered applications can use researcher-developed models and algorithms to predict the success of various drug formulations, giving researchers better direction on which chemical formulations to pursue, develop, and test in the real world.

Hospital readmissions are another area where machine learning can help. Through AI, hospitals can better understand how many patients are likely to be readmitted, allowing them to keep at-risk patients in the hospital longer or staff appropriately for anticipated readmissions.

Third, machine learning is a key tool in tracking disease progression. In the recent pandemic, machine learning played a major role in predicting variants and the spread of the disease. It was also a key factor in how quickly the COVID-19 vaccine was developed and implemented. Deep learning models can come alongside health surveillance organizations to provide early warning and help mobilize public health responses.

2. Natural Language Processing (NLP)

Natural language processing teaches computers how to understand and analyze text and spoken words. In healthcare, large amounts of data must be processed every single day, and NLP technology helps. Medical providers can use NLP technology to harness the most critical data points from this unstructured data without digging through and reading massive amounts of notes written in charts. NLP tools can also analyze clinical documents and translate them into the format necessary for electronic health records.

While processing unstructured data, NLP technology can pull the most important pieces of information from clinical notes. This, in turn, can help doctors find previously missed conditions in their patients, ensuring a better quality of care for each patient.

3. Computer Vision

Computer vision is a branch of AI focused on helping computers learn to identify people and objects, not just data. When computers can understand and analyze the visual world, they can better assist in imaging diagnostics, quickly analyzing imaging for signs of disease and injury.

Computer vision helps physicians in several ways. First, it increases diagnostic accuracy serving as a hedge against human error. Second, it can help doctors find an accurate diagnosis in less time and with less cost to the patient, leading to earlier diagnoses, lower costs, and improved treatment outcomes.

AI is not poised to take over for doctors, but several AI-powered tools are already making medical jobs easier and more successful. From improved patient outcomes to improved paperwork, AI is ready to revolutionize the medical industry.