7 Ways AI is Transforming Healthcare

One area AI has proven incredibly useful is with image analysis. A radiologist’s job is to identify anomalies in medical images. Software has been extremely efficient at highlighting possible irregularities, which a radiologist then investigates further to make their prognosis. This has significantly cut down on the time required to analyse imaging results, and allows radiologists to focus their efforts on gauging the problem and the best course of action to take without the time needed to flag the possible anomalies the AI can do much more efficiently. A 2011 NYU study found that AI could locate lung nodules in CT scans on average 62% to 97%faster than radiologists. The time saved would both help the patient’s treatment, but also save the healthcare industry billions of dollars annually.

Another difficulty AI has helped ease in healthcare is drug dosage errors. Up to a third of all preventable medical errors are a result of dosage errors, which often include educated guesswork with several variables to constantly monitor and amend accordingly. Formulas developed with AI, have been successfully used to calculate immunosuppressant drug dosages in organ transplant patients. The fact that complex algorithms can do a better job of calculating drug dosage for each patient, will help patients avoid medical errors while they undergo different treatments.

Perhaps the most obvious effect AI has had on healthcare is in surgical procedures. Many surgeries have become much safer and with a much quicker recovery time thanks to robotic assistance to surgeons. Surgeries that needed large incisions in the past to allow a doctor to perform certain procedures, have been replaced with tiny incisions that can fit robotic arms controlled by the surgeon. This dramatically reduces human error during surgery and patients who undergo robot-assisted surgeries are 5 times less likely to suffer surgical complications. Robot-assisted surgeries have also cut down recovery time by as much as 21%, allowing patients to leave the hospital sooner after surgery.

Diagnosis by AI is still in its early stages, however promising results have been coming in the past few years. In 2017, an AI was compared to 21 dermatologists at Stanford University. The software was able to perform as competently as the 21 dermatologists when it came to identifying and classifying different types of skin cancer. This possibility of preliminary diagnosis by AI would help save a lot of time and money, and allow doctors to focus more on the best way to treat an ailment that can be easily flagged initially by software.

It is widely known that most parts of the world suffer from qualified nurses shortages. Virtual nursing software is being used by the NHS in the UK to interact with patients, can ask them health-relate questions and based on their symptoms, direct them to the right care unit. This would free up a significant amount of nurses’ time, allowing them to focus on less mundane tasks that need a human at the helm to properly execute.

More than half of nurses’ time and around a fifth of doctors’ time is spent on tasks not directly related to patient care. Writing chart notes, filling prescriptions and ordering tests can all be streamlined with AI, allowing nurses and doctors to dedicate more time caring for their patients, instead of the back-office tasks that consume a big portion of their time at work.

Billions of dollars a year are lost to fraudulent insurance claims. Assessing each claim needs a lot of time and might not always get it right, putting patients’ health and lives at risk. AI neural networks can think like a human brain for certain tasks, but can do so much faster. One way is by identifying true claims quicker and thus allowing patients to receive the care they need faster. It could also save the healthcare industry billions of dollars that would have otherwise gone to fraudulent claims, making sure patients that need the care the most, get it.