In retail, the use of AI and Machine Learning is about driving sales and ROI. In education, it’s about student engagement and learning. In hospitality, it’s about guest services. In healthcare, it’s about saving lives. Medical errors are the third-leading cause of death after heart disease and cancer. By leveraging AI and Machine Learning we can minimize errors, save lives, and conserve resources at the same time.
Virtual nursing assistants can reduce unnecessary patient hospital visits and lower the burden on the medical staff. For routine monitoring of levels, dosages, and checkups, the use of AI can monitor patients after they leave the hospital and lower re-admittance rates.
Back in 2017, Apple and Stanford unveiled a heart study program using the Apple Watch’s heart rate sensor to collect data on irregular heart rhythms and notify users who may be experiencing atrial fibrillation (AFib). AFib, the leading cause of stroke, is responsible for approximately 130,000 deaths and 750,000 hospitalizations in the US every year. Many people don’t experience symptoms, so AFib diagnosis is often missed. As we live in a more connected world, by putting Machine Learning to work on mass amounts of anonymized medical information, doctors and researchers can learn about new diseases, new trends, and build new medications.
Using AI to diagnose patients is undoubtedly in its early stages, but there have been some interesting use cases. A Stanford University study used an AI project to detect skin cancer against dermatologists, and it performed at the same level as humans. As more information is collected, it’s likely that AI diagnosis will become even more accurate over time.
Using AI and Machine Learning is going to allow us to learn more and more about how diseases respond to medicine, how diseases form, and what we can do to prevent. Plain and simple: lives will be saved in the future because of AI and Machine Learning in healthcare.
