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The Medical Futurist, Bertalan Meskó acknowledges that artificial intelligence will greatly affect digital health in 2018. And studies predict that by 2022, A.I. in healthcare will become a seven to eight billion dollar industry.
Artificial Intelligence is revolutionizing everything from the product itself to customer experience in many different industries including healthcare. It is unquestionably influencing the way all healthcare related services are curated. From smart surgical robots to diagnostic algorithms, various aspects of the healthcare sector are being transformed to provide patients with an enriched customer experience.
So let’s take a closer look at how A.I. is leaving every stone unturned in healthcare.
1) A.I. Powered Smart Technologies And Applications Help In Diabetes Detection And Management
A Study shows among the hundred million people in America affected by diabetes, 25% cases go undetected. There have been cases in developing nations especially, where the lack of upgraded technologies and untrained professionals lead to undiagnosed diabetic patients.
A few people in my family have diabetes and at times it is difficult for them to keep track of their insulin doses and sugar levels. For such individuals, artificial intelligence sounds like a ‘miracle management system’ I’d call it. With the help of latest healthcare tech companies and mobile apps, you can use machine learning to monitor glucose in your body, deep learning algorithms for nutrition coaching, “personal health companions” for medication reminders, and the Aetna collaborated Apple smart watch optimized with artificial neural networks or risk predictive algorithms to detect development of diabetes in the first place.
It is important to understand that diabetes isn’t a standalone ailment. It can have adverse physical effects such as cataract, nerve damage, and risk of stroke. Instead of depending on humans, an Israeli healthcare startup Medial EarlySign has announced it will use artificial intelligence and electronic health records to determine when a diabetic patient will develop kidney malfunction. AI driven software like VoxelCloud help ophthalmologists easily and quickly to evaluate diabetic retinopathy using automated medical image analyzers like these.
Takeaway: It is easier for patients to use AI apps instead of dealing with the complexities of processes and delayed services in physical hospital settings.
2) A.I. Chatbots Assist Patients With Their Various Health Problems
In this digital world, we can’t ignore the fact that people want instant everything and not just instant noodles. Instead of patients calling the doctor’s to fix an appointment or travel all the way to a nearby hospital just to garner answers, the virtual assistants have become intelligent enough to humanize conversations and give the best possible solution regarding health concerns and symptoms.
Although humans work in shifts to provide 24/7 support to patients, it is more convenient and economical to invest in a bot that never feels tired. These bots don’t necessarily need a separate application like Your.MD but they can reside in these movers and shakers of conversational commerce such as WhatsApp and Facebook Messenger to function.
I wouldn’t call this automation but a step towards a futuristic and simpler life. However, investor and philosopher George Kassabgi argues that artificial intelligence cannot be used in isolation. Health specialist and bots need to liaise to give patients a seamless experience.
Takeaway: Machines are made by man, and they need constant guidance and upgradation to function properly.
3) Machine Learning Algorithms Study Big Data To Extract Patient Insights & Predict Treatments
I remember the days when my dad had countless patient files stacked up on a rack in his clinic, and it took several minutes to search for one particular case. In an era of big data, it is impossible for humans to collect and evaluate terabytes of information regarding healthcare customers.
Thus, artificial intelligence in the form of artificial neural network, adaptive neuro-fuzzy inference system, natural language processing algorithm applications like Human Dx help gather, organize and analyze essential data so that it can be used to offer prescription medicine, make unerring diagnosis, and most importantly to provide “personalized care”.
In addition to dealing with heaps of patient information, machine learning along with deep phenotyping helps in predicting the potential of cardiovascular disease. The partnership of these two techniques help professional in medical research and clinical practice, which ultimately helps patients receive top-notch care.
Takeaway: Artificial intelligence isn’t an intruder in our lives, but a multi-talented assistant that can improve our lifestyle if used in the right way.
Now it is important to realize that artificial intelligence has loopholes, and that machines cannot alone conquer our lives. While we love the benefits of automation provided by this technology, we need to warn ourselves of relying too much on it. We must also always remember that we’re humans and a “sex doll” for instance cannot satisfy us because we’re innately accustomed to humanized interactions. I feel we should balance everything and one way to find equilibrium is to use the help of both humans and A.I. for a perfect yin yang in healthcare.
How Do You Feel A.I. Is Changing Healthcare Customer Experience? Share Your Views.
– Machine learning: a type of artificial intelligence that uses algorithms to collect data and predict a certain outcome.
– Artificial Neural Network: a brain like structure built with interconnected nodes to carry and process information.
– Adaptive Neuro-fuzzy Inference System: a network based on fuzzy logic that redefines human insights into quantitative analysis.
– Natural Language Processing: a computer program powered by AI that understand human (natural) language in verbal form.
– Risk predictive algorithms: a model that evaluates data to predict the risks of (anything) life-threatening diseases like cancer.
– Deep Learning: a type of machine learning and AI that learns unlabeled and unstructured data.
– Deep phenotyping: a method that analyzes subclasses of human physiology and behavior for precision medicine.
– Precision medicine: a model that proposes the customization and personalization of healthcare services for individual patients.