7 Real-World Applications of AI in Healthcare
Solution consultant at Sahaj Software Solutions
Artificial intelligence (AI) has been one of the main talk of the Town of Technology for the better part of the last decade. And, just like many other much-hyped topics, people are divided here too. We are already aware of the capabilities of AI; some of them are already proven. There are many data scientists, engineers and developers working to build the good side of AI, using it to make the world a safer place. I believe that to prove AI and see widespread adoption of advanced AI technologies could be a real boom to mankind, should we choose to use it's powers for good...
For now, let’s focus on how AI is used for the improvement of healthcare.
The AI-driven medical systems industry is set to grow up to $6.6 billion according to an Accenture report. The role AI can play in personal fitness is something which is being brought to notice recently. With AI, neural networks can process masses of raw data and learn how to organize that data using the most important variables in predicting health outcomes.
The use of AI and the Internet of Medical Things (IoMT) in consumer health applications is already helping people. AI increases the ability for healthcare professionals to better understand the day-to-day patterns and needs of the people they care for, and with that understanding they are able to provide better feedback, guidance and support for staying healthy.
- Freenome uses molecular biology aided with machine learning for the early stage cancer detections. Model can be trained to learn which biomarker patterns signify a cancer’s stage, type, and most effective treatment pathways. AI can help to decode hidden patterns ultimately to recognise disease-associated patterns.
- Propeller Health is another successful AI system which can detect asthma attacks based on patient medication data and environmental conditions. Propeller tracks every dose automatically and learns about your asthma. Propeller makes it easy to track, understand and manage your asthma. Propeller helps you connect the dots and see how your triggers and symptoms are changing over time.
- Using pattern recognition to identify patients at risk of developing a condition – or seeing it deteriorate due to lifestyle, environmental, genomic, or other factors – is another area where AI is beginning to take hold in healthcare.
- Despite the wide usage of digital mammography, spotting and diagnosing breast cancer early remains a challenge. Google health uses AI to improve breast cancer screening. It showed that its AI system is capable of surpassing human experts in breast cancer prediction.
- Kardia has developed a cost-effective EKG wearable. It enables you to take a medical-grade ECG in just 30 seconds and you can know anytime, anywhere if your heart rhythm is normal. Wearable collects vast amounts of EKG data from the users and it is then processed through an algorithm which detects atrial fibrillation. The FDA has in fact cleared Kardia’s system as a valid measure for the detection of this cardiovascular condition.
- Diabetic retinopathy - an eye condition that affects people with diabetes - is the fastest growing cause of blindness, with nearly 415 million diabetic patients at risk worldwide. The disease can be treated if detected early, but if not, it can lead to irreversible blindness. Google research team has built a deep learning algorithm capable of interpreting signs of DR in retinal photographs, potentially helping doctors screen more patients, especially in underserved communities with limited resources.
- AI can identify a measure of clinical similarity between patients. This allows researchers to create dynamic patient cohorts, rather than static patient cohorts. It also enables an understanding which care path works better for a given group of patients.
Artificial Intelligence can analyse large amounts of data and turn that information into functional tools that can assist both doctors and patients.
The increased integration of AI into everyday medical applications might improve the efficiency of treatments and lower costs in various ways.
AI like most of the technologies out there is value neutral, and it’s only how they’re implemented and by whom that makes them either good or bad.
With any new technology there’s a danger it could fall into the wrong hands. It’s up to all of us to ensure that AI is developed responsibly for social good.
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