Folks, In this blog I will going to explain the importance of ML/AI in healthcare sector. First of all, I just want to share some statistics regarding the expenditure on healthcare by the people of different countries. Here is a list of a few BRICS and newly industrialized nations with their per capita expenditure on health.
Here we can see in case of India only 30% expenditure comes from public sector and rest of 70 % from private sector. This is one of the reason healthcare service is costly and affordable service is not available for every citizen.
Let’s see another example:
We can see how much expensive the healthcare system now. The USA has the most expensive healthcare system in world around $10,000 per capita.
Why ML/AI is important in healthcare?
There are many ways ML/AI can solve this problem and make healthcare more affordable for people. Here I’m sharing few observations.
- Hospital error is one of the leading causes of patients’ death. Such errors can be addressed and prevented by Artificial Intelligence.
- Nearly 440,000 Americans die each year due to medical errors which can be easily prevented by AI/ML.
- In the healthcare industry, nearly 86% of the mistakes are preventable and can be easily resolved by using ML/AI. For this reason, In the next 5 years, the AI healthcare market is set to grow by more than 10%.
According to Accenture, artificial intelligence (AI) in the healthcare industry could potentially save $150 billion for the US annually.
As such, machine learning and deep learning algorithms from AI are being rapidly deployed for medical diagnostics, doctor consultations, personalized disease treatments, electronic health records, drug discovery and much more. Amazon’s Alexa provides medical advice for users’ symptoms and can manage blood sugar data for diabetic patients.
How ML/AI can help provide better solutions to healthcare problems?
I’m going to share some well known healthcare problems where ML/AI can play vital role to solve it.
Developing the next generation of radiology tools:
All radio logical images mostly obtained by MRI machines, CT scanners, and X-rays offer
non-invasive visibility into the inner workings of the human body. But many diagnostic processes still rely on physical tissue samples obtained through biopsies, which carry risks including the potential for infection.
Artificial intelligence will enable the next generation of radiology tools that are accurate and detailed enough to replace the need for tissue samples in some cases, experts predict.
Expanding access to healthcare in under served or developing regions:
The shortage of trained healthcare providers, including ultrasound technicians and radiologists, can significantly limit access to life-saving care in developing nations around the world.
Consider the fact that more radiologists work in the half-dozen hospitals lining the renowned Longwood Avenue in Boston than in all of West Africa. This clearly indicates the acute shortage of healthcare professionals in the region.
Artificial intelligence could help mitigate the impacts of this severe deficit of qualified clinical staff by taking over some of the diagnostic duties typically allocated to humans.
Creating more precise analytics for pathology images:
70% of all decisions in healthcare are based on a pathology result. So, the more accurate the results,the sooner we get to the right diagnosis. That’s what digital pathology and AI has the opportunity to deliver.
AI/Analytics that can drill down to the pixel level on extremely large digital images can allow providers to identify nuances that may escape the human eye.
Monitoring health through wearable and personal devices:
Almost all consumers now have access to devices with sensors that can collect valuable data about their health. From smartphones with step trackers to wearable that can track a heartbeat round the clock, a growing proportion of health-related data is generated on the go.
Collecting and analyzing this data and supplementing it with patient-provided information through apps and other home monitoring devices can offer a unique perspective into individual and population health. We can create a personalized health care report of each person with this data.
We will talk about ML/AI application in healthcare in more deep in Next part , so stay tuned.
happy learning !!!!
References:
https://hackernoon.com/how-ai-is-transforming-the-future-of-healthcare-industry-f6020cc18323
https://techburst.io/ai-in-healthcare-industry-landscape-c433829b320c
https://www.statista.com/chart/8658/health-spending-per-capita/
