Artificial intelligence (AI)has the potential to improve every aspect of our lives and help us transform healthcare. Let’s have a look at how healthcare is practiced today and how AI is transforming it. Healthcare implies keeping the health of an individual up to the mark or improving it. It covers injuries as small as paper cuts to blood cancer.


Healthcare can be divided into three categories, namely the following.

  • Curing
  • Preventive
  • Predictive

We can use the huge amount of data produced every day to find a better cure for a disease, find new drugs, and even predict the probability of a disease long before any symptoms relating to it are observed.

Healthcare industry problems

The problems of the healthcare industry can be divided into two broad categories. One category of the problem arises from the sociopolitical and financial issues, while the other arises from the technological challenges in the industry. Issues like shortage of beds, shortage of healthcare workers, and unqualified medical practitioners belong to the first category. The second category contains issues like slow research, human errors in analyzing data, and the lack of data transparency among the organizations.

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AI to improve healthcare

Artificial Intelligence offers an amazing opportunity to transform the world in a huge manner. It has been called as the new electricity by Andrew Ng. It has the potential to touch every person’s life in a meaningful way, just like electricity did.

In healthcare, AI can help in improving each step of the ecosystem. From the prediction of disease to finding a new drug to making all new gene modifications.

AI-Healthcare ecosystem

Imagine a scenario where a couple is about to get married. An AI system can check the compatibility of their genes to figure out if there is any risk to the child or some gene that can result in a complication in the child’s normal life. This system can then help in figuring out the right measures that can be taken before and after the baby is born.

AI in action

Digital Diagnostics using Computer Vision

Currently, a lot of diagnostics require a trained professional to analyze samples of blood, saliva, tissues, semen, etc. under a microscope. This is very time consuming and error-prone. Dedicated machines exist for different tests, but a cheaper solution is possible using AI.

Digital diagnostics use computer vision technology to analyze images of these samples and then apply algorithms such as ANN and CNN to figure out the size shape and movement of cells. This data is then used as the features to train a machine learning model to find the problems that the patient might have.

Predicting Spread of Virus Outbreaks

Various machine learning models have been used to predict the spread of viruses and other infectious diseases. Social media data from platforms like Facebook, Twitter, etc. are used to fit regression models to predict areas of next outbreaks.

Patient flow optimization

We can use data like the number of patients per hour visiting the hospital, current weather conditions, and common injuries to predict the number of patients that might come to the hospital on a given day. This intelligence is useful for medical centers to optimize their supplies and be better prepared for emergencies.

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 Personal Doctors

Advances in Natural Language Processing has made it possible to create smarter chatbots to help patients at any hour of the day. A user can simply type in the common symptoms that she is facing, and her chatbot will tell her if she should see a doctor or not. The assistant can also book an appointment with the doctor automatically based on the urgency of the situation.

Ethics in Healthcare

Ethics is one of the most important pieces of the puzzles when we are talking about AI in healthcare. I leave it to the reader to think about the following scenarios and realize how complex it could get when we have intelligent machines making decisions for us.

  • Who owns your data? The Electronic Health Record(EHR) that your hospital has belongs to you, but should you be allowed to take ownership of it? What if you had a very rare disease and your data is of prime importance, should the society be allowed to use the data even though you don’t want it?
  • Suppose the AI system finds out that you are very likely to have a type of cancer that is incurable. Would you like to learn about it? Think about the emotional toll it can have on the person.
  • What if the predictions made by AI were wrong. Who should be responsible for that, is it the developer who coded it or the organizations that made the system or the data that was used to make the system in the first place?

AI in healthcare has a huge potential if we can solve some of the aforementioned issues. We see tremendous advancements in the area, and most of the things described in this article are not as fictional as they sound.