The importance of Artificial Intelligence in the Indian healthcare system

There are multiple ways in which artificial intelligence (AI) can be leveraged to reimagine the workflows in the healthtech industry as a whole, including drug discovery, pre-emptive detection, and intelligent documentation.

Artificial intelligence has very promising applications when it comes to the healthcare industry. AI in healthcare has seen a wide range of applications ranging from analysis of radiographs, predicting the operation needs at healthcare facilities, and medical departments.

Some AI applications aim at helping pathologists and histologists diagnose patient samples, assist physicians in surgeries, setting drug doses, and reducing dosage errors in chronic diseases to name a few.

Presently, the world of healthcare is living with an enormous amount of data that has arisen from laboratory tests, clinical and physiological observations. Artificial Intelligence in healthcare has recently started picking up steam. There are multiple ways in which AI can be leveraged to reimagine the workflows in the healthtech industry as a whole.

Machine Learning systems have been present well from the 1990s, but large scale applications have just started picking up steam. This large-scale adoption was fuelled by a set of accelerants. The top three accelerants according to me are:

Creation of lightweight stochastic models

Neural networks (thought of as the building blocks of ML models) have increasingly become more powerful and lightweight to run with regard to the compute requirements. This has enabled complex models to be trained and deployed.

Availability of healthcare data

As a significant number of hospitals go digital, a lot of medical history and case specific data is also available digitally. This enables a whole new set of Machine Learning models that can infer higher order information from that seemingly unclustered data.

Availability of edge data

Nowadays, almost everyone wears a fitness tracker. This collects a lot of information about a person's walking style, sleeping patterns, pulse rate patterns and much more. This is a lot of data that can be leveraged to create personalised machine learning models to aid and/or suggest changes in the lifestyle of a person.

Artificial Intelligence has seen a plethora of applications in healthcare. These applications are now making the change from just being present as an idea in a controlled test environment to being developed into full-fledged production systems.

Drug discovery

Artificial Intelligence has seen good applications in this domain. A lot of startups have sprung up who aim at providing cheaper and effective ways to access protein folding. This has also opened up a new realm of possibilities where instead of using the brute force approach by simulating hundreds of molecules on a particular virus or bacteria which is operationally expensive and time consuming, the drug actions can just be simulated using an AI based molecule interaction system. The recent pandemic acted as a catalyst for enabling a lot of algorithmic drug discovery startups.

Pre-emptive detection

Various machine learning models are being created that can predict the possibility of a particular disease before it is even detected in a patient. This relies on the historical data as well as data on the lifestyle of the patient. This technology is still in its infancy, but expected to hold a massive uprise in the future.

Intelligent documentation

Documentation is a very big part of the process that needs to be handled by a human at the moment. In some cases, documentation has seen to increase the time required to process a discharge or document a procedure, etc.

Artificial Intelligence is helping speed up documentation in the following ways:

1) Natural Language Structuring

Where the doctor and patient have a verbal interaction and an AI agent is fed the audio of the interaction. This AI agent structures the said content info formats accepted by the hospital record keeping system (HIS).

2) AI Aided Documentation

Refers to using state of the art tools to aid the doctor in their documentation workflows. Doctors can use AI assisted dictation systems, AI guided radiology reports, etc., for efficient documentation.

3) AI Assisted Surgery

A lot of surgeries require complex invasive procedures that can be reimagined using AI. The action mechanism of a particular drug, reducing invasion in a procedure, using precise robotic equipment to perform surgical operations are some applications in this domain.

Anomaly detection

Machine Learning models have increasingly become very good at detecting anomalies or abnormalities in a time series dataset.

Inappropriate medicine quantity, non-compatible medicine intake, abnormalities in time series reports can be detected by using the correct machine learning models.

Trend prediction and risk analysis

AI can be used to a good extent to predict the trend of a particular series. For example, in the recent pandemic wave, AI was used to predict beforehand and make corresponding preparation with regards to the infrastructure etc.

Overall, AI in healthcare is just getting started and has a wide range of possibilities in the future. We are at the crux of a big revolution in healthcare, which will redefine the way healthcare is thought of and approached by the masses.

Edited by Megha Reddy

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)


Updates from around the world