Eliminating a disease is good. But what if we could actually reduce the likelihood of the disease? 

With the help of emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML), healthcare professionals are planning to do exactly that - predicting the likelihood of disease in the future. How and why do they do it? Before we begin to ponder on that question, let us first explore what AI is and what is its scope in the healthcare industry.

What Are AI And ML?

ML can be classified as a branch of AI. The dictionary definition of ML is, “a branch of artificial intelligence in which a computer generates rules underlying or based on raw data that has been fed into it.”

AI refers to the ability of a computer or machine to mimic the capabilities of the human mind—learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems—and combining these and other capabilities to perform functions a human might perform.
Machine Learning uses various techniques that enable computer programs to ‘learn’ by processing extensive amounts of data. It recognizes patterns, develops models, and makes decisions with minimal human intervention.

Author of Lucky Years: How to Thrive in the Brave New World of Health and The End of Illness David B. Agus (M.D.) says that “We have lots of data that we've been collecting over decades. For the first time, computing power allows us to use the data in a way that benefits the patients.”

A Look At The AI Market:

When it comes to the incorporation of AI and ML in healthcare, VP Analyst of Gartner Christian Titze had this to say:

“Appropriate predictive and prescriptive analytics (including machine learning and AI) would be applied to the digital supply chain twin so that aligned (and to some degree, automatic) decisions could be made.”

One report in Gartner estimated that by the end of 2024, 75% of enterprises will have operational AI.

A report from WIRED from 2020 estimated that the AI healthcare market will rise from $1.3 billion in 2018 to $13 billion in 2025 at 41.7% CAGR.

Although the healthcare industry was adopting AI in its operations, COVID-19 has boosted that growth, which is evident from the fact that the top 50 firms operating in the healthcare AI domain have already received $8.5 billion funding by January 2020. [McKinsey]

In 2016, Frost & Sullivan estimated that the AI healthcare market would grow from $0.66 billion in 2014 to $6.7 billion by 2021. This forecast turned out to be underestimated as that goal was already surpassed in 2020 itself.


Scope Of AI In The Healthcare Industry:

The current ongoing pandemic has demonstrated that when it comes to the collection and analysis of big data, AI and ML can play a crucial supplementary role. It has helped to fast-track the vaccines by simulating plenty of possible situations.

Machine Learning Algorithms enable the doctor to make better decisions driven by data, thus providing them with a holistic picture.

It is no secret that AI and ML provide pharma companies access to important insights into sales, supply chain, and production management, among other things. Analyzing the data with advanced analytic techniques opens up a lot of new possibilities in businesses. The analysis of customer health data allows researchers to understand future scenarios better and prepare for them.


Early Diagnosis Of A Disease:

Simulating a disease and its outcomes play an important role in testing a drug and discovering an effective vaccine.

Today, it is fairly possible to say whether a person has a risk of blurry vision by analyzing the white part of the eye. It is promising to know that AI and ML get only better over time. 

Researchers harvest a large number of datasets of patients’ diets and medical history and various diseases and utilize AI to analyze and interpret aggregated patient data to ultimately forecast the impact of certain diseases on specific geography or person. It is vital to get an early diagnosis and prepare for better treatment. These types of forecasts help plan the supply chain in accordance with the predicted demand.

This paves the way for preventive medicine, which is a branch of medicine that promotes activities to prevent the occurrence of disease.

Chatbots:

Chatbots are useful for patients to self-diagnose, and it can assist doctors in diagnosis also. The bot is fed with lots of relevant health data and is backed by natural language processing (NPL), then it takes input from the patient, and based on that relevant health information is provided.

Robots:

Robots are useful when the same repetitive actions are to be done. ML helps robots to assist surgeons by identifying patterns and making models based on them.

Augmented and Virtual Reality (AR & VR):

ML algorithms enhance AR and VR programs, which in turn provide advanced medical imaging. These algorithms also enhance 3D modeling. According to research by Frost & Sullivan, healthcare outcomes can be improved by 30 – 40% with the help of AI.


Discovery of Drugs:

Many software has demonstrated their extraordinary abilities using AI. Such as one program could successfully predict how proteins fold into 3D shapes. This means that researchers can discover mechanisms behind some diseases and design personalized medicines for individual patients.
This type of forecast assists the initial screening of drug substances by quickly measuring the RNA and DNA of those compounds.


Identify Right Candidates for Clinical Trials:

Getting the right candidate for the trial of any drug is critical. AI helps to identify suitable candidates by looking at their medical history, disease condition, infection rates, demographics, age, and other factors.


Conclusion:

Our online pharmacy app developers, at EMedStore, have seen that AI in pharma, as well as healthcare, offers a variety of benefits. Leveraging AI for pharma & healthcare is one of the most promising areas.

AI will usher in a new area of healthcare that focuses on quality by providing a new set of tools that make healthcare professionals more efficient.