AI in Healthcare

“Health is a new Wealth!” This phrase hits the right chord and tunes our perspective on ensuring a healthy life. But to err is human; we tend to slip away from the path to a healthy life. The Healthcare industry has always led the road to innovation. With the best minds working on making proper healthcare affordable and available to all, the initiative focuses on implementing AI to further disrupt the medical landscape.

AI promises to skillfully diagnose disease in various medical images and on the data of the scans. With revolutionary advancements in AI and deep learning, these technologies may be more efficient in diagnosing diseases in the upcoming years. It is not just limited to diagnosing but also used for possible treatment.

AI has made immense progress in healthcare and pharma efficiency. This blog outlines the top 4 advances of AI in medicine today:

#1 Effectively Diagnose Diseases

It takes years of medical training to diagnose a disease accurately, and even with all the training, the diagnostics are often tedious and time-consuming. In many areas, the demand for professionals far exceeds the ready supply. This puts experts under stress and often results in delayed life-saving diagnostics. Deep Learning algorithms have made tremendous progress in automating diseases’ diagnosis, making them affordable and more convenient.

How it Functions:

Machine learning models in healthcare systems can predict patterns as same as a doctor would predict. The main difference is that algorithms require a lot of concrete examples to be trained upon. Machine learning mainly helps in areas where diagnosis data is readily available from a doctor and provided that it is digitized. For example, ML algorithms are used in detecting lung cancer or future strokes based on CT scans.

#2 Faster Production of Drugs

Developing drugs is a highly expensive process, plus it requires years of tests. Many of the basic analytical processes of drug development are made more efficient with Machine Learning capabilities. These ML models can cut down years of work and budget in hundreds and millions. Various drug development stages, such as identifying drug candidates, performing preliminary tests, identifying targets, and much more medical practices before and after the drug discovery, use AI.

How it Works:

Machine learning algorithms can help experts analyze and understand a particular disease’s biological origin, including its resistance mechanisms. These algorithms work on the available data and can even have the ability to learn and automatically identify feasible target proteins. They also help speed up clinical trials while it’s too hard to find a suitable candidate for the desired test. Algorithms can ease the process of identifying patterns that separate the right candidate from the wrong ones.

#3 Personalized Treatments

Different patients respond to treatment and drug doses differently. Therefore, personalized treatment has immense potential to boost a patient’s lifespan. But it is very tricky to identify which variables affect the choice of treatment. Machine learning algorithms can easily automate this statistically complicated task and help discover which factors help identify a patient’s response to a specific treatment.

How’s it Done:

The machine learning system can be trained by cross-referencing similar patient’s records and analyzing their treatments and post effects of the treatment. The outcome of these test’s predictions makes it very easy for a doctor to devise the right treatment plan.

# 4 Enhanced Gene Editing

CRISPR – Clustered Regularly Interspaced Short Palindromic Repeats is one of the medical industry’s most significant achievements in cost-effectively editing the DNA. This method mainly depends on short guide RNAs to identify and edit a particular portion of the DNA. And as the guide RNA can accommodate in multiple DNA locations, it leads to accidental side effects. The careful selection of guide RNA with minimal side effects is a significant drawback in treating the CRISPR system.

Work Procedure:

Machine learning algorithms can generate the best outcomes for identifying the degree of interactions and off-target effects. This can highly speed up the development of guide RNA for every part of the DNA.

Key Takeaways:

AI is already disrupting the medical industry in developing an efficient diagnosis of diseases, producing drugs, personalizing treatments, and editing a particular gene structure.

As of now, there are not any drug treatments readily available on the markets that were developed by AI, but several companies are working tirelessly to make it a reality. Patient data collection and testing will continue to drive progress. While these are great strides in advancing artificial intelligence to work with medical professionals to save lives, it is far from main stream. But this is just the beginning. The more our medical data is digitized and unified, the more we can use AI to find valuable patterns-patterns that we can use in complex analytical processes to make precise, cost-effective decisions.