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#4 Ways AI is Reshaping Healthcare in India So far, the most significant application of AI is to understand how DNA impacts life

By Dipendra Jain

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The healthcare technology sector has given rise to some of the most innovative start-ups globally. They are poised to help people live longer and improve their quality of life.

These innovations are backed by software and mobility, enabling the sector to digitize numerous convoluted processes, which currently slow down the services.

More recently, software is becoming more intelligent and independent. Studies being carried out under the domain of Artificial Intelligence and Machine Learning are increasing the speed of innovation in healthcare.

So far, AI is trying to solve some of the biggest healthcare challenges in areas such as:

  • Personal genetics
  • Hyper-targeted drugs
  • Diagnosis of new diseases
  • Consumerization of healthcare

A closer evaluation of opportunities existing in each area shows that the stakes are high. The first ones to build and market a sustainable product with value addition will benefit tremendously.

  • Leading a New Era of Personal Genetics

So far, the most significant application of AI is to understand how DNA impacts life. While we were able to decipher the complete sequencing of the human genome and read and edit it, we are yet to know what most of the genome is telling us. Genes are often acting out of patterns and are influenced by variables such as food, body types and surrounding environment.

With systems such as Google's Deep Mind and IBM's Watson, it is possible to process large amount of data such as patient records, clinical notes, images and treatment plans and carry out pattern recognition in a short time period – which without these advents would have taken a lifetime to process.

Mapmygenome is a pioneer in this field. The customized diagnostic tools help you understand your genetic makeup.

The AI technology employed here must have access to huge amounts of data to better understand the lifestyle changes happening in a person. The technology should conduct behavior pattern recognition to filter the information which is valuable and the information which is damaging.

  • Hyper-targeted Drugs for Precise and More Personalized Medicines

An exciting application of AI is reduction in the cost and time of drug discovery. Typically a new drug takes 12-14 years to come to the market, with the cost going around $2.6 billion. This process includes testing the drug molecule against a combination of cell types, genetic mutation and all other conditions related to a particular ailment.

This task being time-consuming limits the number of experiments the scientists can perform and the diseases that can be attacked.

AI algorithms enable computers to learn to make predictions based on the data previously processed. Such algorithms predict the side effects the molecule may have on humans, for speedy approvals.

Pune and Frankfurt based Innoplexus uses artificial intelligence to significantly cut down the drug development time, from synthesis to approval. They help life science companies generate actionable insights during pre-clinical, clinical, regulatory and commercial stages of drug development. The insights are gathered from structured and unstructured private and public data.

The future of drug discovery looks promising, with the application of AI. A 2015 Google Research paper observes, "artificial intelligence and machine learning has a significant potential to accelerate drug discovery and improve human health. Data from various sources can better determine the chemical molecules which will serve as effective drug treatment for a particular disease." This way, AI will save a lot of time in testing millions of molecules.

  • Diagnosis of New Diseases

Most diseases involve more than a mere gene mutation. Despite copious amount of data being generated and stored in the healthcare system – which is gradually improving in quality – previously, we did not have the necessary hardware and software to analyze and convert it into meaningful insights.

Disease diagnosis is often complicated, involving factors such as the amount of sugar one consumers per day to the abnormality in the body part or fluid. Since thousands of years, medicine is dependent on symptomatic detection –the disease is diagnosed based on the symptoms displayed (for instance – if you have a running nose and fever, it is most likely to be flu).

But often, detectable symptoms appear too late, especially in diseases such as cancer and Alzheimer's. With AI, there is hope that the faint signatures of such diseases will be discovered well ahead of advanced symptoms to increase the probability of survival.

NIRAMAI (Non-Invasive Risk Assessment with Machine Learning and Artificial Intelligence) uses a combination of ML, AI and cloud-based screening to solve the problem of access and the cost of breast cancer screening. Their low-cost device takes high-resolution thermal images with no exposure to radiation. Their patented Themalytix technology uses thermography to detect tumors five years earlier than mammography or clinical tests.

  • Consumerism in Healthcare

Another big shift we're noticing is access to primary care. Technology is removing the location barrier by allowing patients to access doctors online, to reduce the travel and wait time. This is particularly useful for emerging countries traditionally facing issues with access and costs.

Many of these products are combining AI and digital tools to manage certain diseases with remote consultants and physicians.

What does this mean?

What was once impossible in healthcare is now being driven towards tangible reality with AI.

For AI to become more prevalent in healthcare, constant access to relevant data is essential. The more exclusive data the algorithm can digest, the "smarter" it will become. Companies are taking additional measures to acquire data residing in an anonymized format. For example, in February 2016, IBM bought healthcare analytics company Truven Health.

With data becoming richer and the technology advancements, opportunities are skyrocketing for entrepreneurs to find new ways to improve our health and well-being.

Dipendra Jain

Founder, OnliDoc

Dipendra Jain is the founder of OnliDoc, an AI-driven and Machine Learning platform for integrated end-to-end diagnosis. He is a software veteran with 12 years of experience and broad expertise ranging in product management to coding. He has worked in most of the best tech companies in delivering world class products in various roles. He is also the founder of LiteLabs, a software development firm with branches in Jakarta, Singapore, and India.


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