Healthcare industry has benefited tying up with Machine Learning, automation and Artificial Intelligence (AI). These three main factors have advanced the types of machinery of hospitals, insurance companies and help doctors understand the root cause of diseases with in-depth analysis tech. According to an industrial source, the healthcare industry will spend an average of $ 54-55 million on AI projects. Compiling and analyzing information like medical records, patient case history (past and present) is done with the application of AI networks and digital automation process. The data and information of a patient is collected, stored, formatted, re-formatted, and traced within seconds to provide faster and efficient access.

The idea of letting a computer take decisions that have an immediate and direct impact on a patient's medical health -- not too easy to digest. But then, think about it, is it not already happening? Computers are already everywhere in the medical domain, and machine learning (for all practical purposes, that's what AI is, in the scope of this article) is the next logical step. The idea is -- let's draw hope, confidence, and inspiration from all the positive impact that computerization, in general, has had on the medical field. To make our case stronger, let's go through some awesome health care medical applications powered by machine learning.

A few years ago, Timothy Bickmore, a computer scientist at Northeastern University, developed an artificial-intelligence program to help low-income patients at Boston Medical Center prepare for their return home from the hospital. The virtual nurse, alternately called Louise or Elizabeth, was embodied as an animated figure on a screen. It began by asking patients whether they were Red Sox fans, then walked them through what they should do after they were discharged. This medication is for your stomach. You will take one pill in the morning.") Bickmore has since created a slew of these programs--an A.I. couples counsellor, an exercise coach, a palliative-care consultant--all aimed at disadvantaged clients. "It's where we think we can have the most impact," he told me recently. "Hopefully, the A.I. is better than nothing." It sounds like a classic techno-dystopia--human warmth displaced by a cold computer, one made somehow worse by the patronizing nod to local-sports fandom.

The mobile health applications have taken the driver seat in the healthcare landscape that's making everything possible at the flick of the button effortlessly. Now, the AI technology integration is making the mHealth solutions- The Hospital of The Future. Yes, it's not a tale of a distant future where patients don't need to physically step inside the hospital to get the doctor consultation, while the virtual assistants on the mobile app book the appointment with the doctor on your behalf, design treatment plans for you after diagnosing the health, make advice for the speedy recovery along with a list of to Do's and Don'ts, even identify the errors in the medical test reports and so on. It sounds like a fiction, but in reality, it's not. Artificial intelligence is making the world so small that it can be encompassed in the palm of your hands.

Khosla Ventures and Data Collective backed 5 startups each. The number of startups entering the healthcare AI space has increased in recent years, with over 50 companies raising their first equity rounds since January 2015. Deals to healthcare-focused AI startups went up from less than 20 in 2012 to nearly 70 in 2016. Last year also saw two new unicorns emerge in the space: China-based iCarbonX and oncology-focused Flatiron Health. "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries."

Technological change is already shaking up the profession. What is your relationship with technology going to be? Picture this: a patient walks into the emergency department and sits in front of the "triage nurse" -- a computer that uses advanced algorithms to ask questions based on the patient's answers. A robot draws the patient's blood. Another one computes real-time nurse schedules and bed availability to decide if the patient can be admitted. "Follow me," the robot says.

The number of startups entering the healthcare AI space has increased in recent years, with over 50 companies raising their first equity rounds since January 2015. Deals to healthcare-focused AI startups went up from less than 20 in 2012 to nearly 70 in 2016. Last year also saw two new unicorns emerge in the space: China-based iCarbonX and oncology-focused Flatiron Health. "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries." We identified over 100 companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things.

Increasingly crowded imaging & diagnostics: 17 out of the 22 companies under imagining & diagnostics raised their first equity funding round since January 2015 (this includes 1st Seed or Series A rounds, as well as a first round raised by stealth startup Imagen Technologies). In 2014, Butterfly Networks raised a $100M Series C, backed by Aeris Capital and Stanford University. This was the third-largest equity round to AI in healthcare companies, after China-based iCarbonX's $154M mega-round and two $100M raises by oncology-focused Flatiron Health. VCs invest in drug discovery: Startups are using machine learning algorithms to reduce drug discovery times, and VCs have backed 6 out of the 9 startups on the map. Andreessen Horowitz recently seed-funded twoXAR, developer of the DUMA drug discovery platform; Khosla Ventures and Data Collective backed Atomwise, which published its first findings of Ebola treatment drugs last year, and has also partnered with MERCK; Lightspeed Venture Partners invested in Numedii in 2013; Foundation Capital participated in 3 equity funding rounds to Numerate.

In our quarterly analysis of companies pursuing healthcare-focused applications of AI, we reported that deals leapt from less than 10 in 2011 to 60 in 2015. So far this year (as of 8/23/2016), companies in this space have raised over 55 equity funding rounds. Some of the recent deals include a 25M Series A round raised by London-based health services startup, Babylon Health, backed by investors including Kinnevik and Google-owned DeepMind Technologies (Babylon will reportedly roll out a Siri-like voice recognition interface this year), and a 154M Series A round raised by China-based iCarbonX. "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries." We identified over 90 companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things.

In our quarterly analysis of companies pursuing healthcare-focused applications of AI, we reported that deals leapt from less than 10 in 2011 to 60 in 2015. So far this year (as 0f 8/23/2016), companies in this space have raised around 55 equity funding rounds. Some of the recent deals include a 25M Series A round raised by London-based health services startup, Babylon Health, backed by investors including Kinnevik and Google-owned DeepMind Technologies (Babylon will reportedly roll out a Siri-like voice recognition interface this year), and a 154M Series A round raised by China-based iCarbonX. "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries." We identified over 80 companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things.