Hello, everyone. I hope you all are having a great time conducting this uh attending this global conference. So today I'm going to talk about artificial intelligence and future of health care. I'm Priyanka. I'm basically from Bangalore. So I have been born and brought up in Kolkata.

So while I was been born and brought up, I grew up over there, I got to realize like um there are certain stories and hidden events which we barely know about Kolkata and those amazing stories I got to hear from my grandparents. While I was hearing those stories, I realized the fact that uh from they have instilled a particular uh kind of education like education is very important for solving any problem in their Children. And finally, that has been passed to us, our grandchildren. So they have always told us education is the answer to solve the greatest problems existing in the world. So uh during the early two thou uh 21st century, while I was passing out of my college uh of my school at that time, it was a time when I had to consider which subject shall I choose. So at that point of time, it has been considered engineering or doctor of the two professionals which can make a person successful in life. And as a result, I had taken up the technical field and there are several friends of mine who had actually gone ahead and taken up the medical field.

So while this 10 years down the line as technical different technical parts have unfolded and I have come come across how technology has advanced with time. I also got to realize uh OK, with the technical advancement, our life is becoming simplified with time. So nowadays, tech technology has advanced to such a level. Today, we are standing in such a position where we can just ask Alexa to book a hotel to book a flight, to place a reminder for our medicine like it acts like just like our assistant. So it has actually simplified a life to a great extent. Now, a couple of years back when I met with one of my friend who is a doctor and while we were having a conversation over a cup of coffee, I got to realize that technology has actually advanced a lot and simplified our normal day to day life. But for the doctors, it didn't their tasks, technology didn't put much impact over there. They have to go through the uh critical structure, flow of their daily routine and they actually require it. That is when it actually I came up with this realization like technology and artificial intelligence, which is a booming technology nowadays has to play and has to come in the healthcare domain. So while I'm in this sector of healthcare domain, let us first analyze what is health?

Health is nothing. If I just do a Google search, I will get to see it is the state of being free from illness or injury. It basically states what is a person's mental and physical condition? So it is very important to understand health by health. We just don't mean the physical health. It is also the men mental health because as the saying goes, a healthy mind dwells in a healthy body and vice versa. Definitely, no, if I show you this picture uh to every one of you, what is this picture? What is the importance of this picture? I do not know how many of you know, but this picture actually is one of the brought one of the revolutionary change in the field of medicine. Why? Because this picture is the first ever picture taken which has given us a peek inside human body. Before that we didn't know how a human body used to look like from inside. So this is the first picture of an X ray and the person who had taken this picture, Sir, William Rogen. He has been awarded with Nobel Prize in the year 1895. So you can understand how important this picture is and how important part this picture has played in the history of medical science. Now, why imaging is so important? Imaging is so important because let us see a scenario before imaging, before emerging came into existence.

Uh people generally used to do any kind of diagnosis based on the symptoms. Now I can visit a doctor and I can tell, ok, I'm having certain pain in my hand and other person can also tell the same symptoms. The doctor based on his experience over time will prescribe the medicine. But imaging actually gave us the first INL look inside a human body uh by which we got to know. Ok, similar symptoms may not have the similar kind of diseases. Diseases might vary at that point of time. As a result, imaging plays a very important part in different kinds of uh in uh determining different kinds of diseases for similar uh uh similar kind of symptoms. And also along with that, it is the main source of research nowadays.

So people, if this amazing wouldn't have happened, people wouldn't have known like, ok, for similar symptoms, different types of diseases existed now the name of this particular uh types of imaging which is present over here. I believe like one or the other we planted with especially X ray.

I still remember during my childhood when I was riding my first bike, I had fallen down and I had to visit a doctor. I had a pain in my hand and he had asked me to take the first X ray of my hand. And that is when I got acquainted with what is X ray. So this is actually I want to show you a scenario how actually imaging has helped us and how machine learning going ahead can help us. So imagine a scenario, a boy and a girl visits a doctor. Now, both of them complain of similar kind of problems that they are experiencing, which is palpitation, chest pain and difficulty in breathing. After analyzing their problems, the doctor won't go ahead and prescribe medicines. First, her work will be to check the vitals of the patients.

He will check the vitals. Let us consider the boy is having different vitals like the vitals might be different from the normal ranges, but the girl is showing similar vitals of normal people. Now, the doctor is a bit confused what is wrong with the girl? Even after showing the similar vital, she is having these problems, she will go ahead and ask the girl to do ecg and echocardiogram. So when the eco reports came for both the patients, the doctor can analyze and see, ok. For the boy, it is a hypertension. He is having a problem maybe due to his lifestyle or the way he work or the stress that he takes in his work, that is where going ahead, he has got hypertension. But for the girl, there is altogether a different problem. The girl is having a condition called hypertrophic cardiomyopathy or HCM. What is this? This is basically the division of the, the world that divides the left and right side of her heart that is little bit thicker in her case than normal people. So all of us have normal ranges of each and everything in our body. But in her case, the muscular wall that divides the left and right side of the heart that is more uh that is more thicker uh thickened in her case in the ventricular sector. So when the doctor is seeing this, he can understand, ok, this is a problem she has got and this is generally genetic in nature. This doesn't have to do anything with your lifestyle, doesn't have anything to do with the bad habits which we are having.

So for all this while she had this problem from her birth, but she got diagnosed with this problem when she's around 20 years old, when she suddenly had started having these symptoms. Now, when the doctor got to know she is having this problem, he will accordingly provide him, uh provide her with different medications. But for the boy, the medication will be different for hypertension. So we can see similar symptoms, having different set of problems over here.

That is where machine learning also plays an important role. How since now the doctor got to understand she is having a genetic disorder, she can ask her parents to get the same checkup done. If her parents will do the same checkup. There can be a probability that one of her parents is also having this disorder from where she has received it. And after doing that checkup it, they got to see, ok, her mother is also having the similar disorder. So now her mother, now people with this kind of problem, they are always at a higher risk of experiencing an irregular heartbeat which can be life-threatening. Now, when now her mother is at around 50 or 55 years old, when doctor got to realize her mother is also having similar kind of problem, but it didn't show any symptom since her age is around 55 years, she is at a higher risk of experiencing an arrhythmia or irregular heartbeat, which can be life threatening.

So to prevent that the doctor will place an I CD or it's an instrument like pacemaker which will be placed over here on the shoulder which will control her from experiencing a life-threatening heartbeat. Now, why is it required a life-threatening heartbeat is something within a fraction of second, it can take away your life, but that I CD will actually help you. Ok. If you are experiencing certain kind of heartbeats, it will give you that relaxation, it will restart your heart within that exact time. What is important over here in the medical field is the right treatment during right time. So I CD, when it is placed over here, it will do the right treatment during the right time, it will. But life-threatening heartbeats are so risky. It will not even give you the time to take the patient from your house to the nearby hospital. So this way it saved us. Now, we can understand this image of the echocardiogram of the heart actually saved not only the daughter's life but also the parents' life. So that is why we told it is a revolutionary change which has been brought by imaging in the medical field. Now what is machine learning and how that is going to impact from this image, machine learning is basically teaching the machine to learn.

So when you're teaching the machine to learn about certain data, like I will feed this kind of data and I will tell the machine, OK. If you get to see these kind of images, you can determine at that point of time that basically this person is having. HCM. No, let us take an example that a boy in Africa is having this problem. Now he's visiting a doctor, the doctor whom he has visited, the doctor didn't get to observe or get to know this kind of patients before. As a result, it it became unlikely for him to diagnose what is the exact problem? Now imagine in case of the doctor, if he had an assistant as a machine learner as a machine or as an application, that machine actually has data globally from my country, India, from USA, from London UK from Germany from different parts of the world. So it can easily determine at that point of time. OK. The problem which the boy is having that is not something uncommon. Many people around the globe had this problem and the symptoms and the image that has been shown up that determines that the boy is having HCM or hypertrophic cardiomyopathy. So we can see how machine learning is actually making the job of the doctor easy over here.

So symptoms based treatments followed by medical images. When medical images are run through different machines, it will definitely revolutionize the health sector in this way, image processing is going to play a very vital role in the part of medicine. Now, the question comes how it is going to be helpful for the doctors. One way we have seen how it can become an assistant for the doctor determining like what is the problem from the images. Also, there is another way like if I built an application where I can keep a track about, I'm, I'm experiencing certain pain in my hand from when am I experiencing that pain? If I can keep that track, uh what am I feeling when that exact pain I'm having? How was my vital is any other thing I'm having along with that pain? If I can keep a track of all these records on an application and I can provide that to a doctor when I'm visiting him after four or five days, it will become so smooth and so easy for him to determine what is my exact problem. And it's a place where machine learning can play an important role. Also, along with that, there are certain cases where it can prove to be very, very useful for the doctors.

And this is something which uh Google has done a survey over here and they had published a paper also on the same thing. So the main problem problem over here is say, suppose a doctor, a patient has gone through a surgery and it's a very crucial surgery that he has gone through like a bi surgery or some transplant or chemotherapy. Or there can be several types of surgery or crucial patients getting admitted TV. Now, what is the risk of that patient getting readmission very soon? How can the doctor determine post surgery that the patient maybe within 56 days he can get sick again? Now, on that patient's previous data of say thousands of records they will be having. So on those data, the machine can learn how the patient has behaved over time and based on it, it can come up with a probability like, OK, in these cases, it will compare like, OK, this particular patient a has this many features of this many problems based on the data points that he has like say he is having mm certain other medicines.

Uh for example, uh patient had a type of surgery but that patient is also having thyroid problem. Along with that his vital signs are not normal and he had been hospitalized twice before. So in this way, we can get certain data which he is already having based on those data, he will compare with other patients data that is already trained on the machine. Those data comparison will give him an idea if those patients, the other patients on whom the data has been trained, if they got readmitted again or not, if the readmission rate is come uh very less. In that case, the rate over here like the chances of re admission, the probability will be very very less if that is very high. Like in case of any transplant patients, in that case, this number will increase. So there these are the signs which actually uh what are naked eyes cannot determine a machine can determine because it has more data inside it. So as I have spoken about physical health for now, equally, mental health is also essential. The first is like the therapist who we visit when we are having a kind of a problem. Mentally, we are we are upset. It is very essential to understand what kind of therapist we are visiting. Many times.

It has happened with me like I have visited the right therapist I believed. But after the conversation, I didn't feel right, I felt like, OK, I need to have a session with some other therapist now. Again, evaluating a session if it is fruitful for the patients or not. And sending feedback to the clinician by this, we mean, say suppose I'm having a conversation with the therapist, but it has been more like a chit chat conversation rather than ex extremely productive conversation. Like he didn't ask me structured questions by from which he can gain certain views about me. So that is also possible. So as a result, this kind of evaluation helps you or will help any clinician or any therapist to understand who the patient or what exactly the patient requires. Because anyway, mental health is such a sphere where people have to work and researchers have to take place more and more even from the choice of words where natural language processing can play a vital role from the choice of words that we make while in the sessions of with the counselors, we can pro provide parameters like if someone is using words like anger or tired or sleep disorder, this kind of words, how many times he is using?

What is the weight of each words based on that we can understand? Ok, he is using more of sleep deprivation. So we can understand, ok. His depression is related to that he is insomnia. This kind of important role machine learning can play in mental health and help the psychiatrist to uh to uh use uh to make all these decisions with the patients extremely fruitful. These are some articles and news from newspapers. I have got, uh, an extremely fruitful one. So now it is Apple Watch has come up with an ECG function where it gets to check if a person's heart beat is normal or not. So, it has actually been proven life saving for several patients. Uh, this is a guy whose picture you can see over here, this person had been admitted in the hospital during the right time. He had actually got arrhythmia or irregular heartbeat, which we call twice and being a responsible citizen, he visited the cardiologist and they did an echocardiogram and got to visit. Ok. He is having a problem for which his valve in the heart is not working or functioning properly and he needed replacement then and there it was an emergency surgery and he had done that and that proved to be fruitful for him and his life got saved.

So in this way, machine learning has proven already started impacting people's lives and saving their lives. With that I want to see and conclude that these are the certain challenges that we are facing nowadays in making or training the computers. So we already know computers are there and advanced technology is there in several hospitals globally. But the problem is this vast amount of patient data need to be fit to the computer so that the computer become as smart as humans. And as a result, a dedicated community should be there who can accelerate and bring more benefits in this kind of work. So that more vision data can be fed in the computer and the model can become more accurate with time. So the vision is to make the doctor's work much easier over time. So that people by sitting at home, they can talk to a doctor along with that um professional or a chatbot. Nowadays whom we talk to that kind of uh help or assistant, we can talk. And as a result, the life of the doctors will become much easier. So that is a vision and I believe with harnessing the power of data and artificial intelligence, that vision can become reality very soon. Thank you.