Machine Learning (ML) and Artificial Intelligence (AI): Healthcare – Part Six by Dr. RGS Asthana Senior Member IEEE

Machine Learning (ML) and Artificial Intelligence (AI):  Healthcare – Part Six

by

Dr. RGS Asthana

Senior Member IEEE

Figure 1: ML and AI in Healthcare [11]

Summary

ML, AI, mobile technology, Big Data and Robotics and many other technologies are being applied to Healthcare in a major way and are showing their impact almost in all areas related to Healthcare. We have reviewed this field and divide the applications in three major areas:  first Tele-medicine and Tele-health apps. These apps came first and are really low hanging fruits. The second category was for improving service delivery and exploiting Robotics and 3D printing technologies for benefits of the human being.  The third category included apps for personal genetics, drug discovery and disease identification and management. All three categories used AI and ML to some degree. The third used AI/ML in a major way and exploited Big Data techniques too. We expect that multi-disciplinary approach to healthcare will grow further in coming years and will show amazing results.

Keywords

Machine Learning (ML) Tools, Artificial Intelligence (AI), Neural Networks, Internet of Things (IoT), Deep Mind, IBM’s Watson, Deep Genomics

Prelude

ML, AI, Mobile Technology, Big Data, 3D Printing and Robotics are playing very important role in healthcare (see figure 1). The primary goal of healthcare is to help people live longer and live better and peaceful lives and this goal has led to the most innovative startups in the world. We do describe here a few apps which are established but need to be revamped with application of ML and AI techniques.  We also cover some of the important apps developed using AI and other upcoming technologies.

The improvements in service delivery is obtained by following multi- disciplinary approach and by automating manual processes by digitizing data to make it usable by the software and also exploiting smart phone technology available ubiquitously. Further, we refer to a few apps which use 3D printing and robotics with AI to further improve healthcare for the benefit of patients by making prosthetics and human body parts available on a customized basis hopefully in very near future at affordable rates.

The use of ML and AI techniques—are accelerating the pace of innovation in healthcare and motivating growth also in the following areas:

·         Personal genetics

·         Drug discovery

·         Disease identification and management

Biotech companies such as Cloud Pharmaceuticals [32] and Berg [24] are combining AI and big data to identify new drug compounds.   Johnson & Johnson [33] and Sanofi [31] are using IBM’s Watson [20] to find new targets for FDA approved drugs.  Johnson & Johnson’s [30] 3D Printing Center of Excellence is working to change healthcare through 3D printing innovations.  In fact, companies are competing among themselves to gain the first movers advantage to market with a sustainable product.

Healthcare Apps

Tele-medicine and tele-health apps

These are making healthcare more beneficial to individual wellness, are less expensive to use and cater mainly for   preventive aspects. These applications need to be further enhanced using new AI techniques particularly using NLP.

Doctor on Demand

It is a medically certified mobile application which permits a Certified professionals {read doctor} to treat non-emergency routine medical cases through video consultation such as cold and flu symptoms, sports injuries, heartburn, respiratory infections and allergies, urinary tract infections as well as many pediatric issues. The doctor can prescribe medicine for travel purposes and refill existing prescriptions.

Microsoft HealthVault [34]

This healthcare service allows facility to store personal health information and let customers participate in new ways which are safe and up to date. The health information can only be shared with healthcare bodies, e.g., hospitals, pharmacies, and lab testing companies

You can either enter your health data manually or have it done automatically by simply connecting HealthVault to any medical device, such as, fitness tracker, Wi-Fi bathroom scales, and other apps. Using ML and analytics, HealthVault is designed to generate new comprehensions about patient health, ensures adherence to care plans, and encourages patient commitment.

Epocrates

This is a mobile application for pharmacists and is HIPPA compliant. It has comprehensive medical information, including drug interaction checker and a pill identification tool, texting service that allows physicians and other hospital personnel to text each other with convenience. It keeps doctors up to date on important news stories, essential resources, clinical guidelines, ICD-10 codes, disease information and other important information.

PingMD

It is basically a messaging app. It allows medical professionals to connect with other specialists as well as with their patients.   It is HIPAA compliant and also lets a patient connect with his/her physicians the same way one might communicate with a friend via text messaging or image sharing.

Apps Developed using AI

Heart Attack Forecast 

It is very difficult to predict Heart attacks.  An AI system [10] based on routine medical data can forecast which patients will have heart attacks within 10 years.  

Figure 2: Heart Attack Prediction using AI [15]

During a new study, Stephen Weng, epidemiologist at the University of Nottingham, U. K. (see figure 2) compared four machine learning algorithms (viz. random forest, gradient boosting, neural networks, and logistic regression) to the ACC/AHA guidelines. The training of the AI system was done by searching through nearly 300.000 medical records from 2005 to not only find patterns associated with cardiovascular events leading to heart failures but also to predict which patients would suffer their first cardiovascular event within the next decade and then compare   answers to the 2015 records.  Each of the four AI methods executed better than the ACC/AHA guidelines, however, the best results came by using neural networks as it correctly predicted 7.6% more events and raised 1.6% less false alarms than the ACC/AHA guidelines. However, no AI technique (out of the four used) projected diabetes as the reason although the guidelines show this as one reason. 

Lung Cancer Identification [12]

A team of experts from the University of Warwick, Intel Corporation, the Alan Turing Institute and University Hospitals Coventry & Warwickshire NHS Trust (UHCW) is formed to use AI and examine pathology images of lung and arrive at computer-assisted diagnosis and grading of cancer, if found. This project is likely to increase both the accuracy and reliability of analysis of cancerous tissue specimen.

Brain-computer interfaces (BCI) [13]

These are making brain control possible. Today, a paralyzed person can control Exoskeletons with their thoughts. Developed   at the University of Melbourne, the device connects into the brain without need to perform any surgery on the skull.  In fact, interface uses stents and slides through an incision made in the jugular vein up to a blood vessel near the motor cortex in the brain. At the end of the stent, a metallic mesh with electrodes is used to pick up brain activity and relays this information to a recording device in the wearer’s chest, which wirelessly transmits it to an external computer that will control the exoskeleton. When a wearer wishes to move in a particular direction say, left — the brain fires and generates a pattern. By detecting the pattern from the brain, BCI and the system not only reads the thoughts of the person but also translates those into actions which can be followed by the Exoskeleton (see figure 3).

Figure 3: Exoskeleton controlled by brain of a paralyzed person

Prosthetics

Biomedical engineers at Newcastle University in the UK [14] have developed a prosthetic hand with an incredible new skill: the ability to "see" by using camera to assess shape and size by taking picture of any object in front of the hand and then taking appropriate action by activating a series of movements in the hand.

3D printing is one technology which is boon for the patients who need prosthetics as one can print them customized for each and every patient.

3D printing in healthcare [17, 30]

Scientists are finding the best way to 3D print human organs for transplant.   Northwestern University's Feinberg School of Medicine and McCormick School of Engineering, joint team is developing 3D-printed ovaries that can boost hormone production and restore fertility and successfully tested it on mice that not only produced healthy pups but mothers also nursed their young.

The ovaries were 3D printed using gelatin scaffolding. The team loaded the structures with immature egg cells before implanting them into their test subjects.  The main cause of the success lies in the temperature used while 3D printing the structure but the team's biosynthetic ovaries can even be considered for use in humans will take long time.  3D printing technologies use all type of materials from metals to polymers to biomaterials—materials that mimic living tissue—to create objects. 

3D printing is used to customize instruments used by orthopedic surgeon specifically for each patient, so he does not bother about so many different sizes into surgery.

Figure 4: 3D printing of mouse ovaries at Northwestern University

Personal genetics

The most significant application of AI and ML in genetics understands how DNA impacts life and also what influences life and biology; we need to first understand the language that is DNA. This is where ML algorithms play a big role and big players, such as; Google’s Deep Mind [19] and IBM’s Watson [20] are in the fray.  Technologies like big data detect patterns from enormous amounts of data (e.g. patient records, clinical notes, diagnostic images, treatment plans).

Deep Genomics [21] is developing the capability to interpret DNA by creating a system that predicts the molecular effects of genetic variation. Their database is able to explain how hundreds of millions of genetic variations can impact a genetic code. This will provide personalized insights to individuals based on their   biological dispositions. This trend is indicative of a new era of “personalized genetics,” whereby individuals are able to take full control of their health through access to unprecedented information about their own bodies.  As is the case with any application of AI/ML and also Big Data, the technology must have access to vast amounts of data in order to better curate lifestyle changes for individuals.  

Develop drugs of the future

Application of AI/ML in healthcare is not only reshaping the industry and making what was once was considered impossible task but also helping in the reduction of both cost and time in drug discovery.

Atomwise [22]—a San Francisco-based startup—is considering to use supercomputers instead of test tubes in the drug development procedure. The company uses ML and 3D neural networks that search through a database of molecular structures to find out therapies, discover the effectiveness of new chemical compounds on diseases and identifying existing medications that can be used to cure another ailment.

Berg Health [24]—a Boston-based Bio-pharma company—uses a different approach for drug discovery and works on patient biological data using AI to determine why some people survive diseases, and then applies this insight to improve current therapies or create new ones.

                       Figure 5: Cloud technology used for drug discovery

Cloud Pharmaceuticals [32] is using AI design innovations to discover and generate new novel drugs using cloud technology (see figure 5).

There are many new companies working on drug discovery exploiting ML/AI to the hilt to discover drugs and the future of this field is encouraging. It may be worth mentioning that the cost and time of drug development in the usual way varies between USD 0.5 to 3 Billion and may take more than a decade to reach the user.

Discovering new diseases

Most diseases are far more than just a simple gene mutation. Although the healthcare system generated enormous amounts of (unstructured) data—which is gradually refining and improving in quality—but we did not possess the necessary hardware and software in place to investigate it and produce telling results.

Disease diagnosis involves a number of factors, like the texture of a patient’s skin to the amount of sugar level in his blood.  So far, we have worked on symptomatic detection, e.g. if one has a fever and stuffy nose, he is diagnosed most likely to have the flu.  Systems can now predict the molecular effects of genetic variation [21], opening a new and exciting path to discovery for disease diagnostics and therapies.

But often the arrival of detectable symptoms is too late, especially when dealing with diseases such as cancer and Alzheimer’s. With ML, the hope is that faint signatures of diseases can be discovered well in advance of detectable symptoms, increasing the probability of survival and/or treatment options.

Freenome [25, 28]—a San Francisco-based startup—is a technology company developing proprietary algorithms and novel methods to enable accurate diagnosis of clinical conditions.  Freenome platform—an Adaptive Genomics Engine—dynamically detects disease signatures in your blood.  The company uses your Freenome—the dynamic collection of genetic material floating in your blood and is constantly changing over time and it’s like a genomic thermometer of who you are as you grow, live and age. As per CEO of the company, ‘our aim is to bring accurate, accessible, and non – invasive screenings to doctor to proactively treat cancer and other diseases at their most manageable stages’.

Enlitic [26] uses deep learning techniques to empower doctors with necessary data to not only become faster but also accurate.  This is possible as doctors look at disease diagnosis and treatment plans by coupling deep learning with medical data and extract actionable insights from billions of clinical cases. IBM’s Watson is working with Memorial Sloan Kettering in New York to analyze data on cancer patients and treatments used over decades to present and suggest treatment options to doctors in dealing with unique cancer cases.

Google’s Deep Mind will scan 1 million medical records of Moorfields Eye Hospital [27] and will analyze these digital scans of the eye  will be used to train an AI system to help doctors better understand and diagnose eye disease.  Google has estimated that up to 98% of sight loss happens due to diabetes and can be prevented with timely and early treatment.

Way forward

The data Scientist today is playing a major role in preprocessing large amount of data and making it meaningful for the ML algorithm to show good and acceptable results. It is well known fact that ML algorithms give better results with more and more of the training and test data.  The rule for maintaining good ratio of training and test data is amid 65:35 to 75:25. The combination of Big Data and ML/AI techniques play an interesting and decisive role in analyzing and handling enormous amount of data (which medical fraternity possesses) and deriving meaningful and more accurate results. 

While robots and computers will never completely replace doctors and nurses, machine learning/deep learning and AI are changing the healthcare industry, improving not only results but also discovering new medicines   to attack diseases, and changing the very mode doctors think today of providing care.  ML/AI is improving diagnostics, predicting results and tomorrow doctors may be in position to give even personalized care.

IBM’s Watson [20] helps oncologist to make the best care decisions for their patients.  The Care team helps doctors devise and understand the best care protocols for cancer patients.

AiCure is using mobile technology and facial recognition technologies to determine if a patient is taking the right medications at the right time to help doctors confirm that the patient is taking their medications and alert them if something goes wrong [23] (see figure 6). This approach may help doctors to face biggest hurdles in healthcare, i.e., patient come back to hospital after discharge and doctor don’t know why?

Figure 6: Amalgamation of mobile and AI tech for doctor feedback

Healthcare start-ups are using ML/AI, Big-data and even deep learning to make it flexible for all the development phases and areas for all kind of users in healthcare. Industry analysts forecast that 30 percent of health providers will use cognitive analytics with patient data by 2018. In near future [29] robots will help children with autism and diseases will be diagnosed by a breathalyzer.

References

[1] Progress and Perils of Artificial Intelligence (AI) 

http://newblogrgs10.blogspot.in/2017/04/progress-and-perils-of-artificial_5.html

[2] Invited Chapter 6 - Evolutionary Algorithms and Neural Networks, Pages 111-136, R.G.S. Asthana in book, Soft Computing and Intelligent Systems (Theory and Applications), Academic Press Series in Engineering, Edited by:Naresh K. Sinha, Madan M. Gupta and Lotfi A. Zadeh ISBN: 978-0-12-646490-0

http://www.sciencedirect.com/science/book/9780126464900

[3] Future 2030 by Dr. RGS Asthana, Senior Member IEEE

https://www.linkedin.com/pulse/future-2030-dr-rgs-asthana-senior-member-ieee-r-g-s-asthana

[4] Machine Learning (ML) and Artificial Intelligence (AI) – Part 1, by Dr. RGS Asthana, Senior Member IEEE

https://plus.google.com/113904112609362672954/posts/4ti2gyQVPZ7

[5] Machine Learning (ML) and Artificial Intelligence (AI) – Part Two, by Dr. RGS Asthana, Senior Member IEEE

https://plus.google.com/113904112609362672954/posts/X5wB7baJLwt

[6] Internet of Things (IoT)

http://www.cisco.com/c/en_in/solutions/internet-of-things/overview.html

[7] Machine Learning (ML) and Artificial Intelligence (AI): Cognitive Services and Robotics – Part Three by Dr. RGS Asthana, Senior Member IEEE

https://plus.google.com/113904112609362672954/posts/EA9hZcY6AwL

[8] Machine Learning (ML) and Artificial Intelligence (AI):  Big Data and 3 D Printing – Part four by Dr. RGS Asthana, Senior Member, IEEE.

https://plus.google.com/113904112609362672954/posts/agAa6pY9ekA

[9] Machine Learning (ML) and Artificial Intelligence (AI):  Drones and Self-driving Cars– Part Five by, Dr. RGS Asthana, Senior Member IEEE

http://newblogrgs10.blogspot.in/2017/05/machine-learning-ml-and-artificial_17.html

[10] Confirmed: AI Can Predict Heart Attacks and Strokes More Accurately Than Doctors

https://futurism.com/confirmed-ai-can-predict-heart-attacks-and-strokes-more-accurately-than-doctors/

[11] Google Image

https://www.google.co.in/search?q=ML+and+AI+and+Healthcare&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjc9oKVmP7TAhWLuY8KHUUPDqYQ_AUIBygC&biw=1280&bih=709#imgrc=S7GeXvSZ1dX1oM:

[12] The Huffington Post: Using AI to spot lung cancer

https://flipboard.com/@huffingtonpost/-using-ai-to-spot-lung-cancer/f-a31b491418%2Fhuffingtonpost.com

[13] Brain implant allows paralyzed people to control Exoskeleton with their mind

https://www.digitaltrends.com/cool-tech/brain-stent-exoskeleton-control/

[14] UK Engineers Develop Prosthetic Hand That Can 'See'

http://www.pcmag.com/news/353532/uk-engineers-develop-prosthetic-hand-that-can-see

[15] Self-Taught Artificial Intelligent Increases Heart Attack Prediction Rate Significantly

http://trendintech.com/2017/05/06/self-taught-artificial-intelligent-increases-heart-attack-prediction-rate-significantly/

[16] 5 apps changing how the healthcare industry works

https://www.airsassociation.org/services-new/airs-knowledge-network-n/airs-articles/item/19318-5-apps-changing-how-the-healthcare-industry-works

[17] 3D-printed ovaries successfully produce healthy mice pups

https://www.engadget.com/2017/05/18/3d-printed-ovaries-mice/

[18] Advances in AI and ML are reshaping healthcare

https://techcrunch.com/2017/03/16/advances-in-ai-and-ml-are-reshaping-healthcare/

[19] Deep mind website

https://deepmind.com/

[20] IBM Watson Website

https://www.ibm.com/watson/

[21] Changing the course of genomic medicine

https://www.deepgenomics.com/

[22] Atomwise website: Artificial Intelligence for Drug Discovery

http://www.atomwise.com/

[23] How ML, Big Data and AI are changing Healthcare forever

https://www.forbes.com/sites/bernardmarr/2016/09/23/how-machine-learning-big-data-and-ai-are-changing-healthcare-forever/#1a1c020c1a1c

[24] Berghealth website

http://berghealth.com/

[25] Freenome website

https://www.freenome.com/

[26] Enlitic website

http://www.enlitic.com/

[27] Google's DeepMind to peek at NHS eye scans for disease analysis

http://www.bbc.com/news/technology-36713308

[28] Freenome

https://www.facebook.com/freenome/posts/908794832474557

[29] The Future of Artificial Intelligence & Machine Learning In Healthcare

https://www.eventbrite.co.uk/e/the-future-of-artificial-intelligence-machine-learning-in-healthcare-tickets-32955561973#

[30] The power of 3D Printing: How this technology is blazing new medical frontiers

https://www.jnj.com/healthcare-products/medical-devices

[31] AI is using in dynamic phases of Healthcare start-ups

https://www.linkedin.com/pulse/dynamic-phases-artificial-intelligence-health-care-start-up-alam-mo

[32] Cloud Pharmaceuticals website

http://www.cloudpharmaceuticals.com/

[33] Johnson & Johnson Looks to IBM’s Watson to Predict Patient Outcomes

https://blogs.wsj.com/cio/2015/09/25/johnson-johnson-looks-to-ibms-watson-to-predict-patient-outcomes/

[34] Microsoft Health Vault website

https://www.healthvault.com/en-us/