Machine Learning (ML) and Artificial Intelligence (AI): Impact of AI/ML in Healthcare: Part-Eight by Dr. RGS Asthana Senior Member IEEE

Machine Learning (ML) and Artificial Intelligence     (AI): Impact of AI/ML in Healthcare: Part-Eight

by

Dr. RGS Asthana

Senior Member IEEE

Figure 1: Some of the ML/AI driven healthcare companies [27]

Summary

What really marks healthcare different from other disciplines [36]?  Healthcare may often have very little labeled data (e.g., clinical NLP). This may prompt the use of semi-supervised learning algorithms i.e. keeping human in the loop (HITL) [38]. Sometimes, we have only small numbers of samples (e.g., for a rare disease) and we need to learn as much as possible from other data (e.g. EHR data of healthy patients). We may have lots of missing data that too at varying time intervals and may only get censored labels. Other more important problem which we need to solve is that ML base algorithms do not give reason for arriving at a particular decision. Therefore, it is pertinent to model the problem keeping these aspects in view and may be reason for HITL in the solution.

ML based solutions are good at prediction and diagnosis too is a prediction in a way. We, therefore, describe ML based diagnosis and treatment systems.  The only thing necessary for systems to give better prediction is training on substantial data. The areas where ML/AI based systems have impact in healthcare are: on-line consultations, Health assistance and medication management, Personal genetics, development of drugs of the future, discovering new diseases, persistent care, discovering new clinical pathways and last but not the least Robotics and Healthcare.

Keywords

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

Prelude

It has always been our goal to catch health-related issues early, before they pose some real problems, for instance, tracking the risk factors that are related to diabetes before a person actually develops diabetes.

Big Data and ML/AI will transform our lives as today we have a lot of data which is partially made available for research in anonymized form and it looks that plowing it into ML algorithms to build predictive models should be easy. However, first major challenge is that this data is raw and needs to be pre-processed.  The second major challenge is in combining various databases such as shopping behavior, daily exercise and diet info, maybe personal genomes and also occasional blood test data.

ML/AI in healthcare and medicine can optimize patient treatment plans as well and also provide physicians with the information to make a good decision.  IBM’s Watson is exploiting cognitive computing for healthcare in a big way.

The quality of the solution is judged from the facts whether the solution is able to transform medicine not only by making it accessible to the poorest medical institutions or also to a handful of experts because they are too difficult to use. AI already found several areas in healthcare to transform starting from the design of treatment plans through the assistance in repetitive jobs to medication management or drug creation. Dell, Hewlett-Packard, Apple, Hitachi Data Systems, Luminoso, Alchemy API, Digital Reasoning, Highspot, Lumiata, Sentient Technologies, Enterra, IPSoft , Next IT are a few leading names in healthcare.

The most obvious application of ML/AI in healthcare is data management which includes processes like collecting, storing, normalizing and tracing   family history of data. Google recently acquired Deepmind [11] – a AI company - to begin with they have aligned with the Moorefield’s Eye Hospital NHS Foundation Trust [29].

IBM Watson [12] provides oncologists/clinicians evidence-based treatment options.  It has advanced ability for oncology to analyze context of structured and unstructured data in clinical notes and generate reports that may be useful to picking a treatment pathway.

Medical Sieve [15] is an ambitious project of IBM to build a “cognitive assistant” with analytical, reasoning capabilities and a wide range of clinical knowledge. The idea is that routine images are seen and analyzed only by Medical Sieve and Radiologists in the future should only look at the most complicated cases where human supervision is useful. This medical assistant will handle both Radiology and Cardiology cases. Deep learning can readily handle a broad spectrum of diseases in the entire body and all imaging modalities (X-rays, CT scans, etc.)

On-line consultations

In US and UK, you cannot meet your GP when you need him but you have to seek an appointment first. In UK, an online medical consultation and health service called Babylon [16] was launched which offers medical AI consultation considering personal medical history and common medical knowledge. This app reminds patients to take their medication and does necessary follow up by finding its patient’s current medical status.  

Health assistance and medication management

The world’s first virtual nurse, Molly was developed by the medical start-up Sense.ly [17].  The virtual nurse has a smiling, amicable face with a pleasant voice and is designed to help people with monitoring their condition and treatment. This interface uses ML to provide care to patients with chronic conditions in-between doctor’s visits. It provides proven, customized monitoring and care, with a strong focus on chronic diseases.

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 [18] 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 [10]

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 [11]—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 [21]—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 2 Cloud technology used for drug discovery

Cloud Pharmaceuticals [26] is using AI design innovations to discover and generate new novel drugs using cloud technology (see figure 2).

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

Genomics aims at identifying patterns in huge data sets of genetic information and medical records, looking for mutations and linkages to disease. A new generation of computational technologies are being invented that can inform doctors when DNA is altered by genetic variation, whether natural or therapeutic. 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 [18], 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 [22, 25] — 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 [23] 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 Moorfield’s Eye Hospital [24] 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 based on the needs and habits of patients. 

CBI [27] insights located number of companies involved in new programs for imaging and diagnostic using ML and deep learning algorithms are more practiced at recognizing patterns.   An IBM supported group called Pathway Genomics [28] provides physicians and their patients with actionable and accurate genetic information to improve or maintain health and wellness.  It is developing a simple blood test to determine if early detection or prediction of certain cancers is possible. Lumiata [30] is using ML based predictive analytics tools to find accurate insights and make predictions related to symptoms, diagnoses, procedures, and medications for individual patients or patient groups.

Treatment

IBM’s Watson [12, 31] helps oncologist make the best care decisions for their patients. Watson provides clinicians with evidence-based treatment options and also helps patients understand specific care options available to them. 

In a completely different field, Ginger.io [32] is developing a mobile app to   deliver mental health treatments.  Using this app a person can   analyze his moods and learn strategies developed by doctors.

Persistent care

Persistent care means that quality of care as applicable remains consistent irrespective of patient location, i.e., IPD in hospital, ICU in hospital or at home. This can only be possible, in my opinion, when care is automated and human being is not involved in actual care.

ML based algorithms are best at prediction if trained properly not only on internal data, within the control of enterprise, but also on external data that is available and accessible on the internet [55]. With the advent of the Internet of Things (IoT) [6], the number of sources of data has significantly increased.  As this is the only way to get vast amount of data.  Diagnosis and treatment also fall in this domain.

Hospital re-admittance is the biggest difficulty a doctor faces in healthcare as they do not know whether a patient is taking prescribed medication when he goes home.

A surveillance tool from Ayadsi [57, 58] analyzes patient’s EHR and gives clinicians’ real-time information whether a patient experiencing shortness of breath may meet the criteria for sepsis, chronic heart failure, pneumonia or any other condition. This acts as an early warning system, enabling clinicians to identify patients that may be trending toward a bad outcome. Being able to have crucial information sent to staff enables them to save time and doctors can get involved earlier in-patient care.

AiCure [33] exploits AI for continuous patient monitoring using mobile technology and facial recognition technologies.  It determines 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.

NextIT [34] uses conversational AI system based bots, assistants, and any other endpoint going beyond streamlining processes for innovating and empowering Fortune 5000 enterprises for over a decade to serve their customers and employees better.  It has developed a digital health coach, similar to a virtual customer service rep on an ecommerce site. The assistant can prompt questions about the patient’s medications and remind them to take the medicine, ask them about symptoms, and convey that information to the doctor.

The Caféwell Concierge [35] app uses IBM’s Watson’s [12] natural language processing (NLP) power and guides and inspires a user to be healthier. It gives personalized recommendations to help a user to attain optimal health and get the most out of health plan benefits.  It is available on Android as well as on Apple platforms.

Clinical Pathways

Last year, I happen to visit a hospital as my sister was admitted there but nurses there were not very nice. They did not care about the patients and only were present in strength during the visiting hours in ICU. The Nurses were of opinion that people who have money leave their patients in hospital to die. In India 90% of nurses come only from one state called Kerala and they in general do take care of the patients.  I came in the above hospital where nurses were local there was problem of service quality.  Doctors were good at handling problem but service quality did not improve despite best efforts put by doctors in front of patients.  Intelligent automation, i.e., use of virtual or ML/AI based robotic nurses is a solution to ensure uniform and persistent care. This may have number of other indirect advantages like reduced infections, cost reduction, less re-admissions and also reduction in length of stay in the hospital.

Using EHR data and machine learning algorithms, researchers have developed a cost-based framework for identifying clinical pathways for chronic disease patients.

By creating step-by-step clinical pathways based on a patient’s anticipated disease development [37], big data analytics techniques could help providers achieve accurate predictions of anticipated future events and costs following different clinical and cost pathways for improved shared decision making.

Figure 3: Clinical Performance Measurement and Standardization within units and across sites enhancing patient safety and clinical outcomes.

The first step in creating these algorithms is to build out Multi-stage approach for patients: 

·         Identify the condition and factors including environmental and historical data may affect treatment?

·         Predict risks and the conditions of unknown variables?

·         Activate resources needed for treatment?

·         Monitor what clinical data can be accumulated during the treatment process? and also

·         Learn what findings after discharge can be applied into algorithms?

Deontics pathway and decision solutions [39] integrate individual patient data from the EMR with guideline/protocol knowledge. If any input data items changes, the system will instantly reevaluate the relevant evidence and patient data and render new outputs. 

All clinical data items are functionally linked with standard ontology codes e.g. SNOMED CT– provides the core general terminology electronic health records (EHRs) [40], RxNorm – a standard drug terminology [41], LOINC [42] - nomenclature for clinical laboratory tests etc., offering communication and interoperability benefits.  Deontics [43] - a world-leading AI company with Clinical Pathway and Clinical Decision Support Systems (CDSS) products - providing the capability to fully ‘measure’ clinical performance at both an individual and population level and offering benchmarks for improvement (see figure 3).

Deontics systems [39, 43] have been implemented in major US and UK hospitals.

Mercy hospital – the seventh largest healthcare system in US - is using Ayasdi Care [60] to increase the quality of patient care by optimizing the care pathways which are about 80 and amounts to about 80% of care provided in most hospitals.  With Ayasdi Care's analytics and unique data-driven approach, physicians determine optimal pathways by using reverse experimentation to identify patterns in data derived from their local patient data, and automatically recognize best practices that deliver the best patient outcomes.

For example, using Ayasdi analytics, Mercy Hospital identified that the administration of a specific analgesic after arthroscopic knee replacement surgery correlated with lower costs and shorter hospital stays [59].

The benefits of this approach will be higher patient satisfaction, lower readmissions, shorter length-of-stays, and also lower costs. However, each outcome of the AI automation is analyzed by a group of physicians before being selected for implementation/modification (if required), i.e., human is kept in the loop.

Robotics and healthcare

Medical robots [44] do not only exist in sci-fi movies but they are coming to healthcare in a big way as Robots can support, assist and extend the service health workers are offering. In jobs of repetitive and monotonous nature robots can even completely replace humans.

 Here are some of the interesting medical robot facts:

·         Due to use of Xenex [45] Robot there is at least 70% drop in hospital acquired infections.

·         Two Belgian Hospitals have “Hired” Pepper Robots as Receptionists [38].

·         Surgical robotics sales are expected to almost double by 2020, i.e., it would reach up to 6.4 billion USDs.

·         Cuddly animal-shaped PARO Robot [52] reduces stress for patients. PARO is an advanced interactive robot.  It allows the documented benefits of animal therapy to be given to patients in hospitals and extended care facilities.

·         Mechanical aides can not only pick up groceries and take out the trash [53] for the elderly people but also can easily be programmed to remind and give medicine.

·         The elderly, a group indifferent to new technology, now finds themselves as a target market for the growing robotics industry. Intuition Robotics [54], an Israel-based startup that makes robots, has received funding from Toyota Research Institute. The funding will be used to develop a social companion robot, ElliQ. This robot will be designed to reduce caregiver stress. It will give reminders about upcoming doctor's appointments. ElliQ works to keep older adults engaged and active personalized suggestions such as going for walks, or listening to music. Hus it will make all effort to reduce their loneliness.

·         Intouch Health [46] has had more than 750,000 remote clinical encounters through so far.

·         TUG Robot [47] able to carry around more than 400 kilograms of medication as it travels the halls, rides elevators and audibly speaks while performing its tasks.  Hospitals using TUGs are not only serious about improving efficiency but also it enhances their reputation as being on the leading edge of medical care.

·         Japanese Bear-Shaped robot [40] can lift patients from beds into wheelchairs or help them to stand up, promising ‘powerful yet gentle care’ for the elderly (see figure 4).   

Figure 4: Robot which can lift Patient

·         Less than a millimeter sized Microbot delivers drugs through bloodstream [49]. The battery in any device always runs the risk of exploding. Therefore, it became necessary to develop a device which is light powered and has no battery. The ability to self-organize pulsing recurrent motions, such as the repetitive flipping motion [50] is one of the fundamental characteristics of living organisms and this can be used in the future to develop bio-inspired molecular motors and robots that will find applications in wide areas, including medicine. Researchers of this tech have no plans to commercialize the technology as yet (see figure 5).

Figure 5: Small, light-powered robots swimming around your body sounds a lot more fun that tiny, squid-like battery-powered mechanism

·         Veebot [51] draws blood in less than a minute to make the whole procedure of venipuncture automated to reduce error and decrease venipuncture times.

Way forward

Robots and computers will probably never completely replace doctors and nurses but application of ML/deep learning/AI will change the healthcare industry by improving diagnostics, predicting outcomes, and providing personalized care and thus changing the way doctors think about providing care which is generally to follow the 19^th century processes.

A general purpose AI system is what may be more useful to healthcare environment provided it can always give reasoning to the actionable info it has come up with. To safeguard all interests, either reasoning is given for every recommendation or Human in the loop (HITL) approach is used for by the AI Automation to get acceptance in healthcare domain. Further, accuracy of outcomes suggested by AI system improves as it has access to more and more data.

”By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries.” — Harpreet Singh Buttar, analyst at Frost & Sullivan.

Cognitive analytics with patient data will become pre-dominant in healthcare Industry and more than one third of providers will use it by 2018.  

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

http://newblogrgs10.blogspot.com/2017/05/machine-learning-ml-and-artificial_26.html

[11] Deep mind website

https://deepmind.com/

[12] IBM Watson Website

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

[13] Artificial Intelligence Will Redesign Healthcare

http://medicalfuturist.com/artificial-intelligence-will-redesign-healthcare/

[14] 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/#438346a11a1c

[15] Medical Sieve

http://researcher.watson.ibm.com/researcher/view_group.php?id=4384

[16] Hand-picked doctors, supported by cutting edge technology

https://www.babylonhealth.com/

[17] Sensely website

http://sensely.com/meet-our-team/

[18] Changing the course of genomic medicine

https://www.deepgenomics.com/

[19] Atomwise website: Artificial Intelligence for Drug Discovery

http://www.atomwise.com/

[20] 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

[21] Berghealth website

http://berghealth.com/

[22] Freenome website

https://www.freenome.com/

[23] Enlitic website

http://www.enlitic.com/

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

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

[25] Freenome

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

[26] Cloud Pharmaceuticals website

http://www.cloudpharmaceuticals.com/

[27] From Virtual Nurses to Drug Discovery: 106 Artificial Intelligence Startups in Healthcare

https://www.cbinsights.com/blog/artificial-intelligence-startups-healthcare/

[28] Pathway Genomics website

https://www.cbinsights.com/company/pathway-genomics

[29] Moorfield’s Eye Hospital NHS Foundation Trust website

http://www.moorfields.nhs.uk/

[30] Lumiata website

https://www.ventureradar.com/profile.aspx?cid=73874eaf-604e-4f6a-a380-c1574c99374c\

[31] Watson for Oncology

https://www.ibm.com/watson/health/oncology-and-genomics/oncology/

[32] Ginger.io

https://www.ventureradar.com/profile.aspx?cid=12379a29-0783-4715-a902-34f976d5f990

[33] AiCure website

https://aicure.com/

[34] NextIT website

http://www.nextit.com/

[35] Cafewell Website

https://www.cafewell.com/

[36] Lecture 1: What makes healthcare unique?

https://mlhc17mit.github.io/slides/lecture1.pdf

[37] EHR Data, Machine Learning Create Cost-Based Clinical Pathways

https://healthitanalytics.com/news/ehr-data-machine-learning-create-cost-based-clinical-pathways

[38] Machine Learning (ML) and Artificial Intelligence     (AI):  Will AI/ML intelligence surpass humans? Part Seven by Dr. RGS Asthana, Senior Member IEEE

http://newblogrgs10.blogspot.in/2017/06/machine-learning-ml-and-artificial.html

[39] AI Clinical Pathways/Decisions

https://www.leadingedgeonly.com/innovation-marketplace/_i1812-ai-clinical-pathways-decisions-ensure-compliance-increase-efficiency-increase-health-mitigate-risk-reduce-cost.aspx

[40] SNOMED CT

https://en.wikipedia.org/wiki/SNOMED_CT

[41] Bio Medical Informatics: Discovering knowledge in Big Data

https://books.google.co.in/books?id=-FMlBAAAQBAJ&pg=PA136&lpg=PA136&dq=ontology+terminology+RxNorm&source=bl&ots=PPxFTqJ6Gl&sig=cNPQVLe3431H50WTa3NI3tkfhFg&hl=en&sa=X&ved=0ahUKEwjrrcvZ_JHVAhXIVrwKHTsJC1cQ6AEIUTAH#v=onepage&q=ontology%20terminology%20RxNorm&f=false

[42] Semantic Web Representation of LOINC: an Ontological Perspective

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839278/

[43] Deontics Website

http://www.deontics.com/

[44] 9 Exciting facts about Medical Robots

http://medicalfuturist.com/9-exciting-medical-robot-facts/

[45] The Robot used by more hospitals than any other device for no-touch room disinfection.

https://www.xenex.com/

[46] Intouch health Website:  Virtual health anytime anywhere

https://www.intouchhealth.com/

[47] TUG Healthcare

http://www.aethon.com/tug/tughealthcare/

[48] Robear: the bear-shaped nursing robot who'll look after you when you get old

https://www.theguardian.com/technology/2015/feb/27/robear-bear-shaped-nursing-care-robot

[49] Microbots: Using Nanotechnology In Medicine

http://www.yalescientific.org/2013/02/microbots-using-nanotechnology-in-medicine/

[50] Light-powered microbots could deliver drugs directly to our bloodstream

https://techcrunch.com/2016/07/07/light-powered-microbots-could-deliver-drugs-directly-to-our-bloodstream/

[51]  Veebot Website

http://www.veebot.com/

[52] Paro Robot Website

http://www.parorobots.com/

[53] Europe Bets on Robots to Help Care for Seniors

https://www.bloomberg.com/news/articles/2016-03-17/europe-bets-on-robots-to-help-care-for-seniors

[54] Can robots solve Grandma's loneliness?

http://money.cnn.com/2017/07/11/technology/culture/robot-senior-citizen/index.html

[55] Persistent: Machine Learning: Data Integration

https://www.persistent.com/machine-learning/

[56] With machine learning and AI in healthcare, can you speak the language?

http://www.healthcareitnews.com/news/machine-learning-and-ai-healthcare

[57] Ayasdi

https://en.wikipedia.org/wiki/Ayasdi

[58]  Ayasdi’s new AI platform touted for use in healthcare

https://www.healthdatamanagement.com/news/ayasdis-new-ai-platform-touted-for-use-in-healthcare

[59] On ML & AI in Healthcare

https://blog.purestorage.com/on-machine-learning-ai-in-healthcare/

[60] Mercy Optimizes and Automates Patient Care Pathways with Ayasdi

https://www.ayasdi.com/company/news-and-events/press/pr-mercy-optimizes-and-automates-patient-care-pathways-with-ayasdi/