 | |
IBM headquarters in Armonk, New York.
| |
| Public | |
| Traded as | |
| ISIN | US4592001014 |
| Industry | Cloud computing Artificial intelligence Computer hardware Computer software |
| Predecessor | Bundy Manufacturing Company Computing Scale Company of America International Time Recording Company Tabulating Machine Company |
| Founded | June 16, 1911as Computing-Tabulating-Recording Company) Endicott, New York, U.S. |
| Founders | |
| Headquarters | , |
Area served
| 177 countries |
Key people
| Ginni Rometty (Chairman, President and CEO) |
| Products | See IBM products |
| Services | |
| Revenue |  US$79.59 billion (2018) |
| US$13.21 billion (2018) | |
| US$8.72 billion (2018) | |
| Total assets |  US$123.38 billion (2018) |
| Total equity | US$16.79 billion (2018) |
Number of employees
| 350,600 (2018) |
| Website | www |
IBM Watson Health is a division of the International Business Machines Corporation, (IBM), an American multinational information technology company headquartered in Armonk, New York. It helps clients facilitate medical research, clinical research, and healthcare solutions, through the use of artificial intelligence, data, analytics, cloud computing, and other advanced information technology.
IBM began in 1911, founded in Endicott, New York, as the Computing-Tabulating-Recording Company (CTR) and was renamed "International Business Machines" in 1924. IBM is incorporated in New York.
IBM produces and sells computer hardware, middleware and software, and provides hosting and consulting services in areas ranging from mainframe computers to nanotechnology. IBM is also a major research organization, holding the record for most U.S. patents generated by a business (as of 2019) for 26 consecutive years. Inventions by IBM include the automated teller machine (ATM), the floppy disk, the hard disk drive, the magnetic stripe card, the relational database, the SQL programming language, the UPC barcode, and dynamic random-access memory (DRAM). The IBM mainframe, exemplified by the System/360, was the dominant computing platform during the 1960s and 1970s.
Advancements
In
healthcare, Watson's natural language, hypothesis generation, and
evidence-based learning capabilities are being investigated to see how
Watson may contribute to clinical decision support systems and the increase in artificial intelligence in healthcare for use by medical professionals.
To aid physicians in the treatment of their patients, once a physician
has posed a query to the system describing symptoms and other related
factors, Watson first parses the input to identify the most important
pieces of information; then mines patient data to find facts relevant to
the patient's medical and hereditary history; then examines available
data sources to form and test hypotheses; and finally provides a list of individualized, confidence-scored recommendations.
The sources of data that Watson uses for analysis can include treatment
guidelines, electronic medical record data, notes from healthcare
providers, research materials, clinical studies, journal articles and
patient information.
Despite being developed and marketed as a "diagnosis and treatment
advisor", Watson has never been actually involved in the medical
diagnosis process, only in assisting with identifying treatment options
for patients who have already been diagnosed.
In February 2011, it was announced that IBM would be partnering with Nuance Communications
for a research project to develop a commercial product during the next
18 to 24 months, designed to exploit Watson's clinical decision support
capabilities. Physicians at Columbia University
would help to identify critical issues in the practice of medicine
where the system's technology may be able to contribute, and physicians
at the University of Maryland
would work to identify the best way that a technology like Watson could
interact with medical practitioners to provide the maximum assistance.
In September 2011, IBM and WellPoint (now Anthem) announced a partnership to utilize Watson's data crunching capability to help suggest treatment options to physicians. Then, in February 2013, IBM and WellPoint gave Watson its first commercial application, for utilization management decisions in lung cancer treatment at Memorial Sloan–Kettering Cancer Center.
IBM announced a partnership with Cleveland Clinic in October 2012. The company has sent Watson to the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University,
where it will increase its health expertise and assist medical
professionals in treating patients. The medical facility will utilize
Watson's ability to store and process large quantities of information to
help speed up and increase the accuracy of the treatment process.
"Cleveland Clinic's collaboration with IBM is exciting because it offers
us the opportunity to teach Watson to 'think' in ways that have the
potential to make it a powerful tool in medicine", said C. Martin
Harris, MD, chief information officer of Cleveland Clinic.
In 2013, IBM and MD Anderson Cancer Center began a pilot program to further the center's "mission to eradicate cancer". However, after spending $62 million, the project did not meet its goals and it has been stopped.
On February 8, 2013, IBM announced that oncologists at the Maine
Center for Cancer Medicine and Westmed Medical Group in New York have
started to test the Watson supercomputer system in an effort to
recommend treatment for lung cancer.
On July 29, 2016, IBM and Manipal Hospitals"Manipal Hospitals | Watson for Oncology | Cancer Treatment". watsononcology.manipalhospitals.com. Retrieved January 17, 2017. A leading hospital chain in India) announced the launch of IBM Watson
for Oncology, for cancer patients. This product provides information and
insights to physicians and cancer patients to help them identify
personalized, evidence-based cancer care options. Manipal Hospitals is
the second hospital
in the world to adopt this technology and first in the world to offer
it to patients online as an expert second opinion through their website. Manipal discontinued this contract in December 2018.
On January 7, 2017, IBM and Fukoku Mutual Life Insurance entered
into a contract for IBM to deliver analysis to compensation payouts via
its IBM Watson Explorer AI, this resulted in the loss of 34 jobs and the
company said it would speed up compensation payout analysis via
analysing claims and medical record and increase productivity by 30%.
The company also said it would save ¥140m in running costs.
It is said that IBM Watson will be carrying the knowledge-base of
1000 cancer specialists which will bring a revolution in the field of
healthcare. IBM is regarded as a disruptive innovation. However the
stream of oncology is still in its nascent stage.
Several startups in the healthcare space have been effectively
using seven business model archetypes to take solutions based on IBM
Watson to the marketplace. These archetypes depends on the value
generate for the target user (e.g. patient focus vs. healthcare provider
and payer focus) and value capturing mechanisms (e.g. providing
information or connecting stakeholders).
In 2019 Eliza Strickland calls "the Watson Health story [...] a
cautionary tale of hubris and hype" and provides a "representative
sample of projects" with their status.
Industry considerations and challenges
The
subsequent motive of large based health companies merging with other
health companies, allow for greater health data accessibility. Greater health data may allow for more implementation of AI algorithms.
A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems.
As the amount of data increases, AI decision support systems become
more efficient. Numerous companies are exploring the possibilities of
the incorporation of big data in the health care industry.
IBM's Watson Oncology is in development at Memorial Sloan Kettering Cancer Center and Cleveland Clinic. IBM is also working with CVS Health on AI applications in chronic disease treatment and with Johnson & Johnson on analysis of scientific papers to find new connections for drug development. In May 2017, IBM and Rensselaer Polytechnic Institute
began a joint project entitled Health Empowerment by Analytics,
Learning and Semantics (HEALS), to explore using AI technology to
enhance healthcare.
Some other large companies that have contributed to AI algorithms for use in healthcare include:
Microsoft
Microsoft's Hanover project, in partnership with Oregon Health & Science University's Knight Cancer Institute, analyzes medical research to predict the most effective cancer drug treatment options for patients. Other projects include medical image analysis of tumor progression and the development of programmable cells.
Google's DeepMind platform is being used by the UK National Health Service to detect certain health risks through data collected via a mobile app.
A second project with the NHS involves analysis of medical images
collected from NHS patients to develop computer vision algorithms to
detect cancerous tissues.
Intel
Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.
Artificial intelligence in healthcare is the use of complex algorithms and software to emulate human cognition
in the analysis of complicated medical data. Specifically, AI is the
ability for computer algorithms to approximate conclusions without
direct human input.
What distinguishes AI technology from traditional technologies in
health care is the ability to gain information, process it and give a
well-defined output to the end-user. AI does this through machine learning algorithms.
These algorithms can recognize patterns in behavior and create its own
logic. In order to reduce the margin of error, AI algorithms need to be
tested repeatedly. AI algorithms behave differently from humans in two
ways: (1) algorithms are literal: if you set a goal, the algorithm can't
adjust itself and only understand what it has been told explicitly, (2)
and algorithms are black boxes; algorithms can predict extremely precise, but not the cause or the why.
The primary aim of health-related AI applications is to analyze
relationships between prevention or treatment techniques and patient
outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, and National Health Service, have developed AI algorithms for their departments. Large technology companies such as IBM and Google, and startups such as Welltok and Ayasdi, have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI solutions
to support operational initiatives that increase cost saving, improve
patient satisfaction, and satisfy their staffing and workforce needs. Companies are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.
The following medical fields are of interest in artificial intelligence research:
Radiology
The
ability to interpret imaging results with radiology may aid clinicians
in detecting a minute change in an image that a clinician might
accidentally miss. A study at Stanford
created an algorithm that could detect pneumonia at that specific site,
in those patients involved, with a better average F1 metric (a
statistical metric based on accuracy and recall), than the radiologists
involved in that trial. The radiology conference Radiological Society of North America
has implemented presentations on AI in imaging during its annual
meeting. The emergence of AI technology in radiology is perceived as a
threat by some specialists, as the technology can achieve improvements
in certain statistical metrics in isolated cases, as opposed to
specialists.
Imaging
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.
In 2018, a paper published in the journal Annals of Oncology
mentioned that skin cancer could be detected more accurately by an
artificial intelligence system (which used a deep learning convolutional
neural network) than by dermatologists.
On average, the human dermatologists accurately detected 86.6% of skin
cancers from the images, compared to 95% for the CNN machine.
Disease Diagnosis
There
are many diseases out there but there also many ways that AI has been
used to efficiently and accurately diagnose them. Some of the diseases
that are the most notorious such as Diabetes, and Cardiovascular Disease
(CVD) which are both in the top ten for causes of death worldwide have
been the basis behind a lot of the research/testing to help get an
accurate diagnosis. Due to such a high mortality rate being associated
with these diseases there have been efforts to integrate various methods
in helping get accurate diagnosis’.
An article by Jiang, et al (2017)
demonstrated that there are multiple different types of AI techniques
that have been used for a variety of different diseases. Some of these
techniques discussed by Jiang, et al include: Support vector machines,
neural networks, Decision trees, and many more. Each of these techniques
are described as having a “training goal” so “classifications agree
with the outcomes as much as possible…”.
To demonstrate some specifics for disease
diagnosis/classification there are two different techniques used in the
classification of these diseases include using “Artificial Neural
Networks (ANN) and Bayesian Networks (BN)”. From a review of multiple different papers within the timeframe of 2008-2017
observed within them which of the two techniques were better. The
conclusion that was drawn was that “the early classification of these
diseases can be achieved developing machine learning models such as
Artificial Neural Network and Bayesian Network.” Another conclusion
Alic, et al (2017)
was able to draw was that between the two ANN and BN that ANN was
better and could more accurately classify diabetes/CVD with a mean
accuracy in “both cases (87.29 for diabetes and 89.38 for CVD).
Telehealth
The increase of Telemedicine, has shown the rise of possible AI applications.
The ability to monitor patients using AI may allow for the
communication of information to physicians if possible disease activity
may have occurred.
A wearable device may allow for constant monitoring of a patient and
also allow for the ability to notice changes that may be less
distinguishable by humans.
Electronic health records
Electronic
health records are crucial to the digitalization and information spread
of the healthcare industry. However logging all of this data comes with
its own problems like cognitive overload and burnout for users. EHR
developers are now automating much of the process and even starting to
use natural language processing (NLP) tools to improve this process. One
study conducted by the Centerstone research institute found that
predictive modeling of EHR data has achieved 70–72% accuracy in
predicting individualized treatment response at baseline. Meaning using an AI tool that scans EHR data it can pretty accurately predict the course of disease in a person.
Drug Interactions
Improvements in Natural Language Processing led to the development of algorithms to identify drug-drug interactions in medical literature.
Drug-drug interactions pose a threat to those taking multiple
medications simultaneously, and the danger increases with the number of
medications being taken.
To address the difficulty of tracking all known or suspected drug-drug
interactions, machine learning algorithms have been created to extract
information on interacting drugs and their possible effects from medical
literature. Efforts were consolidated in 2013 in the DDIExtraction
Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.
Competitors were tested on their ability to accurately determine, from
the text, which drugs were shown to interact and what the
characteristics of their interactions were. Researchers continue to use this corpus to standardize the measure of the effectiveness of their algorithms.
Other algorithms identify drug-drug interactions from patterns in
user-generated content, especially electronic health records and/or
adverse event reports. Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization’s VigiBase
allow doctors to submit reports of possible negative reactions to
medications. Deep learning algorithms have been developed to parse these
reports and detect patterns that imply drug-drug interactions.
