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Advances in Social Sciences Research Journal – Vol. 10, No. 5
Publication Date: May 25, 2023
DOI:10.14738/assrj.105.14633.
Jarrett, J. E. (2023) Artificial Intelligence has a Role in Medical Treatments and Associated Research? Advances in Social Sciences
Research Journal, 10(5).226-233.
Services for Science and Education – United Kingdom
Artificial Intelligence has a Role in Medical Treatments and
Associated Research?
Jeffrey E. Jarrett
University of Rhode Island (COB)
ABSTRACT
I encourage the growth of artificial intelligence and similar methods in health and
medical care for the purpose of continuously improving processes included in this
field. By focusing on the growth on data analytics, statistics, applied mathematics,
and computer methods including machine learning, the future of health-care
methods will change. The development of computerized methods and the growth of
data systems produce ample materials for artificial intelligence to develop and to
bring physician assistance programs to enable continuous improvement resulting
in superior health and medical care. This includes applications in intensive care as
well as diagnostic therapies. The focus is on examples in the use of the promising
developments in data science methods, the accumulation of medical and research
data. With quality and continuous improvement in process control applications
where one determines the usefulness of data analytics, there are great possibilities
of change in the improvement in medical applications as well as the management of
hospital and other health-care facilities.
Keywords: Data analytics, Artificial intelligence (AI), Autoregressive moving average
Modeling, Multivariate Models, Autocorrelation of Data
INTRODUCTION
Data analysis and analytics is now everywhere one looks, from the production of most
scientifically manufactured component parts to the checkout lines at most supermarkets,
hardware stores, and automatic consumer buying via the internet. We refer to this as
automation, but it is the advances in computer technologies that drove this mechanization of
seemingly simple but technologically advanced tasks to streamline production methodologies.
The growth of these technologies in the future will be accelerated by breakthroughs in artificial
intelligence (AI), which will continue the mechanization of tasks to improve the quality of
output. By including AI into health-care procedures is not simple, but it includes the
methodology of statistical and/or mathematical science as it applies to data-driven
methodologies. In this study, we focus on one such plan that involves the analytics associated
with a volume of diagnostic tests to produce plans to generate treatments.
AUTOMATING THE QUALITY MOVEMENT IN DIAGNOSTICS
Improvements in diagnostic care, whether in hospitals, treatment and diagnostic centers, and
other health-care units, are a central function of quality health care. In many places, they are
the principal methods by which patients can secure care. The example of Planned Parenthood
clinics is one where patients can receive care and treatment in an affordable and often
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Jarrett, J. E. (2023) Artificial Intelligence has a Role in Medical Treatments and Associated Research? Advances in Social Sciences Research Journal,
10(5).226-233.
URL: http://dx.doi.org/10.14738/assrj.105.14633
convenient manner for men’s health care. Planned Parenthood provides services that often are
not available to those who do not have sufficient (or even any) places to obtain affordable care.
A client with a severe set of conditions enters the clinic to have scientific tests performed in
order to ascertain or determine a diagnosis and therapeutic plan to produce a treatment to
successfully reduce the problem and achieve positive results. The process includes a total
quality movement (TQM), which is a plan to achieve a successful outcome to the patient’s health
problem. In the future, we expect AI and TQM to spread everywhere. Just look at the current
research in automobiles and the relative changes made by the driverless vehicle.
To consider the depth of AI and modern data analytics topics in health care, refer to the
publications by Jarrett (2008 and Jarrett and Pan 1981, 2015, 2016). In addition, see Patel et al.
(2009), Machado and Costa (2010), and Khoo and Quah (2003). More recently, Acampora et al.
(2013) added specific illustrations of new computer-based methods. Technology firms such as
Google, Amazon, Microsoft, and Apple in recent years made huge investments in AI to deliver
tailored search results and build items called personal virtual assistants. The methodology is
seeping down to hospital care and other forms of diagnostic and treatment methodology in
health care in general. With reforms in health care and health-care reform law, AI will assist
physicians and other health-care personnel in choosing medicines and treatments for patients
in an efficient and timely manner. For example, a physician who has a patient with a particular
diagnosis will be able to choose the best medicine to counter the effect of a severe ailment
quickly. With the huge number of medicines available for a physician to prescribe, much
decision making will be automated thanks in part to the push for computer systems to prescribe
the best treatment available from medical science. No longer will a physician need to peruse
volumes of databases to find the optimal treatment. The computer will find and inform health- care personnel to act quickly and optimally.
Today, data collection by health statisticians includes volumes of patient demographic and
clinical and billing data, which are in an electronic format for analysis by intelligent software.
For these difficult tasks, AI software can analyze quickly to perform the tasks of recommending
medicines, treatment protocols, and general advice to assist physicians in attacking the
problems associated with difficult diagnoses. For example, applications of AI have been utilized
in intensive care for nearly a generation (Hanson and Marshall 2001; Liu and Salinas 2017). In
other examples, new digital devices and home tests are allowing a more thorough patient
examination remotely, which addresses some of the previous setbacks of telemedicine. Remote
diagnostic tools such as Tyto, Scanadu, and Med Wand are expanding the perception of
telemedicine. Heartbeat and respiration rate can now be checked remotely. The same is true
for blood pressure, blood glucose, body temperature, and oxygen levels. A device may contain
a high-definition camera that can look down the throat and ear canals. Cameras can also provide
high-resolution images of the skin to examine lesions, suspicious skin changes, and other
dermatological problems. Urine-testing kits may also be employed in the home or specific
diagnostic centers to provide information to medical personnel to suggest a treatment without
the patient being in the same physical location as the medical personnel.
At this point, we should consider automated statistical quality control (ASQC) or automated
statistical process control (ASPC) as it applies in TQM. These terms are no longer new in
diagnostic and treatment terminology; however, they are based on previous applications in
industry, banking, and everywhere one seeks assistance in the analysis of data where the timing
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of decisions is very important. TQM is the field that ensures that management maintains
standards set and continually improves the processing of successful goals and achievements.
Instead of final, end-of-service inspection (whether the patient is found healthy or not after the
treatment ends). TQM according to Lee and Wang (2003) and Weihs and Jessenberger (1999)
provides. Instead of end-of-service inspection and decision making, TQM emphasizes
prevention, integrated source inspection, process control, and continuous improvement The
mitigating of risks of type I and type II errors are the prime purpose of these methods. In
addition, AI will provide software, services, and analytics solutions to the ambulatory care
market. In a health-care information technology and services company that delivers the
foundational capabilities to organizations that want to promote healthy communities. The
technology provides a customizable platform that empowers physician success, enriches the
patient care experience, and lowers the cost of health care and, in turn, health insurance. Stated
simply, AISQC monitors the incidence characterized by the results of multiple tests on a similar
fluid per period of a short interval over a lengthy period (e.g., 10–20 weeks). The monitoring
requires an intelligent system analyzing items (e.g., control charts) and seeking whether there
are common causes of variation or special causes of variation. In industrial applications, these
were called Shewhart charts. Later, others suggested additional methods including the use of
exponentially weighted moving average (EWMA) control charts (See Griggs and Spiegelhalter
2007).
The great rise of health information systems enables AI in the very early stages of its
development to match one’s own intelligence. Computers certainly cannot diagnose like
physicians; however, AI software and computer technology are capable of processing vast
amounts of data and identifying patterns that humans cannot. AI solves the complex algorithms
that analyze these data and is a useful tool to take full advantage of electronic medical records,
transforming them from mere e-filing cabinets into full-fledged physician analysts that can
deliver clinically relevant, high-quality data in real time.
AISPC AND AISQC IN HEALTH-CARE ENVIRONMENTS
SPC/SQC environments usually assume a steady process behavior where the influence of
dynamic behavior does not exist or is ignored. The focus of control is where there is only one
variable (e.g., medical test) over a lengthy interval of time. SPC controls for the changes in either
the measure of location or dispersion, or both. These procedures as practiced in each phase
may disturb the flow of the service production process and operations. We note that in recent
years the use of SPC to address processes characterized by more than one test or treatment
emerged. First, we review the basic univariate procedures to improve the process of SPC and
allow AI to enter the process.
Shewhart control charts were the central foundation of univariate (one variable) SPC, which
has a major flaw. The process considers only one piece of data, the last data point, and does not
carry the memory of the previous data collected. Often, a small change in the mean of a random
variable is not likely to be detected quickly (Griggs and Spiegelhalter 2007). EWMA control
charts improved upon the detection process of small process shifts. Rapid detection of
relatively small changes in the characteristic of interest and ease of computations through
recursive equations are some of the important properties of the EWMA control chart that
makes the process attractive and easy to use intelligent software to detect changes.
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Jarrett, J. E. (2023) Artificial Intelligence has a Role in Medical Treatments and Associated Research? Advances in Social Sciences Research Journal,
10(5).226-233.
URL: http://dx.doi.org/10.14738/assrj.105.14633
The EWMA chart is used extensively in time-series modeling where the data contain a gradual
drift (Box and Draper 1998). EWMA provides for identifying gradual shifts in medical tests by
predicting where the observation will be in the next period of time. Hence, the EWMA process
improves decision support in the future and is dynamic (Hunter 1986). The EWMA statistic for
monitoring the results of lengthy period of tests having short interval when the actual tests are
performed. Furthermore, the method gives less and less weight to data as they become more
remote in time. Montgomery’s (2013) work contains the development of models for finding
control limits in this univariate process but appears to be another example of where intelligent
software applies.
ADDITIONAL APPLICATIONS USING UNIVARIATE MODELS
In many applications of univariate analysis, the sample size used in the test process is one.
Stated differently, the sample consists of an individual unit the control chart for a sample of one
(the individual chart) employs a moving average of two successive observations to estimate the
process variability. Obviously, small samples lead to incorrect decisions (stated as an increase
in the probability of a type II error) point out problems and issues associated with statistically
based evaluations which must be included in intelligent software. A solution may be provided
by examining the average run length (ARL) of a proposed solution over a variety of alternative
process shifts. ARL performance for an in-control state and for a single shift in a process for
which the proposed detection program optimizes must be evaluated. If the system is not
optimized, misplaced control limits may result. The system for detection of shifts is sub
optimized, and better techniques should be sought. Yeh and Hwang (2004) suggest processes
whereby the units are dynamic. In provider-of-care treatments, the distinction between phases
I and II of SQC solutions is often not clear. Hence, ARL is often the choice used to assist the
providers of care with the assistance they need to recommend courses of treatment.
Alwan (1992) finds that the great majority of SPC applications studied result in control charts
with misplaced control limits and essentially false signals to the providers of care. The
misplacement results from autocorrelated process observation. The autocorrelated time-series
observations violate an assumption associated with Shewhart control charts (Woodall 2005).
Autocorrelation of process observations is common in many applications—for example, cast
steel (Alwan 2000), wastewater treatment plants (Berthouex et al. 1978), chemical processes
many other processes in the health-care industry, especially diagnostic care and similar
applications. In addition, suggest using autoregressive integrated moving average (ARIMA)
charts for decision analysis. Continuous intelligent software can be of particular aid to
identification of the appropriate methods for decision analysis if one follows the works of
Atienza et al. (1998), Box et al. (2008), and West et al. (2002) who employed ARIMA modeling
with intervention. In addition, Jarrett (2016a, 2016b) summarize many of these method in SPC.
All these models are in the process of being computerized to develop intelligent systems that
will enable computers intelligently point to optimal patient treatments and diagnoses. The
notion of physicians having patient-centered diagnostic programs using AI will be of immense
help.
MULTIVARIATE QUALITY CONTROLS (MQC)
Multivariate methods use additional analyses due to having two or more variables that are the
results of several diagnostic procedures to determine a specific plan of care (treatment). The
use of univariate analysis may lead to incorrect interpretation of data due to the cointegration
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of the tests performed. The most popular multivariate methods (MQC) are those based on the
Hotelling T2 distribution (West et al. 2002; Woodall 2005; Yang and Rahim 2005) and
multivariate exponential moving average method (MEWMA; Elsayed and Sastri 2007). Other
approaches, such as control ellipse, apply for the case of two correlated variables. There are
other MQC methods including those developed by Kalagonda and Kulkarni (2003, 2004), Jarrett
and Pan (2006, 2007a, 2007b, 2013), Vanhatalo and Kulachi (2015), and Billen et al. (2016). All
the aforementioned MQC modelers produced results that achieve superiority to SQC analysis
because of one or more of the following factors:
1. The control region of variables is represented by an ellipse rather than parallel lines.
2. The intelligent software is programmed to maintain a specific probability of a type I
error in the analysis.
3. The determination of whether the process is out of control is a single control limit (ARL).
4. Correcting T2-based MQC analysis autocorrelation is present. The New York University
Langone School of Medicine announced recently and reported on New York television
in May, 2022 that Brain Cancer detection will be shortened by great amounts of time ,
obviously reducing the death rate for Brain cancer detection,(Note: Item reported in the
New York Times May 2, 2023)
5. Data Analytica Methods coupled with new algorithm for patient care and data analysis.
As a result, the above methods indicate that intelligent software cannot ignore the various
possibilities to lead to non-optimal decisions. However, proper AI methods will adjust to new
research, and patient-assisted analytical software will be of great use to find diagnoses that
enable one to use AI to solve difficulties with patient care.
LIMITATIONS OF AI
AI, which uses all the methods discussed, is dependent on data science, scientific sample, and
statistical analysis. One of the great problems is that AI has yet to come to grips with the huge
problems associated with the presently insurmountable problem of language. An AI
professional may develop a huge amount of arithmetic equations by combining a small number
of mathematical symbols and, in turn, following a small set of rules. Similarly, one can develop
an enormous amount of sentences by utilizing a relatively modest number of words and rules.
A realistic and useful AI system still needs to cope with the challenges associated with all the
possible sentences that may be created in the conversations developed in the AI interrogatory.
Genuine AI systems in health care need to have simple and realistic combinations of questions
and interpretations that are easily understood and do not require the finite possibility of many
interpretations of results. AI will have problems when correct solutions relate to what is “most
likely true.” Hence, interrogatories must be tight and simple such that AI cannot rely on
insufficient interpretations of questions and answers (see Cambria and White 2014).
As of this time, the dominant approach to AI is not working out. There is no reason to believe
that researchers in AI should return to the projects of making machines actually share some of
human’s cognitive abilities. Human cognition could be built into machines applications as there
is flexibility in human thought that is goal. Approaching AI has been built into Google Translate
and Google Duplex. The limitations of these applications as a form of human intelligence should
alert developers. If machine learning and what is entitled “big data” cannot deliver any further
than a ticket to a Broadway Show in the hands of the most capable AI firms and developers, it
is time to reconsider the strategy associated with AI development.