Artificial intelligence use in healthcare 2022
Artificial intelligence technology can be used most hotly in the medical
field. Because various and extensive tasks such as finding, treating, observing
the prognosis, and researching and applying new treatment methods are being
done in the medical field, new methods are always needed to efficiently perform
these tasks. am. However, on the other hand, since it is directly related to
human health and life, it requires a lot of thought in applying new technology.
In this post, we will briefly review artificial intelligence
technologies that are being researched and utilized in the recent medical
field. And furthermore, it introduces the current concerns about whether
artificial intelligence technology can replace the role of medical staff.
What artificial intelligence technologies will be used in the medical field?
The field of artificial intelligence includes many sub-fields, and so
does the medical field. Therefore, to classify artificial intelligence used in
the medical field, two methods can be applied. The first is to divide the AI
technology field and look at the medical applications to which each
technology is applied [1], and the second is to divide the medical field and
look for AI technologies for each field. In this post, we will use both methods, and we
will follow the method applied in the reference paper
Classification according to AI technology
·
Computer vision – medical imaging
Figure 1. Structure of CNN for processing medical images
Deep learning is the most widely used technology in the field of
artificial intelligence. And deep learning is most widely used in the field of
processing image or video data. Figure 1 briefly expresses the concept of
inputting medical image data into a Convolutional Neural Network (CNN), one of
the most representative models of deep learning. Various tasks such as
classifying types by inputting image data, finding specific patterns in images,
or tying images with similar characteristics can be performed using the deep
learning model. It can be used in the dermatology department to identify the
condition and symptoms of the skin, and in the radiology department, it can be
used in the process of finding nodules or tumors in CT or X-ray images. In
dental X-ray images, it can help determine the condition of teeth or jaw joint,
and in ophthalmology, it can help fundus examination to detect abnormalities in
the retina. use in
· NLP(Natural Language Processing)
The second most representative artificial intelligence technology is
Natural Language Processing (NLP). And NLP can be most utilized in the process
of processing Electronic Health Record (EHR). EHRs contain a huge amount of
information. Not only data, but also the experience and wisdom of doctors are
melted. Therefore, it is important to properly extract and utilize important
information from the EHR and to properly record the information in the EHR. The
process for extracting information from EHR has been tried for a long time in
the data mining field, and research on extracting important information from
content written in natural language and building a system to understand it is
representative. In recent years, efforts are being made to free medical staff
who are overloaded with paperwork by converting spoken contents into text and
storing them. Research related to extracting and storing information from
conversations between doctors and patients is also ongoing. Of course, in this
process, not only NLP but also speech recognition technology is used.
·
Robotics
Robotics is different from the fields mentioned above in that it is a
field in which various technologies are fused. However, it is undoubtedly an
area that can greatly contribute to the development of medical technology.
Robotics technology is typically applied to surgical robots, and in detail,
laparoscopic surgery can be greatly developed. [3] The key is robotic-assisted
surgery (RAS) using reinforcement learning, and allows the robot to learn the
doctor's actual surgical procedure through trial & error, or the doctor
remotely controls the robot. Attempts are being made to use robots for
repetitive tasks such as suturing and knot-tying.
·
Generalized information processing
2. Machine Learning in Genomics
There are AI technologies that are specialized for specific types of
data, such as Vision or NLP, but in real situations, it is often necessary to
process various complex data. For example, in precision medicine, which
predicts which treatment protocol is most likely to be successful based on
patient characteristics or treatment environment, or in the process of
analyzing how well a patient usually follows a doctor's instructions, various
types of data are used. should be collected and analyzed.
Figure 2 conceptually expresses the process of applying artificial
intelligence in Genomics, and the representative example is analyzing what kind
of disease a mutation identified in genetic information causes. Artificial
intelligence technology is also used in the process of predicting a phenotype
(eg body size) or estimating the probability of contracting a specific disease
based on genetic information. These analysis results can be used to find the
most suitable customized treatment method for each patient by interlocking with
the imaging data that records the patient's condition, the data usually
collected using a wearable sensor device, and the patient's examination
(treatment) history information.
Artificial intelligence technology is also being used in the field of
drug development, and it is used for tasks such as finding substances that can
be used to treat specific diseases, synthesizing compounds, predicting drug
properties, and exploring new ways to use specific drugs. is utilized
Classification according to medical field
·
AI for living assistanc
The goal in the field of making devices or systems that help in daily
life is to allow users or patients to efficiently control the devices or
systems they use. In particular, this technology is very important for patients
who cannot use their hands freely or suffer from paralysis in certain parts of
the body. do. Also, in the ambient intelligent system-related technology that
tracks the overall human condition or fall-detection systems, the artificial
intelligence module that detects the user and environment status from various sensor
data plays the most important role. In addition, artificial intelligence robots
that help cognitive (memory) rehabilitation while continuously interacting with
users in daily life are being studied.
·
AI in biomedical information processing
Clinical information is large-capacity, and biomedical data in the form
of images and video information as well as text are complexly connected.
Therefore, if a process for extracting and merging knowledge from such data or
automatically finding and resolving conflicts between information is developed,
it is possible to do things that were not possible in the past. A QA system to
provide appropriate information to patients or caregivers, and an intelligent
workflow system to reduce the administrative burden on medical staff can also
be included in this field.
·
AI in biomedical research
The process of searching for and utilizing a vast amount of newly
published academic research materials in the medical field is a time-consuming
and difficult task. Therefore, attempts are being made to automatically perform
search, analysis, summary, and arrangement of large-scale academic research
data based on artificial intelligence technology. As the COVID-19 virus entered
the pandemic stage, artificial intelligence researchers attempted to
automatically analyze the research results accumulated so far. In addition,
artificial intelligence technology plays an important role in the process of
developing a system that simulates medical experiments that are difficult to
perform in practice.
·
Disease diagnostics and prediction
Artificial intelligence can be applied to the process of diagnosing or
predicting a disease. Typical tasks include detecting abnormalities in genetic
information, diagnosing cardiovascular diseases based on ECG data, finding
diseases from medical image data, or predicting the survival rate of cancer
patients. This process can greatly assist the doctor in the diagnosis and
treatment process.
Will artificial intelligence replace doctors?
There are three main views on whether advanced artificial intelligence
will replace human doctors. These are the position that it will be replaced,
the position that it cannot be replaced, and the position that it will coexist.
The position that artificial intelligence will replace humans is based
on its massive data processing capabilities, tireless learning and monitoring
capabilities, and predictions that it will cost less than using humans. Of
course, the relationship between doctors and patients is a matter of trust and
empathy, but it is a view that even this can be imitated by artificial
intelligence, and patients will want accurate and quick diagnosis rather than
empathy. It is also suggested that the patient will not feel ashamed when
artificial intelligence gives treatment, unlike when a doctor gives treatment.
Some argue that eventually human doctors will play a role in helping AI.
On the other hand, the view that artificial intelligence cannot replace
human doctors argues that health care is not simply achieved through data. The
clinical context plays a big role in the diagnosis and treatment process, and
it is argued that artificial intelligence cannot grasp the context and
situation like humans. Human doctors treat patients as human beings, and it is
argued that artificial intelligence will not be able to implement medical
services based on trust, responsibility, and dedication based on overall
understanding and insight into the patient's life and environment. In addition,
in many cases, it is not possible to explain the patient's situation only with
technical knowledge and quantitative numbers, and sometimes it is necessary to
select a treatment method or drug while knowing the side effects or risks. It
also raises the question of whether Furthermore, the treatment process involves
listening to the patient's questions and giving a feeling of understanding the
patient's feelings. From another point of view, it is argued that it is difficult
to perfectly link the clinical workflow and EHR system with artificial
intelligence technology. [4]
From the standpoint of coexistence [5], we have the view that the best
scenario is to acknowledge and complement each other's limitations
Figure 3. Multi-step automation concept
Figure 3 considers the utilization level of medical artificial
intelligence as the 'automation' level, and compares it with the automation
level of the driverless car. In the figure, it is judged that complete
automation is impossible. There are certain things that AI can do better than
humans, but considering the fatality of the problems that may be caused by the
bias that may occur in the process of implementing artificial intelligence and
the errors that may be caused by artificial intelligence, the ultimate purpose
of medical artificial intelligence is argues that it is not complete
automation, but corresponds to conditional automation. A high level of
automation or complete automation is impossible, and it is a view that always
requires human review and monitoring. The notion that doctors will be
completely replaced by AI is a belief that artificial intelligence is driven by
excessive expectations and fantasies, because AI always needs to be modified
and verified. It is also argued that it is wrong to think that the 99% accuracy
recorded by artificial intelligence during the R&D process will be shown in
clinical practice, and it is also wrong to think that a judgment based on
large-scale data will always produce the best results in the treatment process
of individual patients.
Finish…
In the process of artificial intelligence being put into actual medical
practice, conditions other than technology - legal environment, economic,
social conditions, ethical considerations, social acceptance, etc. - will also
promote or slow down the use of artificial intelligence. Artificial
intelligence is clearly appearing little by little in the medical field, and
will replace humans first in dealing with information and data rather than
directly dealing with patients. And there is ample room to contribute in the
development of preventive, participatory, and personalized healthcare
technologies. Thus, anyone who rejects AI completely and refuses to use it at
all may lose his or her job in the medical field. However, on the other hand,
it is also important that medical AI technology must be gradually incorporated
into the actual clinical environment, and that it must go through a thorough
verification process in the process. Also, even if the verification process has
been completed, thinking that these technologies will completely replace
medical staff may be a misjudgment based on excessive illusions about
technology. This is because the medical process is an interaction between the
medical staff and the patient, and the fact that the knowledge, experience,
judgment, insight, and prompt and accurate actions of the medical staff are
absolutely necessary in the medical field will not change.
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