CC BY
14ARTIFITIAL INTELLEGANCE IN HEALTHCARE: LESSONS FOR
UZBEKISTAN
I. Vikhrov
Abstract: This publication is intended to study the use of artificial intelligence (AI) technologies in healthcare and medicine, as well as to understand the current state and trends in the development of AI in healthcare. In addition, the author outlines the potential improvements associated with the use of AI technologies in medicine, along with problem areas and possible risks. For the author of the article, it is extremely important to focus the research on the practical application of AI technologies in the field of healthcare, in connection with which an example of the practical development of an AI tool was given.
The work is important in training highly qualified specialists and increasing the level of knowledge of medical professionals in the field of AI application in medicine. The use of chatbots as a means of freeing medical staff from routine will allow them to focus on their Covid patients, as well as on creative activities to improve their skills. Besides, it provides a brief overview of how chatbots used in other countries work and how effective they are.
Keywords: artificial intelligence in healthcare, digitalization, Uzbekistan, covid-19, chatbot.
ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ЗДРАВООХРАНЕНИИ: УРОКИ ДЛЯ УЗБЕКИСТАНА
Вихров И.П.
Аннотация: Данная публикация предназначена для изучения использования технологий искусственного интеллекта (ИИ) в здравоохранении и медицине, а также для понимания текущего состояния и тенденций развития ИИ в здравоохранении. Кроме того, автор описывает потенциальные улучшения, связанные с использованием технологий искусственного интеллекта в медицине, а также проблемные области и возможные риски. Для автора статьи крайне важно сосредоточить исследования на практическом применении технологий искусственного интеллекта в сфере здравоохранения, в связи с чем был приведен пример практической разработки инструмента искусственного интеллекта.
Эта работа также важна для подготовки высококвалифицированных специалистов и повышения уровня знаний медицинских работников в области применения искусственного интеллекта в медицине. Кроме того, в
нем содержится краткий обзор того, как работают чат-боты, используемые в других странах, и насколько они эффективны. Тема чат-ботов имеет решающее значение в области здравоохранения, а это означает, что врачи и медсестры, которые работают непосредственно с пациентами, освобождаются от своих рутинных обязанностей, чтобы сосредоточиться на своих пациентах с Covid, а также на творческой деятельности для улучшения своих навыков.
Ключевые слова: искусственный интеллект в медицине, цифровизация, Узбекистан, Covid-19, чат-бот.
Introduction
The development of AI technologies is currently very relevant and occupies one of the first places on the agenda of scientific research around the world. The flow and volume of information generated is so large that the human brain is unable to cope with the analysis of incoming data, and therefore information technologies for processing big data, as well as technologies using AI, including in healthcare, have become widespread.
The field of AI research is quite young and in general it is difficult to identify well-established terms, classifications, standards and norms, nevertheless, scientists working in the field of AI have a certain range of issues that affect various aspects of this area of knowledge. It is no coincidence that experts in the field of public health are closely monitoring trends in the development of AI and its use in medicine. Given the rather strict standards that are used in healthcare and medicine, because it concerns human health, scientists predict the imminent flourishing of AI technologies in healthcare and medicine. Which is expected to qualitatively increase the level of medicine, and, accordingly, will contribute to increasing life expectancy, reducing mortality and improving medical literacy of the population.
The possibilities of using AI in the field of healthcare and medicine are very diverse. More and more new mobile applications using AI technologies are appearing. Moreover, large international technology companies are entering the healthcare market that want to participate in the development of personalized healthcare using AI technologies and mobile healthcare services (M-Health). Of course, algorithms and the use of AI technologies can significantly improve the quality of medical services, thereby contributing to a more efficient use of financial and human resources by the state. This basic approach, focused on the capabilities of AI to improve the quality of medical services provided, should serve as a starting point for considering the topic of this article. However, the use of AI technologies also poses new challenges for us - along with the question of what kind of digital progress we as a society want.
Any study of issues related to healthcare quickly touches on fundamental ethical and moral aspects, the main one of which is what opportunities and risks does the use of AI technologies in healthcare and medicine create? The practical
examples given in this review illustrate the breadth of potential applications of AI technologies in healthcare - from predicting mental illness among social media users to providing expert support for therapeutic decisions of doctors and even helping paralyzed people to restore their mobility. Examples of the use of AI technologies in the diagnosis of Covid-19 were studied in more depth.
This study reveals the topic of how significantly AI technologies can contribute to improving the quality of medical services provided, but at the same time the authors highlighted the issues arising in connection with their use, concerning issues of equal and fair access to medical services, the responsibility of doctors and patients for decisions based on AI, and changes that occur in the relationship between doctors and patients in the era of total digitalization. Accordingly, not least this casts light on the problem of trust in the health care system itself.
Therefore, in the author's opinion, we, as citizens of the Republic of Uzbekistan, need to come to an understanding that AI technologies that are used in healthcare and medicine should be collectively discussed and approved, and we also need to understand where the "yellow stop line" should be drawn, beyond which it is not necessary to go.
The Republic of Uzbekistan also actively carries out research in the field of AI, including through the development and adoption of legislative acts, development strategies and support for scientific projects and academic educational initiatives. Thus, by Decree of the President of the Republic of Uzbekistan No. UP-6079 dated 05.10.2020 "ON APPROVAL OF THE DIGITAL UZBEKISTAN-2030 STRATEGY AND MEASURES FOR ITS effective IMPLEMENTATION", the Digital Uzbekistan -2030 strategy was adopted, where, among other things, the adoption of targeted programs of research and innovation projects in the areas of development of the country's digital economy is expected. Moreover, the priority of such targeted programs is expected to be such research topics as the study and application in practice of the possibilities of using virtual and augmented reality technologies, artificial intelligence, cryptography, machine learning, big data analysis and cloud computing in economic sectors.
Moreover, the Ministry of Innovative Development of the Republic of Uzbekistan has developed a draft Decree of the President of the Republic of Uzbekistan "On the STRATEGY for the development of ARTIFICIAL INTELLIGENCE IN the REPUBLIC OF UZBEKISTAN IN 2021-2022", which was proposed for discussion to the general public on 11/07/2020 on the State Portal for discussing draft regulatory legal acts. This draft regulatory document proposes a broad discussion and implementation of various aspects of AI in order to develop the Republic of Uzbekistan and achieve a competitive advantage of the country.
Republic of Uzbekistan has a long way to go for the development of AI, including in the healthcare system, but given the fact that the intellectual potential and motivation of medical scientists are very high, and also, due to the availability of infrastructural support from the state, we are deeply confident that the necessary breakthrough in the development of AI technologies in healthcare and medicine, it will be implemented in the very near future.
As of February 2022, the World Health Organization estimates that nearly 399 million cases of COVID-19 have resulted in more than 5, 7 million deaths worldwide [1]. During the Covid-19 pandemic around the world, new research was created in all areas, including in the healthcare system. It is hard to imagine a world of news without modern technology. Additionally, during the Covid-19 pandemic in Uzbekistan, a number of studies were conducted in the field of methodological manuals, online surveys, call centers, mobile applications. In the beginning of 2022, it is obvious that despite the research, this epidemiological process is still going on, which has a significant negative impact on its economic, social and health sectors. Despite the advice and information provided by the media during the pandemic, the number of polls among the population has increased. In fact, the population intensively searching for information on social media to get clear answers about Covid-19. It will enable the development of measures to meet the information needs of the population and the introduction of digital technologies in the healthcare system [2]. The Covid-19 Checker chatbot was created in July 2021 to manage the epidemiological situation, in accordance with the recommendations and guidelines of the World Health Organization.
Materials and research methods
During the global pandemic the use of chatbots increased significantly. Data on the use of chatbots in the healthcare system have been researched. These terms have been reviewed and analyzed in professional journal articles, including PubMed, Springer Link, Journal of Medical Internet Research, and Google Scholar. The study included only articles published in 2019-2021. From our research on the 10 most used chatbots, we analyzed that most chatbots were used in European countries and the USA.
To study the results of our own research, a chatbot developed by us was analyzed. The task of the chatbot is to facilitate decision-making and the choice of further actions in acute respiratory viral diseases COVID-19, colds and flu.
Results
Based on guidelines from the Italian healthcare system, Covid-19 worked on an anonymous survey called "Support for Surveys" during the pandemic. Query Support (QS) software is designed as a web-based algorithm. A total of 75,557 participants took part in the Italian "Survey Support" Chatbot. From them 65,207 were diagnosed with the flu and 19,062 had the Covid-19 virus. 65,207 had
symptoms but not PSR confirmed, as well as 8,692 participants who had contact with a patient with COVID-19 status [3].
Sorting out users with COVID-19 - Recommendations are made for screening those who have had contact with patients with confirmed symptoms or confirmed COVID-19 virus and taking appropriate action. Including anonymous access to the system is open to any user. Continuous communication is based on a chat interface [4].
Another algorithm was based on the 2009 Patient Selection for Nurses (HBS) program, which was developed around the world when the H1N1 virus was detected [5].
In this program, a coordinated state-wide HBS system called MN Flu Line (Minnesota Flu Line) was created to address the following objectives: (1) to provide accurate information, - to send consistent messages and assistance, including the use of antiviral drugs reducing public confusion through; (2) reducing the spread of the disease by reducing the number of patients who accumulate in health facilities; (3) reduction of medical indications in HCS to ensure that other priority medical needs are met; and (4) meeting the needs of uninsured or uninsured patients and patients who do not have easy access to health care [6].
Another study, using the concept of artificial intelligence created in a pandemic environment in Boston, USA, introduced the use of artificial intelligence to optimize claims and complaints in the "Nurse Helpline" only chatbot. The introduction of artificial intelligence to remotely perform tasks performed individually by clinical staff is an important step in the health care system. The Nurse Helpline online chatbot provides advice on patient management, the hospitalization of medium and severe patients, staying at home, and self-protection for those who come in contact a virus-confirmed patient. The chat used effectively by citizens and medical staff. The use of this chatbot for early diagnosis of the disease and to limit the chain of transmission of the disease received a 58% positive result [7].
At the beginning of the COVID-19 pandemic, the Copenhagen Emergency Medical Services (CEMS) developed a digital diagnostic "See a Doctor" chatbot to assess signs of infection. Launched in the Danish capital region. One week later, the device was introduced nationwide in Denmark and was used more than 90,000 times in the first week and almost 150,000 times in the second week. The chatbot was provided for 2 different purposes: (a) to assist isolated citizens in assessing whether their symptoms were potentially associated with COVID-19 and to advise them on when and where to seek additional medical care; and (b) reducing the number of calls to health hotlines [8].
Coronavirus symptom testing Chatbot "AVA" Chatbot developed under the guidance of WHO and the Indian Ministry of Health and Family Welfare. This
Chatbot developed an app based on population, age, gender, whether or not they were communication and a number of principal surveys [9].
Published in the Journal of Experimental Psychology on October 28, 2021, French scientists have developed a Chatbot that offers tailored answers to questions posed by curious or hesitant people and demonstrated its effectiveness. Vaccination hesitation is one of the key challenges in the fight against the COVID-19 pandemic. Previous research has shown that mass communication through short messages broadcast on television or radio is not an effective means of persuading hesitant. The team tested their Chatbot with 338 people. After a few minutes of chatting with Chatbot, the number of participants who expressed a positive opinion on the vaccine increased by 37%. After using Chatbot, people became more prone to vaccination, and the idea of vaccine rejection decreased by 20%. Additionally, this Chatbot is regularly updated with information about the new vaccine [10].
A total of 2,618,862 participants reported potential symptoms of COVID-19 in the American-made online mobile app Nurse Helpline. Among 18,401 people who tested SARS-CoV-2, the proportion of participants who reported a loss of smell and taste was positive (4,668 of 7,178 people; 65.03%) with a negative test, which was higher than that of the positive. 805 753- The participant estimated that COVID-19 may be present, of which 140,312 (17.42%) confirmed Covid-19 virus [11].
Another was the creation of the Covid-19 Preliminary Test website, developed by the Ministry of Health Uzbekistan. Unlike the Covid-19 checker bot, this online survey includes a few additional questions. For example: there are chronic diseases; whether or not they have been on a trip to a foreign country [12].
In Uzbekistan, in line with other countries, an online survey of the web application COVID-19 Checker has been developed. The survey was conducted from July to October 2021. The survey was conducted online via mobile phone, answering questions from participants about gender, age, whether or not they had been vaccinated, symptoms, and contact with other people.
A study conducted in Uzbekistan in this area, the Covid-19 checker bot developed by the Tashkent Pediatric Institute, involved 332 respondents, men and women aged 20-60 years and older [13].
The research conducted by the staff of the Innovation Center of the Tashkent Pediatric Medical Institute covers the period up to June-October 2021. As part of our research, COVID-19_CHECKER Chatbot was developed to help Chatbot users make a differential diagnosis between cold and flu. We analyzed COVID-19, the most common symptoms of cold and flu, and took into account some signs and indications. Table 1. shows the recommended distribution of signs and symptoms to provide information needed to distribute COVID-19, the likelihood of influenza and influenza infection, considering the seasonality and epidemiological situation.
Based on epidemiological situation, symptoms such as COVID-19 pentad's, fever, dry cough, loss of smell or taste, shortness of breath, and fatigue were given maximum scores. Vaccinations of users were also taken into account, which affected reducing the likelihood of COVID-19 infection.
Further, recommendations were developed, which were based on the percentage of the respondent's likelihood of COVID-19. A copyright certificate was obtained for the developed program at the Agency for Intellectual Property of the Republic of Uzbekistan (certificate No. DGU12138 dated 07/09/2021).
Table 1. COVID-19, Cold, and Flu ranking points
Features and signs Covid-19 Common cold Influenza
Temperature 150 20 80
Dry cough 150 20 75
Loss of smell or taste 150 0 15
Fatigue 150 10 10
Dyspnea 150 0 0
Joint pain 50 60 80
Diarrhea 50 0 80
Sore throat 50 70 10
Headache 30 10 80
Nausea and vomiting 30 0 0
Skin rashes 20 0 5
Rhinorrhea 10 80 5
Sneezing 0 80 0
Conjunctivitis 20 10 10
Pain in the eyes 0 20 70
Abdominal pain 30 60 10
Contact with an infected 200 10 10
person
In total, 332 people took part in the online survey via COVID-19_CHECKER Chatbot between July and October 2021. Thus, the distribution of signs and symptoms is presented below.
According to the results of the Chatbot operation, the following data were obtained as indicated in the Fig.1 out of 332 participants, 174 were women and 158 were men. From them COVID-19 - 153 respondents, flu - 68, colds - 54 and 57 participants are not sick.
200 180 160 140 120 100 80 60 40 20 0
GENDER
174
DISEASE
158
-
153
68
- 54 57
-
=
Female
Male
COVID 19 Flu
Cold No illness
Figure 1. Distribution by sex and disease according to the Chatbot COVID-19-
CHECKER
YES NO
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Figure 2. The distribution of symptoms and signs of COVID19_CHECKER
Chatbot
In this diagram, the presence and absence of symptoms and signs are given. Among these indicators, the most common symptom in participants was weakness -208, while in 124 participants it was not observed. Another 294 participants with
skin rashes denied this sign. Another of the most common symptoms was headache in 160 participants, and no symptoms of sore throat in 206 participants.
After that, work was carried out to develop a neural network architecture that could predict one of the 3 diseases with at least 95% accuracy. In this connection, the collected data were randomly divided into training (main) and test (control) samples. The training sample included 265 participants and the test group - 67 participants. With the help of the built-in software of the Keras library, each time the training and test samples were split anew, in connection with which a high level of random participation was ensured in the process of the neural network training experiment.
The neural network architecture itself consists of a number of successive layers shown in the figure below (Fig. 3). The sequential neural network includes a fully connected layer of 64 neurons with relu-activation, then comes the Dropout drop-down layer, which turns off 30% of the data from the operating process, then a fully connected layer of 32 neurons with relu-activation, as well as the Dropout drop-down layer, which turns off 20 from the processing % data. At the end, we add an output fully connected layer for 4 neurons with softmax activation. The last 4 neurons determine the probability of determining a disease: COVID-19, Colds, Flu, or no disease.
dense_jnp ut InputLayer
dsise Dense
1
dropout Dropout
1
dense_l Dense
1
dropout_l Dropout
1
dense_2 Dense
Figure 3. Neural network Architecture
In total, 3812 parameters are involved in training a sequential neural network. After training the network, we achieved an accuracy of more than 95% of the neural network. Below is a graph of the network training accuracy for 100 experimental epochs (Fig. 4).
Learning epoch
Figure 4. Accuracy Plot on the Training Set.
In this picture, we can see that the comparative percentage in correct answers on the training set with validation set. Although, percentages of correct answers in the validation set significantly less than the training set.
We also analyzed the graph of errors that occurred during experiments on 100 epochs to train the neural network (Fig. 5).
Learning epoch
Figure 5. Loss on the training set 28
Comparative graph of the loss on the training set with validation set. This trade shows that the loss on the validation set noticeably higher than loss on the training set. Thus, the stated goal of achieving an accuracy of more than 95% in determining COVID-19 disease, influenza, colds or no disease in the test sample was achieved.
Comparative graph of the loss on the training set with the validation set. This trade shows that the loss on the validation set noticeably higher than loss on the training set. Thus, the stated goal of achieving an accuracy of more than 95% in determining COVID-19, influenza, cold, or no disease in the test sample was achieved.
Discussion
The development of AI technologies in healthcare and medicine is happening very rapidly. Countries such as China, the United States, the United Kingdom and a number of EU countries top the ranking of countries in the world for research and development in the field of AI, including in the field of AI for healthcare. In this regard, it seems important to us to get involved in the process of AI research in all fields, including medicine.
Experts attribute the future success of the development of the healthcare system in the world to the widespread introduction of digitalization in medicine, since e-health is an integral condition for the development of AI technologies in medicine. National electronic data in the field of healthcare is a necessary minimum for the widespread introduction of AI into medicine.
The benefits for healthcare systems that AI technologies bring are confirmed by the fact that absolutely all technological multinational corporations already produce commercial products for healthcare using AI. Moreover, the national governments of many countries have included issues of regulation and support for the development of AI technologies in all areas, including healthcare, in their current development agenda of the country.
In a number of countries, the matter of the near future is the widespread introduction of AI technologies into healthcare, which includes: the introduction of expert diagnostic and treatment systems based on AI technologies into the healthcare information system, the organization of a disease monitoring and surveillance system based on AI technologies, the prediction of diseases and mortality for a particular patient, taking into account individual characteristics, and so on.
These studies show that the use of chatbot services is widespread in developed countries. Given the slow development of artificial intelligence and information technology in Uzbekistan, we can propose that our research has made significant progress. Moreover, its widespread use in this area is an important factor in maintaining and diagnosing public health, especially in medicine in Uzbekistan.
Additionally, there are a number of shortcomings, especially in the health care system. One of them is to promote the rapid and easy use of digital technologies in the health care system, which ultimately involves the use of databases. These studies show that the use of chatbot services is widespread in developed countries.
The aim was to promote the rapid and easy use of digital technologies in the health care system, which ultimately involves the use of databases. It is also increasingly important to ensure that the public is properly aware of the clinical opportunities offered by these new technologies, and to ensure an optimal balance between social and individual benefits.
Conclusion
The Republic of Uzbekistan consistently solves all organizational issues of the development of AI technologies in all fields, including medicine, from the creation and adoption of the necessary regulatory documents and ending with the organization of the necessary innovation ecosystem to support startup initiatives in the field of AI.
Moreover, a number of universities in Uzbekistan have created bachelor's and master's degree programs in the field of AI technologies, which will give impetus to the development of human capital with the experience and necessary skills to develop intelligent AI systems.
Nevertheless, there remains a certain gap in the digitalization of the healthcare system and the transition to electronic healthcare in the Republic of Uzbekistan, which hinders the process of development and implementation of AI technologies in medicine. In this connection, the issue of training and retraining of personnel with experience and understanding of the work of electronic and digital healthcare, versed in intelligent and expert systems based on AI technologies, is acute.
The results show that at a time when the number of cases with Covid-19 is increasing, it is necessary to further increase the number of high-tech bots being developed in the healthcare system and ensure that they are perfectly developed and widely used in practice.
In almost all developed countries, especially in the field of medicine, artificial intelligence technologies have been promoted and widely used. The need for this trend is growing. Research and analysis show that the United States, Europe, and India have the highest number of Chatbot users.
Most countries' digital responses include a combination of big data analysis, integration of national health insurance databases, tracking travel history from person location databases, code scanning, and online person reporting. What is lacking in the COVID-19 pandemic around the world is an integrated approach to digital health governance. Bulk surveillance and contact tracing that collect
personal data should not be used by government agencies without public scrutiny, but should be associated with contactless anonymized digital health technologies.
In the Republic of Uzbekistan, digital solutions for tracking contacts with AI, including chatbots, are still under development. Although a number of options for mobile COVID-19 contact tracing applications have been proposed, they have not been able to find their place in the official anti-epidemic measures of the Uzbek government to combat the spread of infection. Nevertheless, the effective possibilities of such digital solutions for the epidemiological prevention of infection at the level of communities, cities and countries are beyond doubt.
Digital chatbots using AI can become a tool in the fight against COVID-19 and similar pandemics. However, from the above literature review of the current state of the art note that AI systems are still in preliminary stages, it will take time before results are seen. Very few of the examples and models of digital Chatbot solutions we've reviewed have operational maturity at the given stage.
References
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9. 'AVA" chat bot Healify group, India, New Delhi, [Internet] Heallify -Coronavirus Symptoms Checker Tool COVID-19 is developed based on the guidelines of WHO and Ministry of Health and Family [Cited 2022 Jan 19 02] Available from: https://coronavirus.heallify.com/
10. Sacha Altay, Anne-Sophie Hacquin, Coralie Chevalier, Hugo Mercier, The French National Centre for Scientific Research. Chatbot for addressing COVID-19 vaccine hesitancy. October (28, 2021). DOI: 10.1037/xap0000400
11.Hswen Y, Brownstein JS, Xu X, Yom-Tov E. Early detection of COVID-19 in China and the USA: summary of the implementation of a digital decision-support and disease surveillance tool. BMJ Open 2020 Dec 10; doi: 10.1136/bmjopen-2020-041004
12. Covid-19 Preliminary Test website, developed by the Ministry of Health Uzbekistan. [Internet]. [Cited 2022 Feb 02]. Available from: https://coronavirus.uz/uz
13. @covid19_checkerbot Covid -checker bot TashPMI Medical University. 2021 April [Cited 2022 Feb 10] [Internet] Available from: https://tashpmi.uz/podrazdeleniya-instituta/czentry/innovaczionnyj-czentr/.
CREDIT - MODULAR SYSTEM AND ITS PRINCIPLES OF IMPLEMENTATION (IN TEACHING MATHEMATICS IN HIGHER
EDUCATION INSTITUTIONS)
T. Ismailov
Abstract: This article analyzes the theories of application of credit-modular system in higher education institutions of Uzbekistan, teaching mathematics and their importance, in which the content and essence of the credit-modular system, priorities, and the work done in the transfer of the system to this system, as well as the author's recommendations and opinions in this regard are presented on a scientific basis.
Keywords: Credit-modular system, module, modular teaching technology, credit, credit teaching technology, credit-modular system in teaching mathematics.
