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The Impact of Artificial Intelligence Visualization on the Medical Field

Introduction

The artificial intelligence (AI) visualization age is already with us. Without a doubt, its popularity has already overtaken the medical field, and its promises have grown (Keravnou & Lavrac, 2001). Artificial Intelligence visualization included robotics, machine learning, and natural language processing, and any field of medicine can use AI. AI is a term used to describe computers and technology used to replicate human beings' critical thinking and intelligent behavior. For the last sixty years, various private medical practice sites and public healthcare facilities have adopted revolutionary technological development to increase supervision. In the early centuries, medical practitioners and nurses used to check the heartbeat manually. We currently have specialized equipment types that match the patients' heartbeat and tell the doctors if there is any issue with the heart that can cause any disease if not urgently addressed. Machinery invention has now made way into the human world, and it has positively embraced. The question arises: how artificial intelligence and technology benefit the medical field? The best response to this question lies in the information technology field in a global society. Information technology is a new trend and speedingly develops science (Keravnou & Lavrac, 2001).

Upgrading a multi-level and illustrative ending in the investigation of technology and Artificial Intelligence visualization effects on the medical field will help establish a more detailed and possible analysis. This study will capture the economic, social, human enjoyment, and ethical impact of applying the new medical trends in technology (Keravnou & Lavrac, 2001). This research will use various peer-viewed journal articles, news articles, and new books on future, recent, and past technological machines and devices. These research sources will be important in providing information to the research. They will explain how technology and Artificial Intelligence application in the medical field and their effects on the nurses, doctors, and the medical field (Lidstromer & Ashrafian, 2020).

In this systematic literature review, we will look into how Artificial Intelligence visualization can 'humanize' the healthcare sector and improve medicine in the future (Lidstromer & Ashrafian, 2020). With the speedy development of technology fast increasing in the medical field, it is essential to select ways to affect its workforce and practice. Correspondingly, the field of medicine keeps evolving and transforming as time goes. Hence, to determine whether technology and Artificial Intelligence impact or will keep impacting the medical field, we need to adopt various perspectives to help with comprehensive and inclusive research results and consequences (Keravnou & Lavrac, 2001).

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2.0 Aim and objectives of the study

The main aim of the study is to explore evidence the effect of artificial intelligence visualization has on the healthcare systems. In doing so, the researcher formulates the following objectives:

1. To critically improvement impacts that artificial intelligence visualization has on healthcare industry.

2. To critically the effects artificial intelligence visualization in Telehealth, drug development and disease diagnosis

3. To critically understand the background of artificial intelligence in Medicine

4. To establish the ethical implications of artificial intelligence visualization in the medical field.

In a bid to clarify on the purpose of the study, the researcher also formulates research questions that would guide the whole research process. The development of the most appropriate research questions increases the chances of developing a successful research project (Ratan, et al., 2019).

a. What are the improvement impacts that artificial intelligence visualization has had on healthcare industry?

b. What are the effects of artificial intelligence visualization in Telehealth, drug development and disease diagnosis?

c. What is the background of artificial intelligence in Medicine?

d. What are the ethical implications of artificial intelligence visualization in the medical field?

3.0 METHODS

Over the last few decades, there has been an increased number of studies published about the role of Artificial Intelligence in the healthcare sector. The increased number of publications and availability of a number of literature on various roles of artificial intelligence visualization are increasingly complex. Sufficient knowledge and evidence from various sources informing various decisions is important. For the purposes of looking the effects of artificial intelligence visualization on the healthcare system. This study adopts a systematic literature review design. This is a methodological design involved in evaluating, analyzing and discussing available evidence relating to a particular topic.

A systematic review is considered to have a high level of evidence in regards to evidence based. This type of evidence therefore provide feasible effects that artificial evidence has had in the healthcare sector in line with contemporary evidence-based medicine. In conducting this study, this part details the literature search strategy, the selection of the right databases, the inclusion and exclusion criteria used, critical appraisal, search results and the selection of literature used for the review.

2.1 Research topic

The topic identified and purpose of the study should inform the whole research process. The research topic identified should be relevant, ethical, novel, interesting and clear. The general research question formulated should therefore have a clear, well-defined and logical stature. In this systematic literature review it will guide the researcher in establishing the facts required in framework development. Amodio et al (2017) indicate that use of framework guides the researcher in their analysis of the problem based on the existing conditions. The framework involves identification of population (P), exposure (E), and outcome (O). Based on this, the key word include; artificial intelligence visualization, healthcare system, effects on healthcare systems.

2.2 Generation of key words

In most quantitative evidence synthesis, the PICO (Population, Intervention, Comparison, and Outcome) framework helps in formulating the research question with the above characteristics. According to Methley et al (2014), this framework is more sensitive in a quantitative evidence study compared to other specific approaches. In order to retrieve a more comprehensive search depending on the resource and time limitations, the PICO tool is commonly used (Methley, et al., 2014). The table below summarizes the use of the PICO framework in coming up with the research question or topic.

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The Effects of artificial intelligence Visualization on healthcare systems

Furthermore, the purpose of the research is to critically explore effects of artificial intelligence visualization on healthcare systems. The researcher goes on further to list the specific objectives that would help him achieve this aim in the introduction. The research focus and purpose enabled the researcher to conduct the literature search as follows.

2.3 Search strategy

The researcher builds a basic search strategy based on the formulation of the research topic (PICO framework). The search strategy includes free-text terms such as the title and the purpose of the research. This also includes any appropriate indexing of the subject expected to retrieve studies that may prove to be eligible. From the formulation of the research topic and the development of the research purpose, the researcher generated some key terms that would be used as a search strategy in the systematic review. These key terms include healthcare system, artificial intelligence, and visualization. The key terms generated have been summarized in the table below:

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2.4 Database selection

In relation to a systematic review, it is crucial to select the most appropriate databases (Bettany- Saltikov, 2012). In existence, there are various electronic databases with different literature on various clinical research topics. Additionally, the literature to be selected and used in this study had to be articles with Scopus and Schimago ratings Q1-Q4. In this regard, the researcher concentrated on certain databases that would enable the retrieval of much more practical and analytical articles on the research title selected. As a result, the following databases were selected: PUBMED, BMJ, NCBI and BNI.

2.5 The inclusion and exclusion criteria

The study focuses on the effects of artificial intelligence visualization on healthcare systems. Relevance is therefore a significant element in selecting the articles to be used in the research. Foundationally, the literature included in the study had to be relevant to the topic identified. Also, only literature published in the English language were included. This is because the researcher is conversant with the English language. Furthermore, English is the official language in regards to the research setting. Only articles published in full and after the year 2001 were also included in the study. This is because the most recent articles contain the most applicable and appropriate information on the subject matter. This criterion also ensures the inclusion of reliable, valid and credible articles. The summary of the inclusion and exclusion criteria has been shown below.

Table 3: Summary of the Inclusion and Exclusion criteria used in the identification of articles used for the review

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2.6 Critical appraisal

In the research process, it is essential to guide the facilitation and evaluation of the research articles that the researcher identifies. This research uses the CASP tool in the critical appraisal process, similar to most clinical researches. The Critical Appraisal Skills Programme (CASP) tool is effective in the specific and versatile evaluation of evidence in terms of credibility, reliability and validity. In order to effectively inform clinical practice, and act as evidence-based research, it is crucial for the researcher to use articles that are reliable, valid and credible.

3.0 Results

3.1 Search results

The preliminary search resulted in over 100 records consisting of various articles, publications and sources. The preliminary search conducted was necessary in the validation of the idea presented in the research topic. After the preliminary search, literature search was conducted using the search strategy; this involved a basic generation of the key terms and use of the relevant key words. As a result, around 16 full text articles were retrieved. The use of the inclusion and exclusion criteria and the CASP tool in terms of relevance and validity further excluded a number of articles. The PRISMA diagram shows the results of the literature search process.

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3.2 Literature used

The literature search is one of the foundational steps of a systematic review. For a successful research, this occurs in two stages: the preliminary search and the use of an appropriate search strategy. In order to ensure the validity of the proposed research topic, and avoid duplication of already addressed questions, it is necessary to conduct a preliminary search. This also assures the study of the availability of enough articles on the research focus. The preliminary search was conducted via a simple search in Google Scholar. The results can be seen in figure 1: the PRISMA diagram. The following are the sources used;

Abhimanyu S. Ahuja, (2019). The impact of artificial intelligence in medicine on the future role of the physician. PMC.

Bouton, Chad E., et al., (2016). "Restoring cortical control of functional movement in a human with quadriplegia." Nature 533.7602: 247.

Dilsizian, Steven E., and Eliot L. Siegel, (2014). "Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment." Current cardiology reports 16.1: 441.

Esteva, Andre, et al., (2017). "Dermatologist-level classification of skin cancer with deep neural networks." Nature 542.7639: 115.

Golden, J. A. (2017). Deep learning algorithms for detection of lymph node metastases from breast cancer. JAMA, 318(22), 2184. https://doi.org/10.1001/jama.2017.14580.

Greenberg, T. (2017). Trust in patient-physician relationships. The psychological impact of acute and chronic illness: A practical guide for primary care physicians. Springer Science & Business Media.

Karakülah, Gökhan, et al., (2014) "Computer-based extraction of phenotypic features of human congenital anomalies from the digital literature with natural language processing techniques." MIE.

Keravnou, E., & Lavrač, N. (2001). AIM portraits: Tracing the evolution of artificial intelligence in medicine and predicting its future in the new millennium. Artificial Intelligence in Medicine, 23(1), 1-4. https://doi.org/10.1016/s0933-3657(01)00071-9

Loh, E. (2018). Medicine and the rise of the robots: A qualitative review of recent advances of artificial intelligence in health. BMJ Leader, 2(2), 59-63. https://doi.org/10.1136/leader-2018-000071

Peleg, M., & Combi, C. (2013). Artificial intelligence in medicine AIME 2011. Artificial Intelligence in Medicine, 57(2), 87- 89. https://doi.org/10.1016/j.artmed.2013.01.001

Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018). Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States. Insights into Imaging, 9(5), 745-753. https://doi.org/10.1007/s13244-018-0645-y

Rathnam, C., Lee, S., & Jiang, X. (2017). An algorithm for direct causal learning of influences on patient outcomes. Artificial Intelligence in Medicine, 75, 1- 15. https://doi.org/10.1016/j.artmed.2016.10.003

Somashekhar, S. P., et al., (2017) "Abstract S6-07: Double-blinded validation study to assess the performance of IBM artificial intelligence platform, Watson for oncology in comparison with Manipal multidisciplinary tumor board– the First study of 638 breast cancer cases." S6-07.

Tekkeşin, A. İ. (2019). Artificial intelligence in healthcare: Past, present, and future. The Anatolian Journal of Cardiology. https://doi.org/10.14744/anatoljcardiol.2019.28661

Warner, E., Wang, N., Lee, J., & Rao, A. (2020). Meaningful incorporation of artificial intelligence for personalized patient management during cancer: Quantitative imaging, risk assessment, and therapeutic outcomes. Artificial Intelligence in Medicine, 339-359. https://doi.org/10.1016/b978-0-12-821259-2.00017-x

3.3 Data analysis

In relation to a systematic review, it is crucial to select the most appropriate databases (Bettany- Saltikov, 2012). In existence, there are various electronic databases with different literature on various clinical research topics. Additionally, the literature to be selected and used in this study had to be articles with Scopus and Schimago ratings Q1-Q4. In this regard, the researcher concentrated on certain databases that would enable the retrieval of much more practical and analytical articles on the research title selected. As a result, the following databases were selected: PUBMED, BMJ, NCBI and BNI.

3.0 Sythesis Findings

The Study adopted a thematic approach where findings were obtained. The results were systematically obtained and compared based on different literature reviews.

3.1 Background of artificial intelligence in Medicine

All the studies indicate that the popularity of Artificial intelligence visualization grew between the years 1960 and 1970, albeit in small bits. The rapid growth of Artificial Intelligence visualization was not explicitly happening in the medical field but the sciences as a whole (Keravnou & Lavrac, 2001). Edward Feigenbaum and Joshua Lederberg orchestrated the first Al problem-solving machine. The primary purpose of the machine was chemical analysis. The machine was known as Dendral, and it analyzed complex compounds carrying molecules such as carbon, oxygen, nitrogen, among others. Overtime, Dendral became essential to the medical field. Dendral use was to find out the structure of molecules in a compound and make accurate automated decisions to solve chemical analysis problems while mirroring the competence and understanding of chemists and other earlier machines (Keravnou & Lavrac, 2001). The Dendral laid the ground for a new wave of artificial machines that could moderately change the healthcare sector overtime (Lidstromer & Ashrafian, 2020).

Artificial Neural Networks (ANN) is a widely used form of Artificial Intelligence visualization in the medical field. It is almost related to biological neural networks because several computer processors are commonly known as neurons. The processors can also work with the same neural complexities of the human brain. They can learn from previous experiences, disorganized process data, and analyze any data that included non-linear data (Lidstromer & Ashrafian, 2020).

Artificial Neural Networks began to escalate in the 1990s when researcher William Bazt assessed using the ANN's in 1991. The study he conducted had over more than three hundred and fifty adult patients suffering from anterior pains in the chest (keravnou & Lavrac, 2001). Medical practitioners used Artificial Neural Networks to know the length at which the diagnostic specificity and sensitivity to decide whether the patient had heart attack complications or was suffering from acute myocardial infarction. The medical practitioners who carried out the diagnoses recorded 80% sensitivity and 85% specificity. On the other hand, Artificial Neural Networks recorded 97% specificity and 98% sensitivity (keravnou & Lavrac, 2001). They concluded that Artificial Neural networks were more accurate and detailed than human physicians' diagnoses.

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In the 21st century, there has been a vast development in Artificial Intelligence visualization and innovations in the medical field (Lidstromer & Ashrafian, 2020). The invention of new machines has helped solve any issues affecting the world's medical area during this period, especially human personnel shortage. One of the significant conditions widely affecting the world is chronic stroke targeted by the innovations. Before escalating to a fatal stroke, machine learning (ML) has been essential in detecting and treating stroke and related diseases (Lidstromer & Ashrafian, 2020). A scientific study of algorithms and models of statistics used in computers to alter data analysis is the primary role of artificial intelligence (Lidstromer & Ashrafian, 2020).

A principal component analysis (PCA) is a distance-based ordination procedure used mainly to showcase various data patterns. PCA, together with the genetic fuzzy, states that use machines to decide how multiple variables related to a specific medical condition and the extent of the medical condition's perception (Lidstromer & Ashrafian, 2020). Before the introduction of machine learning, it was hard to detect early signs of stroke. Only patients who could see early signs of stroke were the ones who could get treatment early. A stroke in the human body occurs when blood supply to the brain is interrupted or lowered, stopping brain tissues from receiving sufficient oxygen and nutrients. A blood clot mainly causes a stroke, or a condition called a thrombus which blocks blood vessels. Early detection of the premature blood vessel helps the blockage machine invented to assist medical practitioners in detecting early thrombus and treating them before the condition escalates. The device designed could detect early thrombus by monitoring the neck movements of a patient. Once they see any abnormal neck movements, they will send a signal to alert the medical practitioner on the issue's exact location. On discovering the problem, the doctors solve it by putting the patient into a stroke or related conditions treatment (Lidstromer & Ashrafian, 2020).

In recent times, technology innovations have speedingly evolved the healthcare sector and practice in the world (Peleg & Combi, 2013). At the significant point of medical technology and invention lies AI. In the medical field, the term AI means automated treatment and taking good care of patients. It involves the process of patient data collection, making it to practice, getting to know more about their conditions and ailments, and prescribing the best medication. Despite how successful and considerate medical technology has been in transforming and upgrading care to the patient's advantage, artificial intelligence is better than any other method because of its ability to learn quickly and with deep understanding. It can quickly process information just like the human brain and practice with better clarity and effectiveness than a doctor's or a human nurse (Peleg & Combi, 2013). Artificial intelligence visualization has contributed so much in the medical field in various machines like the Germwatcher set to examine patients' infections. AI also introduced robotic surgical systems, which enhance accuracy in surgeries and lowers the risks of patients losing their lives of harmful germs getting in their wound during surgery and reducing internal infection. Through a study carried out, it determined that human hands could not perfect a precise surgery. Having AI robotic surgical systems helps develop quality care and lowers the possibility of losing lives (Lidstromer & Ashrafian, 2020).

As artificial intelligence visualization acceptance keeps growing in the medical field, its effect on the healthcare sector and involved parties is yet to be comprehensively exhausted(Peleg & Combi, 2013). Some of the expected impacts of artificial intelligence include precision enhancement that has reduced human errors, use of resources, earlier and fast detection of medical conditions symptoms that could go unnoticed by medical practitioners. Various computers which function as the human brain are still in the primary stage of upgrading. However, these computers are showing significant signs of evolution with time. Computer evolution will come with different economic, ethical, and social factors, with some of the computers currently taking effect in the AI-controlled healthcare system (Peleg & Combi, 2013).

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4.0 Discussion

4.1 Effects of AI in Telehealth, drug development and disease diagnosis

Artificial intelligence visualization has got the capability of using computer algorithms to make conclusions in the medical industry without any help from human. With rates of human error in the healthcare industru on the rise, AI has come handy with helping professionals come up with solutions that otherwise could have been difficult to undertake. Human negligence and lack of proper concerntartion may have been the main reason why medical errors happen (Tekkesin, 2019). Artificial Intelligence seems to be addressing this problem by ensuring that medical functions are undertaken with high level of accuracy, right speed, and precision. It is through this that motarity rate as a result of human error has been reduced for the few past years that it has been in use. It is important that indicate the positive impact that Artifficial Intelligence has had in the medical field (Tekkesin, 2019). Atleast all function of the healthcare system have been positively been affected through Artificial Intellingence. Disease diagnosis, drug devepment and telehealth are some of the areas in which Artificial Intellingence has had its impact felt.

Disease diagnosis is the process through which a patient is examined and the symptoms and signs are explained to ensure the problem is established. Diagnosis is one area that AI has had a very positive impact (Tekkesin, 2019). It reduces time taken for a medical staff to discover the kind of problems that patients face. There have been new forms of treatments that have been enhanced by AI. They are very accurate and they enable the work of the nurse and doctor to be more easier. Several lives have been saved due to application of AI in their treatment processes (Tekkesin, 2019). AI provides information that is more vital and accurate that doctors and nurses can follow and ensure that every aspect concerning the condition is thoroughly dealt with. The information obtained through AI is in such a way that quick conclusions wil be made at the end.

Telehealth is another area that AI has had an impact in the medical field. It has made it easy for the provision of medical care to patients even without visisting of medical facilities. Telehealth is a technology bases way of medical service provision. Patients are able to receive health information services, patient and provider education, selfcare, and medical care through the use of digital communication means or telecommunication (Loh, 2018). Artificial intelligence visualization in the use of telehealth technologies has promoted the best ways of monitoring patients suffering and ensuring the best treatment is given before the health condition of a patient worsens. Through artificial intelligence technologies, constant monitoring of a patient's condition has been boosted in terms of speed and efficiency, and also it has enables the ability to notice changes that may be less distinguishable by humans. In the provision of services, patients are able to undertake their medical checkups and receive results without necessarily visiting the medical facilities (Loh, 2018). They always ensure that all aspects of health information is easily available guiding people on various steps that will assist them have better medical conditions and have control of the spread of disease.

Drug development is the process of manufacturing a new pharmaceutical drug to the market once all the requirements have been identified through the process of drug discovery (Peleg & Combi, 2013). Artificial intelligence visualization technologies use knowledge from humans and learn from various experiments and solutions results that are produced to communicate both specific and complex problems in drug development. This activity has made the processes of treatment to become much easier since the drugs are readily available , it has also sped up the process of healthcare among many patients. Other than discovery of drugs, AI has enabled advanced methods of drug administration that are targeted in specific areas that are affected. There are some drugs that have an impact if they are not accurately administered, it is at this areas that AI has helped in a great way (Peleg & Combi, 2013). Targeted drug administration is one of the areas that medical staff have had problem requiring assistance from machines in the process.

Artificial intelligence visualization has brought various benefits in the healthcare industry specifically in disease diagnosis, telehealth, drug development. In disease diagnosis, which is the process of determining which disease or condition explains a person’s symptoms and signs, this has facilitated treatment levels since symptoms and signs are easily detected by the use of artificial intelligence technologies (Peleg & Combi, 2013). Telehealth which is the facilitation of health and health-related services including medical care, provider, and patient education, health information services, is similarly using Artificial intelligence visualization technologies to promote the best ways of monitoring patients suffering and to ensure the best treatment is given before the health condition of a patient worsen (Bouton et.al. 2017). Drug development which is the way new drug comes into existence or being manufactured. With the aid of artificial intelligence technologies, discoveries of new drugs have been made, this has in turn improved and facilitates treatments of patients to greater heights.

4.2 Impacts of AI in the Medical Field

The enhancement of artificial intelligence visualization has come up with various changes. Most positive changes have improved the healthcare system and reduced deaths by early detection of fatal health conditions. Early detection of diseases with early response to treatment of the illnesses has helped save lives (Peleg & Combi, 2013). Nevertheless, a question remains; an artificial intelligence performs nurses, doctors, and other healthcare officers' roles?

It is unclear whether artificial machines can replace human resources, as most of them are still in their early development stages and are yet to reach their full potential (Loh, 2018). However, these machines are showing a significant improvement in their functionality with the future capability of addressing human resource shortage in the healthcare field affecting the world. For the longest time, the healthcare sector has been experiencing a scarcity of doctors. The lack of enough doctors is due to exhaustion from old age, increasing demand for chronic care, and lack of trained medical professionals worldwide (Loh, 2018). The enhancement of various AI machines in various medical sector subfields will fill existing gaps currently experienced. The COVID-19 pandemic exposed a shortage of healthcare professionals, with most countries failing to respond effectively due to a lack of healthcare professionals. In cases where healthcare professionals promptly responded to the situation, many healthcare workers contracted the virus and ended up out of action. In other conditions, patients suffering from other diseases like cancer, diabetes, and blood pressure felt neglected as their doctors were assigned to treat COVID-19 patients.

Machines lack human attributes like compassion and empathy, and hence patients need to see human doctors as leading consultants. Moreover, we do not expect patients to trust AI as technology is encountered with mistrust immediately (Loh, 2018). Consequently, AI primarily handles important tasks but is restricted to leave the primary responsibility of patient handling with a human physician. A current clinical trial employs AI to determine target zones for neck and head radiotherapy more precisely and faster than a human being. A radiologist will still be responsible for delivering the therapy, but AI has an essential background in preventing the patient from harmful radiation (Peleg & Combi, 2013). AI system is ideal for situations where human resource is scarce as a single AI system can support many patients. In most TB-frequent countries, there is insufficient radiological expertise, especially in remote areas.

More specifically, in robot form, artificial intelligence will help deal with such scenarios of healthcare professionals shortage. Robotics can handle highly infectious patients and reducing contact of the patient to healthcare professionals from contracting the virus (Loh, 2018). It can also increase efficient care as doctors will have enough time to handle other less infectious patients. There are some helpful AI machines currently being developed, like Artificial Narrow Intelligence (ANI). ANI is vital in executing single tasks; this means that we can use technology to tailor-make the machine to perform specific tasks with a human healthcare professional's help. Various assumptions can arise from using the available AI forms to predict medical Alon's medical field effects when it develops fully. Despite AI not being fully developed, medical AI significantly changes the healthcare sector positively and negatively (Loh, 2018).

In the last years, AI has made a flurry of essential steps in the correct direction in developing the healthcare sector and enhancing quality care. AI has improved its diagnosis of various medical conditions, with most of them being those related to the human eye and vision treatment (Tekkesin, 2019). With the enhancement of technology, artificial intelligence visualization is one most influential information technology in ophthalmology. There has been significant development using artificially intelligent machines that can detect any eye or vision problem by scanning the eye and informing the healthcare professions of the exact place the eye is affected. The use of AI in ophthalmology mainly focuses on high incidence diseases, such as age-related macular, diabetic eye disease, premature eye disease, and age-related or congenital cataracts with lower retinal vein occlusion. As previously indicated, innovations such as deep learning machines can use neural networks containing interconnected computer networks to discover signs of a stroke and give enough time for the patient's medication and treatment. The learning machine has attested to be more reliable in diagnosing medical diseases or conditions, providing healthcare professionals with correct information, which they can use to treat patients. They can use the provided information to base their treatments to obtain exceptional patients results and medical outcomes (Tekkesin, 2019). We can conclude machines are almost accurate and better than human healthcare professionals in detecting and diagnosing diseases that could, at times, go unnoticed.

Medical artificial intelligence has increased the diagnosis and treatment of some common health issues like cancer (Tekkesin, 2019). Cancer is one of the killer diseases globally; the use of AI has significantly improved its detection. Currently, AI use in cancer research and care is in its early stages. Most researchers are focusing on methods of developing and not only executing methods of clinical practice. Most specifically, AI has improved skincare detection (Loh, 2018). New machines and innovations like computational and digital pathology, more specifically, are paving their way to advance diagnosis in the healthcare sector. They aim to provide exceptional opportunities to include these tools in the advancing healthcare sector. A recent study showed the availability of neural network AI with more than 120,000 biopsy and clinically detected images of the skin's cancer to discover the infection's extent—the results measured against the diagnosis of 20 certified dermatologists from the medical board. The effects on the diagnosis from the artificial machine were at par with that of the twenty dermatologists. It showed a level of competence that can compete with healthcare professionals and improve as medical AI keeps evolving with time (Loh, 2018). With increased investments, AI potentiality will result in more precise and fast diagnoses, improved decision-making, and better health results for cancer patients and those at risk.

Other than its substantial input in inpatient diagnoses, medical AI has also proven its importance in psychiatry and psychological medicine subfields. However, before artificial intelligence can affect psychiatry and psychological medicine fields, there are many factors to consider: data security and privacy, clinical governance, and capacity. A study conducted by several researchers utilized a machine learning model that used functional magnetic resonance imaging technology to measure brain function by assessing the flow of blood within the vessels in the human brain. The machine learning model aimed to identify the patients with negative or positive symptoms of schizophrenia, and it reported 74% accuracy, providing healthcare professionals with the data (Rathnam et al., 2017). Psychological conditions such as bipolar disorders and schizophrenia have proven to have brain imprints that machines can easily detect. However, other psychological thinking patterns, such as suicidal thoughts, are not as easy to see. The American Psychological Association has conducted several studies for more than fifty years to determine the chances that a person had suicidal thoughts, revealing that those chances are too low to decipher (Rathnam et al., 2017).

Patients' psychiatric assessment involves observing their mental state, and patients answered questionnaires that can be difficult and time-consuming sometimes (Loh, 2018). Artificial intelligence may enable more methods, like video and audio assessment, which have better predictive results and have a more significant objective. AI has helped diagnose with tools that help monitor the patients' progress in outpatient and inpatient settings. The successful collaboration of AI in healthcare will significantly improve the quality of care. New tools for diagnosis, monitoring, and treatment may refine patient results and re-balance clinician workload in psychiatry. AI might encounter challenges and numerous risks, but this innovation's implementation might be successful with careful navigation (Rathnam et al., 2017).

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In response, Vanderbilt University Medical Center researchers built a machine controlled by learning algorithms. The device reported impressive 80%-90% accuracy in determining whether one had long-term suicidal thoughts and 92% accuracy in determining whether an individual had short-term suicidal tendencies spanning a week or less. The machine analyzed and applied algorithms based on information regarding the subjects' suicide attempts and an analysis of their MRI neural signatures about life and death (Rathnam et al., 2017).

Currently, the use of AI treatment is commonly embraced by the healthcare sector. (Rathnam et al., 2017). AI treatments have always performed robotics technology that is still being considered a bot underdeveloped. Some of the most famous robotics cases stepping in to perform treatment duties include the intelligent surgical robot that repaired a pig's intestines. These fully automated treatment functions have proven that medical AI in the form of robotics can improve or transform healthcare and practice in the field of medicine once they have fully reached their expected potential (Tekkeşin, 2019).

AI's diagnostic prowess has proven to affect medical subfields, such as radiology and pathology, significantly (Golden, 2017). These fields both depend heavily on pattern-based diagnosis formulations, making AI imperative in enhancing diagnostic outcomes. AI and machine learning have proven of great importance by supplementing and approving clinicians' work, most specifically in the field of imaging analytics. Pathologists must carefully analyze medical images to treat patients; sometimes, analyzing hundreds of tissue slides can cause abnormalities (Tekkeşin, 2019).

Studies conducted by experts in pathology have predicted a futuristic lot dominated by AI and computers integrated into the field of pathology to improve accuracy and lessen the workload on human pathologists (Golden, 2017). According to a study conducted by researchers from Google, artificial intelligence integrated into pathology has been proven to perform at a rate or level of accuracy higher than human pathologists' performance level by 16%. This accuracy level was recorded when the AI machines tested against human pathologists to identify tumours in breast cancer images (Pesapane et al., 2018).

Overall, AI has been essential in assisting human healthcare providers in improving the quality of care and transforming practice for the better (Rathnam et al., 2017). However, there is a growing fear that AI might take over several physicians' jobs in different medicine subfields. Humans' replacement with machines may pick up in the coming months. Artificial intelligence should release humans from dull and dangerous roles to take more cognitive tasks to make the company more productive and raise their overall pay. In the past, technology was introduced gradually by giving employees time to shift into new undertakings. Those who were unfortunate to lose their jobs could find other alternatives: using severance pay, retrain, or file for unemployment to find a job elsewhere. When COVID-19 struck the world, the transition was abrupt to employers as they worried about sudden lockdown measures and a rush to replace humans with machines and software. There was no time to retrain the workers on the transition. The affected workers were left to gain new skills for their survival. As past and present evidence of AI's impacts on the medical field has proven, AI is poised to augment these healthcare professionals' jobs instead of taking over the entire medicine area (Rathnam et al., 2017). Even though it might be ignorant to deny the possibility of AI robotics taking over from nurses or surgeons due to their high efficiency and accuracy in disseminating care, it is still in its infant stages. It requires more technological input to be able to function without any human intervention or assistance. However, medical AI needs to be observed or supervised by human physicians at all times (Tekkeşin, 2019).

Nevertheless, AI is making some promising strides in treatment, diagnoses, and medical conditions' prediction functions (Loh, E. 2018). Despite these tremendous achievements, there are still other medical care aspects that AI machines or robotics cannot achieve. Things like making ethical decisions, empathetically caring for patients, generating a personal connection with a patient, and dealing with legal cases still require the human touch. Therefore, medical professionals can be assured of keeping their jobs in the future, even when the inevitable AI takeover of the medical field comes to pass (Tekkeşin, 2019).

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4.3 Effects of Artificial Intelligence on Healthcare Systems

Recent developments of artificial intelligent technology has led to heated debate across the healthcare sector. One of the major concerns is future replacement of doctors by robots. Though it may not be possible to replace human physicians, Artificial Intelligence will significantly help physician to make better decision and minimize human errors which have manifested the healthcare sector for long period (Bouton et.al. 2017). With the onset of Artificial Intelligence, there has been increased abundance the usage of healthcare data. Through the fast development of big data analytics the method has been utilized in the healthcare sector and it’s predicted to play a more significant role in the future. When Artificial Intelligence is directed by relevant and concise clinical questions useful clinical information will be obtained. Through this, it will be possible for certain difficult decision to take will be simplified (Bouton et.al. 2017). It may be a very difficult decision making process, but the benefits of technology are many by far and they cannot be compared with the kind of disadvantages that may arise.

Using of Artificial Intelligence in the healthcare sector can be linked to the number of significant benefits often discussed in the medical literature. Generally, Artificial Intelligence uses algorithms that obtain large amount of information on healthcare, which in return is used in gaining insight into the clinical practices (Bouton et.al. 2017). Besides, it can be fitted with self-correction capacities that improve the quality of feedback. All these factors lead to better provision of services that are informed. Through the improving of ease to access of information through Artificial Intelligence, physicians can use the system to obtain current medical journals, textbooks, and clinical reports. All these resources will help in making informed decisions (Bouton et.al. 2017). Additionally, Artificial Intelligence system has assisted in reducing diagnostic and therapeutic errors that were common in human-guided clinical practices. With large amount of data being analysed in real-time, patients can get quick reference on health risk alerts, and health outcome prediction. Below are key areas that have benefited from the adoption of Artificial Intelligence in provision of care.

4.3. 1 Healthcare Data

Though Artificial Intelligence has been alleged to put private data at risk, Artificial Intelligence has had a significant transformation in the medical field. Collection of data associated with a patient, historically has been known to be undertaken by professionals (Ahuja, 2019). Critical information such as previous health history, patient health condition, and family history has always been slow to get and sometimes challenging. The practice indicates the importance of data collection hence a spark to adoption of technology. In the recent past, medical facilities and hospitals have developed databases meant to organize information and its retrieval.

Through well-organized data the review process of data by professional is made easy and quick. With adoption of artificial intelligent data collection, crucial information is obtained hence, used for improvement of quality of services provided to patients (Ahuja, 2019). Artificial Intelligence has enabled healthcare institutions to match patients to treatment plans that can be associated to their family history, lifestyle and the conditions that affect them (Ahuja, 2019). Facilities like IBM’s Watson Health cloud have efficiently used this and obtained good results. The technology has reduced cases of human error, through ensuring informed decisions are taken. Human error can be source of complex problems in the lives of patients. It is important that all aspects of error will be reduced to almost zero allowing patients to have the best diagnosis and care.

Though they increase operation costs, Artificial intelligence systems have improved ways through which medical information is stored and retrieved. It has improved operational and clinical activities such as diagnosis treatment and screening. Initially, clinical data existed with limitations on demographics, physical examination, clinical laboratory images, and medical notes. However, with Artificial Intelligence, there is the use of data analytics at the diagnosis in the form of imaging, genetic testing, and electro-diagnosis (Ahuja, 2019). Radiologists have adopted use of Artificial Intelligence technologies when performing diagnostic imaging that contain large amount of information. Further, Artificial Intelligence is used in abnormal gene expression in the non-coding of RNAs when diagnosing gastric cancer (Ahuja, 2019). Electronic Diagnosis has further supported a system that localizes neural injuries where Artificial Intelligence is widely used.

In addition, clinical laboratory and physical examination results are key sources of clinical data. The data is separated from imaging, genetic and electrophysiological information because of large unstructured narratives of clinical notes that cannot be directly analysed (Karakula et.al, 2014). Therefore, Artificial Intelligence technology has helped to convert unstructured text information into the machine-readable electronic medical record (EMR). According to Karakula et.al (2014), artificial intelligence technologies have helped in extracting of phenotypic features from reports which enhance diagnostics and accuracy of congenital anomalies. The reports are essential n decision making processes. Nurses, doctors and other medical staff involved in these process are able to determine the course of action. If they could not be using AI, it could be an overwhelming task that otherwise many could not accommodate making patients ate time receiving the wrong medications.

4.3.2 Disease Focus

The application of Artificial Intelligence can be hailed for its concise diagnosis of diseases like cancer, nervous system ailments and cardiovascular diseases in which it has largely been applied. According to Somashekhar et.al. (2017), IBM Watson for oncology is the only reliable Artificial Intelligence system used in the diagnosis of cancer through double-blinded study. On the other hand, Esteva et.al 2017) analyses clinical images in the identification of dermatological cancer. In the study of neural diseases, Bouton et.al. (2017) developed an artificial intelligence system for restoration of controlled mobility for patients with quadriplegia. Also, Dilsizian and Siegel (2014) came up with a proposal on the potential use of Artificial intelligence system in coronary disease diagnostic through cardiac imagery. All these applications are meant to ensure that disease diagnosis takes place with ease and it can be detected at early stages. It shows the impact that Artificial Intelligence has on the medical profession because disease is the common war for these sector.

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Focusing on the three main diseases with a view of using Artificial Intelligence in improving the quality of health care services is an indication of the significance and seriousness of the technology. The three diseases are the world’s leading cause of human mortality. Therefore, early diagnostics is viewed to be the most important way to prevention (Bouton et.al. 2017). Through early diagnosis, patients can be advised on how to avoid severe complications. Also, early diagnostic is attainable through improved analysis procedures used in imaging, EMR, and gene data recording. All these form part of the strengths of Artificial Intelligence system. It is not only that it shows strength, but it brings about efficiency when it comes to dealing with complicated diseases. When complication of disease that come with diseases like cancer have been addressed the medical staff can direct there focus into other areas hence improving the patient experience and quality of care.

Apart from the above mentioned diseases, Artificial Intelligence can be applied when diagnosing other diseases (Ahuja, 2019). Recently, Artificial Intelligence systems have been used in collection of ocular imagery data and identification of the congenital cataract diseases. Further there has been an increase use of Artificial Intelligence in referable diabetic retinopathy through the retinal fundus photography. Also, when it comes with accident patients, Artificial Intelligence is able to assist locate fracture in the body assist medical staff taking the correct problem that may have arose in the process (Ahuja, 2019). It further helps to monitor the progress that patients enabling medical staff decide the best way to proceed with the healing procedure.

Artificial intelligence has got the capability of using computer algorithms to make conclusions in the medical industry without any help from human. With rates of human error in the healthcare industru on the rise, AI has come handy with helping professionals come up with solutions that otherwise could have been difficult to undertake. Human negligence and lack of proper concerntartion may have been the main reason why medical errors happen (Tekkesin, 2019). Artificial Intelligence seems to be addressing this problem by ensuring that medical functions are undertaken with high level of accuracy, right speed, and precision. It is through this that motarity rate as a result of human error has been reduced for the few past years that it has been in use. It is important that indicate the positive impact that Artifficial Intelligence has had in the medical field (Tekkesin, 2019). Atleast all function of the healthcare system have been positively been affected through Artificial Intellingence. Disease diagnosis, drug devepment and telehealth are some of the areas in which Artificial Intellingence has had its impact felt.

4.4 Ethical Implications of AI in the Medical Field

With every emerging trend in a field as sensitive as medicine comes the potential implications regarding ethics (Greenberg, 2007). The slow integration of advanced AI into treatment can be associated with the fear of the ethical issues that come with inpatient care. Most of the most established moral codes that govern practice do not include machines or artificial intelligence. For this reason, there is a thin and sometimes invisible line between what to consider right or wrong when it comes to the use of AI in medical care. The ethical codes of conduct about AI are not explicitly drawn because of most AI's infant stages. However, with the significant transformations they bring to medical care and practice, there is the need to consider the ethical implications of these machines that threaten to take over the entire medical field shortly (Warner et al., 2020).

Over time, the debate on whether machines can mirror human biases that can cloud their decision-making has come to the forefront of AI machines' ethics. Some AI devices in other fields have already proven to depict biases in decision-making (Warner et al., 2020). There is, therefore, a higher risk of medical AI programming to practice biases, especially since the medical field is very diverse with individuals from different ethnicities or racial backgrounds. Preferences can build into AI machines unintentionally. Still, they can also be re-programmed to shed off those biases and perform their functions based on well-informed and untainted decisions (Greenberg, 2007).

One central theme to be addressed in this issue is how to balance the advantages and risks associated with AI technology(Warner et al., 2020). There are advantages of carefully integrating AI with the healthcare system, as it can upgrade the healthcare system and improve patient quality of delivery and care. Despite the opportunities associated with AI, there is a need to reduce its implementation's ethical risks. The risks might include threats to informed consent, privacy and confidentiality, patient independence, and AI integration into clinical practice. Stakeholders ought to be encouraged to integrate AI as a tool to complement physicians and not as their replacement. On the use of AI robots during surgery, there is a need to declare the significance of informed consent and AI technology use management. The possible threats related to using this technology must be transparent to the participants (Greenberg, 2007). Robots can change the end of life care by helping people remain independent and lower the need for home and hospital care. AI and humanoid design developments allow robots to have 'conversations and other social interactions with patients to retain relationships.

Moreover, there is maintaining patient privacy and confidentiality when handling patient information (Greenberg, 2007). Since machines are increasingly taking over doctors, nurses, and physicians' duties, there is the question of whether they will protect personal patient information. To ensure that machines perform their tasks at the required level, they need to feed with deep learning algorithms based on a wide array of personal patient information. This information must be handled with the utmost care and protected from third parties' access, a task that healthcare professionals have been successfully partaking in for years. AI machines in other fields are already presenting cybersecurity issues that make them vulnerable to hackers and crackers targeting confidential or personal information. Patients might, therefore, be unwilling to provide AI machines with all the information they need to function effectively. Withholding information may not enable them to receive the high-quality care they need from AI machines, hence proving a problematic issue for AI use inpatient care (Warner et al., 2020).

The protection of patient information confidentiality is also closely related to the close relationship that patients share with doctors or healthcare professionals in the field (Warner et al., 2020). The relationship between patients and doctors has been an essential aspect of the field of medicine. However, with AI technology continuing to integrate itself into the medical field, the patient-doctor relationship is increasingly being threatened. The bond generated by the personalized care that patients often receive whenever they interact with their doctors appears to be diminishing. As previous roles carried out by doctors are now done by AI technology, which has dramatically reduced patient-doctor contact. More worryingly, several studies have proven that this bond is essential in influencing positive patient outcomes due to the personalized touch of care they receive. Since machines cannot effectively mirror human emotion, they have been incapable of feeling empathy or providing customized care to patients (Greenberg, 2007). Therefore, their increased use in the medical field has significantly led to doubts about their inability to establish an empathetic connection with the patients and improve patient outcomes in every medicine department (Pesapane et al., 2018).

The lack of a bond with patients has reduced because integrating AI machines and robotics into the medical field would enlarge the current socioeconomic gaps in the medical field regarding access and affordability of medical care (Loh, E. 2018). If past trends are believed, AI will not equally benefit patients of all races and financial statuses. A significant number of patients cannot afford increasingly expensive healthcare costs. The introduction of advanced AI will push up healthcare costs even further, making it more challenging for all patients to afford it. Therefore, high-quality AI care will only be accessible and affordable to wealthy patients, widening the gap in healthcare accessibility between the rich and the poor. The poor will have to struggle to meet up with the expensive healthcare costs (Greenberg, 2007). Hence, as much as AI technology makes work more manageable, it also negatively impacts the poor's lives.

Another important ethical issue about using AI in the medical field would arise when machines and robotics have a task in making informed decisions regarding life and death. Whenever a patient loses their life while under the care of a robot AI, there would be ambiguity when deciding who to blame; the robotics or the engineers who built them (Loh, E. 2018). The medical field has strived for years to try and avoid such situations from arising. Therefore, there will always be the need to have a trained professional physician working in tandem with AI technologies to ensure that they supervise the machines and rectify any situation that requires a human touch. It will further ensure that AI machines do not cross the lines drawn by ethical codes and always operate within the supervised moral bounds. There is, therefore, a great need to closely supervise these machines whenever they are in operation (Greenberg, 2007).

Even though machines and AI have been recording exceptional improvements in performing tasks that healthcare professionals, they still require human observation to ensure that they do not engage in biases that result in machine errors and lead to the unnecessary loss of lives (Tekkeşin, 2019). Even though the possibility of human error is much higher than that of machine error, whenever machines commit mistakes, the ethical issues that can accompany the facility using it to care for patients can be too much to handle. AI will keep growing in popularity in the medical field, and machines or robotics are poised to work toe-to-toe with healthcare professionals shortly (Warner et al., 2020). It is, however, imperative to integrate AI in the medical field to lessen the load that doctors, nurses, and physicians have, but not to take over completely. Regardless of how effective they are, machines will always need human observation to ensure that they don't experience a malfunction and cause irreparable damage to patients (Pesapane et al., 2018).

5.0 Conclusion

With the rise of the world’s reliance on technology, the healthcare sector is increasingly embracing it in various aspects. The healthcare sector has significantly reaped from the adoption of technology through improved disease diagnostic systems and treatment plan through better management of patient information. Artificial Intelligence visualization has played a vital role in improving service provision and making informed decisions. The positive impact of Artificial Intelligence supersedes that of negative effects hence making it a significant inclusion in the healthcare sector.

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Over the last one hundred years, the field of artificial intelligence visualization has undergone tremendous changes. Among the most impacted industries by these changes is medicine. AI has transformed the medical field and practice in many ways than never before. New learning machine technologies have made the diagnosis of medical conditions much more accessible, even going a step further to detect medical issues that have not begun to exhibit visible symptoms yet. Robotics can now be used to limit the risk of human error in critical surgical operations. However, as AI keeps growing in popularity within the medical field, concerns over AI machines taking over the entire medical practice field have been growing in equal measure. But these worries are misplaced. Given the importance of the human touch in establishing patient-doctor relationships or bonds and improving patient outcomes, AI is only poised to augment medical practice and work in tandem with healthcare professionals tasked with keeping close tabs on them. Even though jobs with essential tasks such as those held by nurses, doctors, and surgeons may not be under significant threat of being taken over by AI, jobs that require fewer tasks such as collection and storage of medical data might not survive the AI revolution in the medical field.

The ethical implications of using AI inpatient care present another reason why human professionals should put close tabs on AI machines while performing their assigned tasks. Sensitive scenarios involving making life or death decisions, as well as protecting patient information from malicious third parties, place AI machines at a disadvantage since they do not have empathetic connections with patients and are vulnerable to cyberattacks due to the large amounts of information they require to function effectively. AI has been, and promises to remain, a prominent force in the field of medicine. To ensure its success, engineers should not tailor them to replace healthcare professionals but complement them to improve care quality. Ethical issues will always arise in the medical field, and the problem can avert if humans and machines work together to limit errors and enhance accuracy in practice.

6.0 References

Abhimanyu S. Ahuja, (2019). The impact of artificial intelligence in medicine on the future role of the physician. PMC.

Bouton, Chad E., et al., (2016). "Restoring cortical control of functional movement in a human with quadriplegia." Nature 533.7602: 247.

Dilsizian, Steven E., and Eliot L. Siegel, (2014). "Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment." Current cardiology reports 16.1: 441.

Esteva, Andre, et al., (2017). "Dermatologist-level classification of skin cancer with deep neural networks." Nature 542.7639: 115.

Golden, J. A. (2017). Deep learning algorithms for detection of lymph node metastases from breast cancer. JAMA, 318(22), 2184. https://doi.org/10.1001/jama.2017.14580.

Greenberg, T. (2017). Trust in patient-physician relationships. The psychological impact of acute and chronic illness: A practical guide for primary care physicians. Springer Science & Business Media.

Karakülah, Gökhan, et al., (2014) "Computer-based extraction of phenotypic features of human congenital anomalies from the digital literature with natural language processing techniques." MIE.

Keravnou, E., & Lavrač, N. (2001). AIM portraits: Tracing the evolution of artificial intelligence in medicine and predicting its future in the new millennium. Artificial Intelligence in Medicine, 23(1), 1-4. https://doi.org/10.1016/s0933-3657(01)00071-9

Lidströmer, N., & Ashrafian, H. (2020). Artificial intelligence in medicine. Springer.

Loh, E. (2018). Medicine and the rise of the robots: A qualitative review of recent advances of artificial intelligence in health. BMJ Leader, 2(2), 59-63. https://doi.org/10.1136/leader-2018-000071

Peleg, M., & Combi, C. (2013). Artificial intelligence in medicine AIME 2011. Artificial Intelligence in Medicine, 57(2), 87- 89. https://doi.org/10.1016/j.artmed.2013.01.001

Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018). Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States. Insights into Imaging, 9(5), 745-753. https://doi.org/10.1007/s13244-018-0645-y

Rathnam, C., Lee, S., & Jiang, X. (2017). An algorithm for direct causal learning of influences on patient outcomes. Artificial Intelligence in Medicine, 75, 1- 15. https://doi.org/10.1016/j.artmed.2016.10.003

Somashekhar, S. P., et al., (2017) "Abstract S6-07: Double-blinded validation study to assess the performance of IBM artificial intelligence platform, Watson for oncology in comparison with Manipal multidisciplinary tumor board– the First study of 638 breast cancer cases." S6-07.

Tekkeşin, A. İ. (2019). Artificial intelligence in healthcare: Past, present, and future. The Anatolian Journal of Cardiology. https://doi.org/10.14744/anatoljcardiol.2019.28661

Warner, E., Wang, N., Lee, J., & Rao, A. (2020). Meaningful incorporation of artificial intelligence for personalized patient management during cancer: Quantitative imaging, risk assessment, and therapeutic outcomes. Artificial Intelligence in Medicine, 339-359. https://doi.org/10.1016/b978-0-12-821259-2.00017-x

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