Mesopotamian Journal of Artificial Intelligence in Healthcare

In the era of the development of information technology, artificial intelligence has become a vital part of the process of developing electronic applications and platforms.Recent years have witnessed tremendous growth in the adoption of artificial intelligence and machine learning techniques in healthcare services and helping healthcare workers to track patients and produce medicines, as well as in emergency medicine. The possibilities of artificial intelligence in the medical domain are enormous and include magnetic imaging applications, disease prediction, continuous monitoring of vital signs, and monitoring the spread of epidemics. This article will discuss the importance and role of artificial intelligence in emergency medicine while highlighting its profitable contributions to assisting healthcare workers

The COVID-19 pandemic is considered one of the most dangerous diseases that have emerged in recent years, as it began at the end of 2019 in China and quickly spread to become a severe epidemic. This virus has become, in a short period of time, threatening millions of the earth\\'s populations, and infected individuals show symptoms that may lead to the loss of their lives. In this article, the author concentrates on the significance and methods of artificial intelligence strategies in facing COVID-19 and the primary role of these technologies.AI techniques have been applied in various areas, including creating diagnostic tools, predicting disease transmission, identifying new treatments, and improving resource allocation. Moreover, these techniques have been employed to analyze medical images to find symptoms of COVID-19 in patients, speeding up diagnosis and reducing transmission risks.Public health professionals can more efficiently allocate resources thanks to machine learning models that have been trained on data from other pandemics to anticipate the spread of COVID-19 and identify possible outbreaks. Large datasets of currently available medications have also been analyzed using AI to find potential new molecules.

In recent years, the term Health 4.0 has appeared in health services and is related to the concept of Industry 4.0. The term Health 4.0 focuses on replacing traditional care in hospitals and medical clinics with home health services that are based on artificial intelligence techniques through the use of telemedicine applications that allow the monitoring of patients in a virtual environment. This term is utilized to represent digital change in the healthcare sector. Governments aim to develop the level of medical care in hospitals and clinics to ensure the provision of healthcare benefits at low costs and increase patient satisfaction. It has become vital for hospitals to grow their environment into digital environmentsin their services through the use of a set of computer programs based on artificial intelligence. Artificial intelligence techniques in Health 4.0 provide a set of procedures that benefit patients and healthcare workers, including early diagnosis, make inquiries into treatment, data analysis, reports on the patient\'s condition, and others. The primary purpose of this article is to determine the significance of Health 4.0 and AI techniques in healthcare by mentioning the most important benefits and weaknesses of using AI techniques in healthcare.

In the medical field, specialists aspire through the use of artificial intelligence to obtain accuracy in the results and details of patients. Such as automatic detection and prediction of diseases and obtaining medical results by applying artificial intelligence techniques such as machine learning algorithms to clinical data. Therefore, because coronary artery disease (CAD) is one of the most prevalent types of diseases in the world and with the highest rate of injuries, one of the machine learning algorithms will be used in this research to diagnose this disease through artificial intelligence. Patient clinical factors were used, a data set of 303 people. The Support Vector Machine (SVM) algorithm is used and applied to the clinical factors of patients, which are 56 variables, as they are considered one of the most important clinical factors that can be used to detect CAD. This algorithm is considered to have good predictive ability in medical clinical characteristics. The Support Vector Machine algorithm model, which was used in this research, provided the highest prediction accuracy of (96.7%), with an AUC value of (71.5%). This indicates the effectiveness of the SVM algorithm in classifying the clinical patient data set that was adopted in this research. After applying it to the research data set, the SVM algorithm showed its ability to classify and predict well. The accuracy of the algorithm model was (96.7%). This indicates that this algorithm can help cardiologists in different ways during their daily practices to predict coronary artery disease, which is one of the most common heart diseases.

Healthcare is considered one of the most critical areas that contribute to the development of the global economy, as it has made significant developments in science and technology. The remarkable development in information technologies and the digital revolution has revolutionized the development of health institutions through the digitization of all medical records and the management of clinical operations. The process of automating medical records is one of the most important processes that are significantly developed in healthcare services. Artificial intelligence technologies contribute to the operation of digitizing medical records through a set of applications that rely on artificial intelligence to help specialists complete work quickly and with high efficiency. These applications are vital in simplifying operational procedures and reducing costs in administrative or clinical contexts. Artificial intelligence contributes to speeding up diagnosis and treatment tasks, improving the quality of health services, and reducing human errors. This paper explores the importance of digitization in healthcare and the role of artificial intelligence in administrative processes. This paper concluded that digitization contributes to developing the environment of health institutions and assisting doctors in diagnosing diseases and tracking patients accurately

The eye is a critical part of the human being, as it provides complete vision and the ability to receive and process visual details, and any deficiency in it may affect vision and loss of sight. Corneal topography is one of the essential diagnostic tools in the field of ophthalmology, as it can provide important information about the cornea and the problems that appear in it. Artificial intelligence strategies contribute to the development of the healthcare domain through a group of approaches that have a significant and vital impact on improving the field of ophthalmology. The primary purpose of this paper is to highlight the efficiency of artificial intelligence in extracting features from corneal topography and how these techniques contribute to helping ophthalmologists diagnose corneal topography. Furthermore, the focus is on the performance of AI algorithms, their diagnostic capabilities, and their importance in helping physicians and patients. The effects of this paper confirm the effectiveness and efficiency of artificial intelligence algorithms in the clinical diagnosis of various eye concerns

In the field of medicine, artificial intelligence has become a useful tool, particularly in the diagnosis and treatment of disorders with malignant tumours. Deep learning and machine learning algorithms, for example, have significant promise for increasing the precision and effectiveness of tumour diagnosis and treatment. The importance of AI in diseases with malignant tumours is examined in this work, with particular attention paid to its function in medication discovery, therapy prediction, and medical imaging analysis. It also emphasizes the difficulties and restrictions related to the application of AI, such as problems with poor data quality, as well as the requirement for legal and moral considerations. Basically, AI offers exciting possibilities to improve personalized treatment, early detection, and research developments in oncology, but careful consideration must be given to ensure appropriate and successful integration into clinical practice.

Artificial intelligence-assisted surgical robots combine the agility and accuracy of robotic systems with the cutting-edge capabilities of AI technology. These advanced surgical robots are made to help surgeons carry out difficult surgical procedures more precisely and effectively. Surgical robots with AI algorithms built in can analyse enormous volumes of patient data, make choices in real-time, and adjust to changing circumstances. The surgical process and patient safety could both be considerably improved by this technology. Several important advantages of surgical robots powered by AI include Enhanced Accuracy Surgical robots can make extremely precise motions with little error because of the capabilities of robotics and AI, leading to more accurate surgical procedures. During an operation, real-time decision-making AI algorithms can evaluate data from a variety of sources, including patient vitals, medical imaging, and surgical history, to give surgeons on-the-spot recommendations and insights. Artificial intelligence-driven surgical robots can automate tedious and repetitive activities, freeing surgeons to concentrate on more important choices and increasing operating room productivity. By analysing patient data and delivering individualized insights, AI can help surgeons with preoperative planning, resulting in better surgical approaches and outcomes. AI technology's sophisticated skills can assist in identifying potential dangers and difficulties during surgery, allowing surgeons to proactively address them and reduce unfavourable events

Medical image diagnostics is crucial to healthcare since it aids in the diagnosis and treatment of a variety of diseases and conditions. However, the process is time-consuming and prone to human error. In recent years, artificial intelligence has been a powerful tool for enhancing medical imaging diagnosis. Using AI algorithms for medical picture interpretation has the potential to revolutionise the field by improving accuracy, efficacy, and standardization. These algorithms can quickly sift through enormous amounts of medical imaging, finding anomalies, quantifying features, and providing useful information to help medical professional-s make judgments. AI-based systems can be used to track the evolution of diseases, plan treatments, and highlight particular areas of interest in medical pictures. Additionally, AI systems can aid in case triage by classifying cases according to urgency, enabling quick response to life-or-death situations. Healthcare practitioners can gain from increased diagnostic accuracy and efficiency, improved workflow management, and standardized interpretations by utilizing AI in medical imaging diagnostics. However, it's crucial to understand that AI complements human expertise rather than replacing it. To ensure a safe and efficient application in clinical settings as AI technologies continue to evolve and advance, continuing research and collaboration between AI developers and healthcare practitioners is essential. Medical image diagnosis is poised to advance significantly with continued AI integration, ultimately improving patient outcomes and healthcare delivery

Artificial intelligence (AI) plays a vital role in the development of pharmaceuticals. The whole drug development process, from target identification and lead optimization to clinical trials and post-marketing monitoring, has the potential to be revolutionized by AI. Data analysis is a crucial area where AI thrives. To find trends, correlations, and prospective targets for therapeutic intervention, AI systems can analyse enormous volumes of data, including electronic health records, molecular databases, academic literature, and clinical trial data. This saves time and resources by enabling researchers to decide which compounds or pathways to investigate. Lead optimization is another area where AI is essential since algorithms can screen and forecast the efficacy of new drug candidates. To improve efficacy, safety, and pharmacokinetics, the most promising leads are prioritized, and their attributes are optimized. By examining past data to pinpoint patient groupings that are most likely to respond to a certain medication, AI can also improve clinical trial design and patient selection. This advances success rates aids in the development of personalized medication and can support post-marketing surveillance by continuously scanning real-world data for adverse events or unexpected drug-drug interactions after a medicine has been licensed. This permits the quicker detection of potential safety issues and contributes to maintaining the continuous safety of licensed medications. However, some obstacles must be overcome to completely utilize AI in medication development. These include the requirement for high-quality data, assuring AI model transparency and interpretability, taking into account ethical issues, and regulatory frameworks for using AI in drug development.

Brain diseases are considered life-threatening malignant diseases. Malignant brain diseases, such as glioblastoma multiforme (GBM) and metastatic brain tumours, present significant challenges as doctors and healthcare workers must constantly monitor the patient\\'s condition due to their aggressive nature and limited treatment options. In recent decades, artificial intelligence techniques and practices have developed significantly in medical imaging disease diagnosis and contributing to the development of vaccines and vaccines. In this article, we will highlight the ability of artificial intelligence techniques to analyse and diagnose malignant brain diseases, enhance early detection of the disease, and help doctors treat patients. This article concluded that artificial intelligence is an essential part of assisting physicians to make suitable decisions in real-time, and it also has a significant role in clinical practices, improving patient outcomes and providing excellent healthcare to patients.

A persistent mental illness that changes thoughts, feelings, behaviour, and communication with people is schizophrenia. Delusions, hallucinations, distorted thinking, lack of motivation, and fear of society are often its hallmarks. Although there is currently no cure for schizophrenia, patients can greatly benefit from early diagnosis and treatment. The field of artificial intelligence (AI) is very promising for the diagnosis and treatment of schizophrenia. Artificial intelligence systems can scan and analyse vast amounts of data, including genetic and hereditary data, brain scans, and medical records, and predict when a person might develop schizophrenia if the factor is hereditary. This can help medical providers act quickly to start treating patients before their symptoms worsen. AI can also help patients with schizophrenia manage and regulate their medications. Artificial intelligence systems can analyse patient data to determine the best medications and dosage amounts while also highlighting any potential side effects of treatment. This can help medical providers customize strategies structure treatment for each patient and enhance treatment outcomes. Considering all the above-mentioned matters, the use of artificial intelligence in diagnosing, analysing,and treating schizophrenia promises to improve patient outcomes and treatment, deepen our knowledge of this complex disease, early detection of the disease, and find appropriate treatment.

Artificial intelligence systems are receiving significant attention in the healthcare domain because they have a considerable impact on the development of patients' electronic medical records (EMRs). Artificial intelligence systems have the ability to develop healthcare institutions and assist physicians in making the proper decisions. These systems have the ability to perform diagnosis, provide appropriate treatment, and perform administrative tasks in hospitals. Healthcare institutions seek to integrate hospital systems with computer systems and construct large warehouses to store data and medical records for each patient. These methods allow machine learning and deep learning algorithms to analyse diseases, predict and determine new patterns for diagnosis and grow pharmaceutical applications. Big data is one of the essential things that artificial intelligence seeks to generate and develop algorithms to analyse, extract unique patterns from, and create strategies to deal with. In this article, the matter of integrating artificial intelligence systems in the healthcare domain, growing hospitals, and helping doctors and healthcare workers track patients and improve their health conditions will be analysed. This article is a straightforward description of the essence of artificial intelligence in healthcare

Over the past few decades, healthcare sector has developed along parallel paths with trendy technology. Modern practices of artificial intelligence strategies have led to improved diagnosis, the development of effective treatments, telemedicine, and the development of patient monitoring devices. Many applications have emerged that help doctors and healthcare workers, most notably Chatbot. It is a program that utilises artificial intelligence techniques to conduct conversations in real-time. This program is able to simulate that you are a natural person writing or talking to another Internet user, which is the application. This article discusses the matter of Chatbot technology in developing electronic healthcare services and helping patients communicate electronically to obtain information that supports them in improving their health condition

Depression is a common and complex mental health condition that affects millions of people in the world. Medical advice, medications, and constant medical supervision by a specialist are common components of traditional treatment methods. Recently, there has been a growing interest in the potential of artificial intelligence to improve the diagnosis, monitoring, and treatment of depression. The potential of artificial intelligence algorithms has been demonstrated in the development of chatbots, or virtual agents, that can provide treatment, assistance, and support to individuals with depression. These artificial intelligence (AI) systems can simulate therapy sessions, offer strategies, monitor progress in treatment phases, and speak in natural language. Artificial intelligence has the potential to play an important role in the early diagnosis and prognosis of depression. By analysing multiple data sets and information such as genetic information, patient medical records, and social media posts using the Internet, artificial intelligence algorithms can identify individuals vulnerable to depression and distinguish them from normal humans. This facilitates the implementation of interventions and preventive measures at the right time and day. AI can also be used to improve depression treatment strategies. By analysing massive databases of patient data, AI systems can determine the ideal drug combinations, doses, amounts, and combinations for each patient. This personalized approach can lead to better treatment outcomes and reduces the trial-and-error process typically required to determine the best action. While AI has the potential to treat psychological depression, it is important to keep in mind that AI should never replace qualified and helpful medical professionals. Artificial intelligence in treating depression seeks to enhance and support the care provided by therapists, psychologists, and psychiatrists, rather than replace human communication and knowledge

Our study aims to identify the state of cybersecurity in the healthcare domain. Cyber incidents, including ransomware and similar cyber-attacks, impact healthcare entities. The review highlights the government's efforts to protect citizens' health information by passing laws regulating the healthcare industry. The review targeted healthcare-related laws in the United States, the European Union, Singapore, and India. The study identified that while developed countries like the United States, the European Union, and Singapore have health data privacy laws, developing countries like India still need data privacy laws. The nature, value, and sensitivity of data retained by healthcare entities make the healthcare domain a rich target for cyber threat actors. Based on the study, the paper proposes security practices, including security monitoring, secure network architecture, information technology vulnerability management, cyber policies, and user training, that can help prevent cyber-attacks on healthcare entities

For the early identification of diabetes, a chronic illness that affects millions of people globally, artificial intelligence (AI) shows enormous promise. AI algorithms can recognize diabetes signs and give patients and healthcare providers early warnings by examining a variety of data sources, suchas medical records, patient histories, and lifestyle factors. AI's capacity to evaluate sizable and intricate datasets is one of its main advantages in the diagnosis of diabetes. AI is capable of taking into account a broad range of variables that could lead to diabetes, such as age, BMI, food preferences, physical activity, and genetic predisposition. Artificial intelligence (AI) systems can recognize early indicators of diabetes and forecast the risk of developing the condition by analysing patterns and relationships among these variables. AI can also be applied to customize diabetic care and screening. Healthcare providers can improve patient outcomes and lessen the burden of disease by customizing suggestions and treatment options for each patient. AI can assess a patient's risk of diabetes by looking into their lifestyle choices and medical history. Healthcare providers can focus treatments and preventative actions to lower the chance of illness onset by identifying high-risk individuals can identify early indicators of diabetes by analysing blood glucose levels, demographic data, and lifestyle factors. Because of this, medical practitioners may be able to intervene early on, when the illness is most amenable to treatment systems that can monitor blood glucose levels and examine patient histories to create customized diabetic treatment programs. This can involve individualized prescription schedules, workout programs, and food advice. All things considered, AI has the power to completely transform the waypeople with diabetes are managed by giving patients and healthcare providers individualized data-driven insights. The use of AI in clinical practice is fraught with difficulties, such as privacy issues and a dearth of standardized data, yet the advantagesin identifying and treating diabetes are substantial.