Health Information Management Master's Degree – The Impact of Artificial Intelligence on the COVID-19 Pandemic: Exploring Image Processing, Disease Tracking, Outcome Prediction, and Computational Medicine
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Health Information Management Master's Degree
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Best Master Degrees In Information Technology 2023
Received: 15 December 2022 / Revised: 5 January 2023 / Accepted: 9 January 2023 / Published: 11 January 2023
Artificial intelligence (AI) is a branch of computer science that enables machines to work efficiently by analyzing complex data. Research on artificial intelligence has increased tremendously, and its role in healthcare and research is accelerating. This review discusses the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google Scholar using specific keywords and phrases such as “Artificial Intelligence”, “Pharmaceutical Research”, “Drug Discovery”, “Clinical Research”, “Disease Diagnosis” etc. select studies and review articles published in the last five years. The application of artificial intelligence in disease diagnosis, digital therapy, personalized treatment, drug discovery, and prediction of epidemics and pandemics was discussed in detail in this article. Deep learning and neural networks are the most commonly used artificial intelligence technologies; Bayesian nonparametric models are potential technologies for clinical trial design; natural language processing and wearable devices are used to identify patients and monitor clinical trials. Deep learning and neural networks have been used to predict outbreaks of seasonal flu, Zika, Ebola, tuberculosis, and COVID-19. With the development of artificial intelligence technologies, the scientific community can witness rapid and cost-effective research in the field of health care and pharmaceuticals, as well as provide improved services to the general public.
Artificial Intelligence (AI) is a combination of various intelligent processes and behaviors developed using computational models, algorithms, or a set of rules that help a machine mimic human cognitive functions such as learning, problem solving, etc. [1, 2]. AI is rapidly penetrating healthcare and is having a major impact on clinical decision-making, disease diagnosis, and automation . There are opportunities for further exploration of artificial intelligence in pharmaceutical and health research due to its ability to explore huge data in different ways . Some of the current research is developing the use of artificial intelligence in healthcare and other sectors. Artificial intelligence technologies in healthcare include machine learning (ML), natural language processing (NLP), physical robots, robotic process automation, etc. . In ML, neural network models and deep learning with various features are applied in imaging data to identify clinically significant features at early stages, especially in cancer-related diagnoses [ 6 , 7 ]. NLP uses computational techniques to understand human speech and derive its meaning. Recently, ML techniques have been widely integrated into NLP for unstructured database data and records in the form of doctors’ notes, laboratory reports, etc. and treatment options . Continuous disruptive innovation creates a way for patients to receive accurate and rapid diagnosis and personalized treatment . AI-based solutions have been identified that include platforms that can use different types of data, ie. Patient-reported symptoms, biometrics, imaging, biomarkers, etc. Advances in artificial intelligence have made it possible to detect potential disease in advance, leading to a greater chance of detection through very early-stage prevention. Physical robots are used in various healthcare segments including nursing, telemedicine, cleaning, radiology, surgery, rehabilitation, etc. [10, 11]. Robotic process automation uses technology that is inexpensive, easy to program, and can perform structured digital tasks for administrative purposes and act as a semi-intelligent user of systems. It can also be used in conjunction with image recognition. In the healthcare system, tasks such as prior authorization, updating patient records, and recurring billing can use this technology .
While focusing on the pharmaceutical sector, the role of artificial intelligence cannot be ignored due to its wider application at different stages. The impact of artificial intelligence on all stages of pharmaceutical production from drug discovery to product management is very clear. In drug discovery, artificial intelligence technologies are used in both drug screening and drug development; algorithm includes, to name a few, ML, Deep Learning, Quantitative Structure-Activity Relationship (QSRL) AI-based technologies, QSLRML, Virtual Screening (VS), Support Vector Machines (SVM), Deep Virtual Screening, Deep Neural Networks ( DNN) , recurrent neural networks (RNN), etc. Neural networks and artificial intelligence are inspired by biological neural networks, where after processing the received information, there is a response to the input and output. Artificial neural networks (ANNs) have several connected units for processing information. DNNs are similar to ANNs where there are multiple layers of data processing units. RNNs process the data sequentially, whereby the output of the previous analysis is processed as the input for the next stage of analysis. SVMs are used for classification and regression of input data. In pharmaceutical product development, artificial intelligence is used to select appropriate excipients, select the development process, and ensure compliance with specifications such as compliance. Model Expert System (MES), INM, etc. are used in the development of pharmaceutical products. In manufacturing, artificial intelligence is used in automated and customized manufacturing, matching manufacturing errors to set limits. Artificial intelligence technologies such as metaclassifiers and tablet classifiers are used to achieve the desired quality of the final product . Incorporating artificial intelligence into clinical trials helps with subject selection and process monitoring, with failures reduced through close supervision. ML is used in clinical trials . Artificial intelligence technologies such as ML and NLP tools are used in market analysis, product positioning, and product costing . Recently, some AI-related articles have been published discussing the applications of AI in medicinal chemistry, healthcare, pharmaceutical and biomedical research, especially in protein target identification, automated drug design, virtual screening and in silico pharmacokinetic evaluation, with a focus on diagnosis of diseases. on diagnosis and treatment of cancer [15, 16]. AI has made extensive inroads into the aforementioned sectors and has led to improved outcomes. Due to the wide application of artificial intelligence in healthcare and the pharmaceutical industry, this review includes articles related to the application of artificial intelligence in disease diagnosis, drug discovery, clinical trials, personalized treatment and epidemiological studies, as well as epidemic and pandemic prediction. Research related to the application of artificial intelligence in pharmaceutical manufacturing, education, market analysis, customer service, commercialization and anything not related to medical/pharmaceutical research is excluded from this review. All studies were searched using domains such as PubMed, Science Direct and Google Scholar for specific keywords.
Master Of Science In Health Informatics
Analysis of the disease becomes key in developing careful treatment and protecting the well-being of patients. Human-created imprecision creates an obstacle to accurate diagnosis, as well as misinterpretation of the information obtained, creating a dense and complex task. AI can have a variety of applications, providing real confidence in accuracy and efficiency. After a lively review of the literature, the application of various technologies and methodologies for disease diagnosis was reported. With the evolution of the human population, there is always a growing demand on the health care system depending on the various manifestations of the environment .
A substantial body of evidence has shown that although there are vulnerable, conflicting inconsistencies that are not amenable to analysis, the development of new methods can determine applicability by reflecting a real-world scenario.
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