The Psychology Of Innovation In Healthcare And Medical Breakthroughs
The Psychology Of Innovation In Healthcare And Medical Breakthroughs – The Impact of Artificial Intelligence on the COVID-19 Pandemic: A Survey of Image Processing, Disease Tracking, Outcome Prediction, and Computational Medicine
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The Psychology Of Innovation In Healthcare And Medical Breakthroughs
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Understanding The Psychology Of Creativity And The Big Five
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Methodology For A Mental Health Plan For Health Care Workers
By Subrat Kumar Bhattamisra Subrat Kumar Bhattamisra Scilit Preprints.org Google Scholar 1, * , Priyanka Banerjee Priyanka Banerjee Scilit Preprints.org Google Scholar 2, Pratibha Gupta Pratibha Gupta Scilit Preprints.org Google Scholar 2, Scyashure Mayen Mayen. Scholar 3, Susmita Patra Susmita Patra Scilit Preprints.org Google Scholar 2 and Mayuren Candasamy Mayuren Candasamy Scilit Preprints.org Google Scholar 4
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 allows machines to work efficiently, analyzing complex data. Research focused on AI has increased dramatically, and its role in healthcare services and research is emerging faster. This review discusses the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from areas such as PubMed, Science Direct and Google Scholar using keywords and specific phrases such as ‘Artificial Intelligence’, ‘Pharmaceutical Research’, ‘Drug Discovery’, ‘Clinical Study’, ‘Disease Diagnosis’, etc. select the research and review articles published in the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and prediction of epidemics or pandemics has been extensively reviewed in this article. Deep learning and neural networks are the most widely used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks have been applied to predict the outbreaks of seasonal flu, Zika, Ebola, tuberculosis and COVID-19. With the development of AI technologies, the scientific community can see rapid and cost-effective healthcare and pharmaceutical research as well as provide better service to the public.
Challenges For The Evaluation Of Digital Health Solutions—a Call For Innovative Evidence Generation Approaches
Artificial intelligence (AI) is a combination of various intelligent processes and behaviors, developed by computational models, algorithms or a set of rules that support the machine to imitate the cognitive functions of humans such as learning, problem solving, etc. [1, 2]. AI is rapidly penetrating the field of the health sector and is having a major impact on clinical decision making, disease diagnosis and automation [3]. There are opportunities to further explore AI in the field of pharmaceutical and health research due to its ability to explore huge data from different methods [4]. Some of the current studies are developing on the use of AI in healthcare and other sectors. The AI technologies in the healthcare industry include machine learning (ML), natural language processing (NLP), physical robots, robotic process automation, etc. [5]. In ML, neural network and deep learning models with different features are applied in imaging data to identify clinically significant elements in the early stages, especially in cancer-related diagnosis [6, 7]. NLP uses computational techniques to understand human speech and derive its meaning. Recently, ML techniques have been widely integrated in NLP for unstructured data in the database and records in the form of doctors’ notes, laboratory reports, etc. and treatment options [8]. The ongoing disruptive innovation creates a way for patients to receive accurate and rapid diagnosis and personalized treatment interventions [9]. AI-based solutions have been identified that include platforms that can use a variety of data types, viz. Patient-reported symptoms, biometrics, imaging, biomarkers, etc. With the advances in AI, the ability to detect potential disease well in advance is possible, leading to an increased likelihood of detection as a result of very early stage prevention . Physical robots are used in various healthcare segments including nursing, telemedicine, cleaning, radiology, surgery, rehabilitation, etc. [10, 11]. The robotic process automation uses technology that is cheap, easy to program and can perform structured digital tasks for administrative purposes and act as a semi-intelligent user of the systems. This can also be used in conjunction with image recognition. In the health care system, tasks such as prior authorization, updating patient records and billing, which are repetitive, can use this technology [12].
While focusing on the pharmaceutical sector, the role of AI cannot be ignored due to its wider applications in various stages. The influence of AI across all stages of pharmaceutical products from drug discovery to product management is very evident. In drug discovery, AI technologies are used in drug screening and drug design; algorithm includes, to name a few, ML, Deep Learning, AI-based Quantitative Structure-Activity Relationship (QSRL) technologies, QSLRML, Virtual Screening (VS), Support Vector Machines (SVMs), Deep Virtual Screening, Networks Deep Neural (DNNs), Recurrent Neural Networks (RNNs), etc. Neural networks and AI are inspired by biological neural networks, where there is an input and output response after processing the received information. Artificial neural networks (ANN) have several connected units for information processing. DNNs are similar to ANN where there are several layers of data processing units. RNNs process the data sequentially, where the output data of the previous analysis is processed as input data for the next stage of the analysis. SVMs are used for classification and regression of input data. When developing a pharmaceutical product, AI is used to select the appropriate excipients, select the development process, and ensure that specifications such as compliance are met during the process. The Model Expert System (MES), ANNs, etc. are used in the development of pharmaceutical products. In manufacturing, AI is used in automated and customized manufacturing, matching manufacturing errors to specific limits. AI technologies such as meta-classifiers and tablet classifiers are used to achieve the desired quality in the final product [13]. Incorporating AI into clinical trials helps to select subjects and monitor the process, the failures are reduced due to close supervision. ML is used in clinical trials [14]. AI technologies such as ML and NLP tools are used in market analysis, product positioning and product costing [13]. Some of the recently published AI-related articles have discussed the application of AI in medicinal chemistry, healthcare, pharmaceutical and biomedical studies, especially in target protein identification, computer-aided drug design, virtual screening and in evaluation in silico pharmacokinetics, disease diagnosis. focus on cancer diagnosis and treatment [15, 16]. AI has conquered the above sectors extensively and has led to an improvement in the outcome. Due to the widespread use of AI in healthcare and the pharmaceutical industry, this review includes articles related to the application of AI in disease diagnosis, drug discovery, clinical testing, personalized treatment, and epidemiological research and prediction of epidemics or pandemics. The studies related to the application of AI in pharmaceutical manufacturing, education, market analysis, customer service, commercialization, and anything not related to healthcare/pharmaceutical research are excluded in this review. All studies are searched using domains such as PubMed, Science Direct and Google Scholar with specific keywords.
Disease analysis becomes critical in planning careful treatment and safeguarding patient well-being. The inaccuracy produced by humans creates an obstacle to a correct diagnosis, as well as the misinterpretation of the information produced, which creates an intensive and demanding task. AI can have various applications by bringing real assurance of accuracy and efficiency. After a lively literature survey, applications of various technologies and methodologies for disease diagnosis purposes have been reported. With the evolution of the human population, there is always an increasing demand for the health system, according to various environmental manifestations [17].
What Are The Applications Of Technology Based Mental Health Interventions?
A considerable amount of evidence has revealed that even if there are fragile, contradictory inconsistencies that cannot be analyzed, the development of new methods can define competence by portraying the actual current situation.
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