Artificial Intelligence In Medicine Pdf – The Impact of Artificial Intelligence on the COVID-19 Pandemic: A Study of Image Processing, Disease Monitoring, Outcome Prediction, and Computational Medicine
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Artificial Intelligence In Medicine Pdf
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Artificial Intelligence In Medical Imaging
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By Subrat Kumar Bhattamisra 1, * , Priyanka Banerjee 2, Pratibha Gupta 2, Jayashree Mayuren 3, Susmita Patra 2 and Mayuren Candasamy 4
The Missing Pieces Of Artificial Intelligence In Medicine: Trends In Pharmacological Sciences
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, able to analyze complex data. Research focused on AI has increased, and its role in healthcare and research is rapidly emerging. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. Literature was retrieved from domains such as PubMed, Science Direct and Google scholar using keywords and specific phrases such as ‘Artificial Intelligence’, ‘Pharmaceutical Research’, ‘Drug Discovery’, ‘Clinical Trial’, ‘Disease Diagnosis’, etc. selected research and review articles published in the last five years. The use of AI in disease diagnosis, digital therapy, personalized medicine, drug discovery and epidemic or pandemic prediction was explored in detail in this article. Deep learning and neural networks are the most widely used AI technologies; Bayesian nonparametric models are potential clinical trial design techniques; natural language processing tools and wearables are used in diagnosing patients and monitoring clinical trials. Deep learning and neural networks have been applied in predicting seasonal flu, Zika, Ebola, Tuberculosis and COVID-19 outbreaks. With the development of AI technologies, the scientific community can see faster and cost-effective healthcare and pharmaceutical research as well as providing better service to the public.
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 perform human cognitive functions such as learning, problem solving, etc. 2]. AI is rapidly gaining ground in the healthcare sector and is having a major impact on clinical decision making, disease diagnosis and automation [3]. There are opportunities for AI to explore further in the field of medicine and healthcare research due to its ability to analyze large amounts of data from various methods [4]. Some current research is expanding on the use of AI in healthcare and other sectors. 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 are applied with different features in imaging data to identify clinically important features in the early stages, especially in cancer-related diagnosis [ 6 , 7 ]. NLP uses computational techniques to understand and extract meaning from human speech. Recently, ML techniques are widely applied in NLP to analyze unstructured data in databases and records in the form of medical notes, laboratory reports, etc. and treatment options [8]. Continuous disruptive innovation creates a way for patients to receive accurate and rapid diagnoses and tailored treatment interventions [9]. AI-based solutions have been identified that include platforms that can use different types of data, viz. patient-reported symptoms, biometrics, imaging, biomarkers, etc. With advances in AI, the ability to detect potential disease well in advance is becoming possible, resulting in a greater likelihood of early prevention. Physical robots are used in different areas of health care including nursing, telemedicine, hygiene, radiology, surgery, rehabilitation, etc. [10, 11]. Robotic process automation uses technology that is cheap, easy to program and capable of performing set digital tasks for administrative purposes and acting as a semi-intelligent user of 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 wide applications at various stages. The impact of AI is very evident in all stages of pharmaceutical products from drug discovery to product management. In drug discovery, AI technologies are used in drug testing and drug design; algorithms include ML, deep learning, AI-based structure activity techniques, QSLRML, virtual visualization (VS), support vector machines (SVM), deep virtual visualization, deep neural networks, to name a few. DNN), recurrent neural networks (RNN), etc. Neural networks in AI are inspired by biological neural networks that have an input and output response after processing the received information. An artificial neural network (ANN) has many connected units for information processing. DNNs are similar to ANNs in that they contain multiple layers of data processing units. The RNN processes the data in sequence so that the output data of the previous analysis is processed as the input data for the next stage of the analysis. SVMs are used for classification and regression of input data. In pharmaceutical product development, AI is used to select appropriate additives, select the development process, and ensure that properties are reliably achieved during the process. Model expert system (MES), ANN, etc. are used in the development of pharmaceutical products. In manufacturing, AI is used in automated and personalized production, adjusting manufacturing errors to certain limits. AI techniques such as meta-classifier and tablet classifier are used to achieve the desired quality in the final product [13]. The participation of AI in clinical trials helps to select subjects and monitor the trial, due to close monitoring the deviations are reduced. 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 articles related to AI include the application of AI in medicinal chemistry, healthcare, pharmaceutical and medical research, especially in target protein identification, computer-aided drug design, virtual validation and in evaluation in silico pharmacokinetics, diagnosis of the focus disease is discussed. cancer diagnosis and treatment [15, 16]. AI has widely invaded the above mentioned sectors and led to better results. Due to the widespread use of AI in the healthcare and pharmaceutical industries, this review includes articles related to the application of AI in disease diagnosis, drug discovery, clinical testing, personalized treatment, and epidemiological research in epidemic or pandemic prediction . Research related to the application of AI in pharmaceutical production, education, market analysis, customer service, commerce, and anything unrelated to healthcare/pharmaceutical research is excluded from this review. All studies are searched using domains such as PubMed, Science Direct and Google scholar using specific keywords.
Artificial Intelligence In Healthcare: Transforming The Practice Of Medicine
Analyzing the disease becomes important in designing effective treatment and protecting the health of patients. Human-caused inaccuracy creates an obstacle to accurate diagnosis, as well as misinterpretation of the information produced, making it a difficult and challenging task. AI can have various applications by providing the right confidence in accuracy and efficiency. After an extensive literature review, applications of various technologies and methodologies for the purpose of disease diagnosis have been reported. With the development of the human population, according to different environmental phenomena, there is always an increasing demand for the health care system [17].
A lot of important evidence has revealed that even if there are poor, contradictory, non-analytical findings, the development of new methods can determine eligibility by shaping the current scenario that is not included [18, 19, 20]. It is important to categorize patients according to whether they are severely affected by diseases, and AI can play an important role in diagnosis [21]. The diagnosis is called
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