Artificial Intelligence Adoption in the Post COVID-19 New-Normal and Role of Smart Technologies in Transforming Business: a Review

Journal of Science and Technology Policy Management, Journal Year: 2022, Volume and Issue: 15(3), P. 506 - 529

Published: Feb. 14, 2022

Purpose The purpose of this paper is to give an overview artificial intelligence (AI) and other AI-enabled technologies describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media entertainment, banking insurance, travel tourism. Furthermore, the authors discuss tactics in which information technology used implement business strategies transform businesses incentivise implementation these current or future emergency situations. Design/methodology/approach review provides rapidly growing literature on use smart during pandemic. Findings 127 empirical articles have identified suggest that 39 forms been used, ranging from computer vision technology. Eight different are using technologies, primarily services manufacturing. Further, list 40 generalised types activities involved including providing data analysis communication. To prevent spread illness, robots with being examine patients drugs them. online execution teaching practices simulators replaced classroom mode due epidemic. AI-based Blue-dot algorithm aids detection early warning indications. AI model detects a patient respiratory distress based face detection, recognition, facial action unit expression posture, extremity movement analysis, visitation frequency sound pressure light level detection. above applications listed throughout paper. Research limitations/implications largely delimited area COVID-19-related studies. Also, bias selective assessment may be present. In Indian context, advanced yet harnessed its full extent. educational system upgraded add potential benefits wider basis. Practical implications First, leveraging insights across industry sectors battle global threat, one key takeaways field. Second, integrated framework recommended for policy making area. Lastly, recommend internet-based repository should developed, keeping all ideas, databases, best practices, dashboard real-time statistical data. Originality/value As relatively recent phenomenon, comprehensive does not exist extant authors’ knowledge. emerging

Language: Английский

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A medical multimodal large language model for future pandemics

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: Dec. 2, 2023

Deep neural networks have been integrated into the whole clinical decision procedure which can improve efficiency of diagnosis and alleviate heavy workload physicians. Since most are supervised, their performance heavily depends on volume quality available labels. However, few such labels exist for rare diseases (e.g., new pandemics). Here we report a medical multimodal large language model (Med-MLLM) radiograph representation learning, learn broad knowledge image understanding, text semantics, phenotypes) from unlabelled data. As result, when encountering disease, our Med-MLLM be rapidly deployed easily adapted to them with limited Furthermore, supports data across visual modality chest X-ray CT) textual free-text note); therefore, it used tasks that involve both We demonstrate effectiveness by showing how would perform using COVID-19 pandemic "in replay". In retrospective setting, test early datasets; in prospective variant COVID-19-Omicron. The experiments conducted 1) three kinds input data; 2) downstream tasks, including disease reporting, diagnosis, prognosis; 3) five 4) different languages, English, Chinese, Spanish. All show make accurate robust decision-support little labelled

Language: Английский

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Artificial intelligence in healthcare delivery: Prospects and pitfalls

Journal of Medicine Surgery and Public Health, Journal Year: 2024, Volume and Issue: 3, P. 100108 - 100108

Published: April 16, 2024

This review provides a comprehensive examination of the integration Artificial Intelligence (AI) into healthcare, focusing on its transformative implications and challenges. Utilising systematic search strategy across electronic databases such as PubMed, Scopus, Embase, Sciencedirect, relevant peer-reviewed articles published in English between January 2010 till date were identified. Findings reveal AI's significant impact healthcare delivery, including role enhancing diagnostic precision, enabling treatment personalisation, facilitating predictive analytics, automating tasks, driving robotics. AI algorithms demonstrate high accuracy analysing medical images for disease diagnosis enable creation tailored plans based patient data analysis. Predictive analytics identify high-risk patients proactive interventions, while AI-powered tools streamline workflows, improving efficiency experience. Additionally, AI-driven robotics automate tasks enhance care particularly rehabilitation surgery. However, challenges quality, interpretability, bias, regulatory frameworks must be addressed responsible implementation. Recommendations emphasise need robust ethical legal frameworks, human-AI collaboration, safety validation, education, regulation to ensure effective healthcare. valuable insights potential advocating implementation efficacy.

Language: Английский

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Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Diagnostics, Journal Year: 2021, Volume and Issue: 11(7), P. 1155 - 1155

Published: June 24, 2021

Since December 2019, the global health population has faced rapid spreading of coronavirus disease (COVID-19). With incremental acceleration number infected cases, World Health Organization (WHO) reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential artificial intelligence (AI) this context is difficult to ignore. AI companies have been racing develop innovative tools contribute arm world against pandemic and minimize disruption it may cause. main objective study survey decisive role technology used fight pandemic. Five significant applications for were found, including (1) diagnosis using various data types (e.g., images, sound, text); (2) estimation possible future spread based current confirmed cases; (3) association between infection patient characteristics; (4) vaccine development drug interaction; (5) supporting applications. This also introduces comparison datasets. Based limitations literature, review highlights open research challenges could inspire application COVID-19.

Language: Английский

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An augmented Snake Optimizer for diseases and COVID-19 diagnosis

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 84, P. 104718 - 104718

Published: Feb. 17, 2023

Language: Английский

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Ensemble Deep Learning and Internet of Things‐Based Automated COVID‐19 Diagnosis Framework

Contrast Media & Molecular Imaging, Journal Year: 2022, Volume and Issue: 2022(1)

Published: Jan. 1, 2022

Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers deep learning models automated screening suspected cases. An ensemble and Internet Things (IoT) based framework proposed Three well-known pretrained are ensembled. medical IoT devices to collect CT scans, diagnoses performed on servers. compared with thirteen competitive over four-class dataset. Experimental results reveal that ensembled model yielded 98.98% accuracy. Moreover, outperforms all in terms other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, 98.57% AUC. Therefore, can improve acceleration diagnosis.

Language: Английский

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Medical images classification using deep learning: a survey

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(7), P. 19683 - 19728

Published: July 28, 2023

Language: Английский

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Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review

Big Data and Cognitive Computing, Journal Year: 2023, Volume and Issue: 7(1), P. 32 - 32

Published: Feb. 9, 2023

Preeclampsia is one of the illnesses associated with placental dysfunction and pregnancy-induced hypertension, which appears after first 20 weeks pregnancy marked by proteinuria hypertension. It can affect pregnant women limit fetal growth, resulting in low birth weights, a risk factor for neonatal mortality. Approximately 10% pregnancies worldwide are affected hypertensive disorders during pregnancy. In this review, we discuss machine learning deep methods preeclampsia prediction that were published between 2018 2022. Many models have been created using variety data types, including demographic clinical data. We determined techniques successfully predicted preeclampsia. The used most random forest, support vector machine, artificial neural network (ANN). addition, prospects challenges discussed to boost research on intelligence systems, allowing academics practitioners improve their advance automated prediction.

Language: Английский

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Explainable screening of oral cancer via deep learning and case-based reasoning

Smart Health, Journal Year: 2025, Volume and Issue: unknown, P. 100538 - 100538

Published: Jan. 1, 2025

Language: Английский

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Dual-path information enhanced pyramid Unet for COVID-19 lung infection segmentation

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 142, P. 109977 - 109977

Published: Jan. 5, 2025

Language: Английский

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