The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare

British Medical Bulletin, Journal Year: 2021, Volume and Issue: 139(1), P. 4 - 15

Published: Aug. 14, 2021

Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI's present applications healthcare, its benefits, limitations future scope.A review of the English literature was conducted with search terms 'AI' or 'ML' 'deep learning' 'healthcare' 'medicine' using PubMED Google Scholar from 2000-2021.AI could transform physician workflow patient care through applications, assisting physicians replacing administrative tasks to augmenting medical knowledge.From challenges training ML systems unclear accountability, implementation is difficult incremental at best. Physicians also lack understanding what AI represent.AI can ultimately prove beneficial but requires meticulous governance similar conduct.Regulatory guidelines needed on how safely implement assess technology, alongside further research into specific capabilities use.

Language: Английский

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Artificial Intelligence for COVID-19: A Systematic Review

Frontiers in Medicine, Journal Year: 2021, Volume and Issue: 8

Published: Sept. 30, 2021

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation epidemic trends, prognosis, exploration effective safe drugs vaccines; discusses potential limitations. Methods: We report this systematic review following Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines. searched PubMed, Embase Cochrane Library from inception 19 September 2020 published studies AI applications COVID-19. used PROBAST (prediction model risk bias assessment tool) assess quality literature related diagnosis prognosis registered protocol (PROSPERO CRD42020211555). Results: included 78 studies: 46 articles discussed AI-assisted COVID-19 with total accuracy 70.00 99.92%, sensitivity 73.00 100.00%, specificity 25 area under curve 0.732 1.000. Fourteen evaluated based on clinical characteristics at hospital admission, such as clinical, laboratory radiological characteristics, reaching 74.4 95.20%, 72.8 98.00%, 55 96.87% AUC 0.66 0.997 predicting critical Nine models predict peak, infection rate, number infected cases, transmission laws, development trend. Eight explore drugs, primarily through drug repurposing development. Finally, 1 article predicted vaccine targets that have develop vaccines. Conclusions: In review, we shown achieved high performance evaluation, prediction discovery enhance significantly existing medical healthcare system efficiency during pandemic.

Language: Английский

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Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives

International Journal of Biological Sciences, Journal Year: 2021, Volume and Issue: 17(6), P. 1581 - 1587

Published: Jan. 1, 2021

Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis treatment, socioeconomics.The association AI can accelerate rapidly diagnose positive patients.To learn dynamics a pandemic with relevance AI, we search literature using different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) preprint servers (bioRxiv, medRxiv, arXiv).In present review, address clinical applications machine learning deep learning, characteristics, electronic records, images (CT, X-ray, ultrasound images, etc.) diagnosis.The current challenges future perspectives provided this review be direct an ideal deployment technology pandemic.

Language: Английский

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Using artificial intelligence to improve public health: a narrative review

Frontiers in Public Health, Journal Year: 2023, Volume and Issue: 11

Published: Oct. 26, 2023

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, public health, the widespread employment only began recently, with advent COVID-19. This review examines advances health potential challenges that lie ahead. Some ways aided delivery are via spatial modeling, risk prediction, misinformation control, surveillance, disease forecasting, pandemic/epidemic diagnosis. implementation not universal due to factors including limited infrastructure, lack technical understanding, data paucity, ethical/privacy issues.

Language: Английский

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A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare

Cureus, Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 4, 2023

Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early diagnosis, reducing the medical administrative burden, aiding drug development, personalising and oncological management, monitoring parameters on an individual basis, allowing clinicians spend more time with their patients. In post-pandemic world where there a drive for efficient delivery of manage long waiting times patients access care, AI has important role in supporting systems streamline care pathways provide timely high-quality Despite technologies being used some decades, all theoretical potential AI, uptake been uneven slower than anticipated remain number barriers, both overt covert, which have limited its incorporation. This literature review highlighted barriers six key areas: ethical, technological, liability regulatory, workforce, social, patient safety barriers. Defining understanding preventing acceptance implementation setting will enable clinical staff leaders overcome identified hurdles incorporate benefit staff.

Language: Английский

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Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends

Healthcare, Journal Year: 2023, Volume and Issue: 11(2), P. 207 - 207

Published: Jan. 10, 2023

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights speeding breakthroughs lies using large datasets integrated on several levels. However, even if there is more data at our disposal ever, only a meager portion being filtered, interpreted, integrated, analyzed. subject this technology study how computers may learn from imitate human mental processes. Both an increase learning capacity provision decision support system size that redefining future healthcare enabled by AI ML. This article offers survey uses ML industry, particular emphasis clinical, developmental, administrative, global health implementations infrastructure as whole, along impact expectations each component healthcare. Additionally, possible trends scopes utilization medical have also been discussed.

Language: Английский

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Artificial Intelligence in Science Education (2013–2023): Research Trends in Ten Years

Journal of Science Education and Technology, Journal Year: 2023, Volume and Issue: 33(1), P. 94 - 117

Published: Oct. 6, 2023

Language: Английский

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Ethical implications of artificial intelligence integration in nursing practice in arab countries: literature review

BMC Nursing, Journal Year: 2025, Volume and Issue: 24(1)

Published: Feb. 11, 2025

Applying artificial intelligence (AI) to nursing practice has dramatically enhanced healthcare delivery in Arab countries. However, AI application also raises complex moral issues, including patient privacy, data security, responsibility, transparency, and equity decision-making. A systematic analysis of the ethical issues surrounding nations is carried out this review, highlighting most important recommending responsible integration. comprehensive literature search was conducted across major databases. Following initial identification 150 articles, 120 were selected for full-text review based on title abstract screening. Subsequently, 50 pertinent studies incorporated into review. Numerous significant concerns regarding decision-making processes identified. The assessment highlighted possible effects nurse-patient interaction critical role played by ethics committees regulatory frameworks resolving these issues. Ethical must be established guarantee integration practice, safeguard patients' welfare, strengthen trust between providers patients. No clinical Trial.

Language: Английский

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COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 139, P. 104984 - 104984

Published: Oct. 30, 2021

Language: Английский

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Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery

Journal of Infection and Public Health, Journal Year: 2022, Volume and Issue: 15(2), P. 289 - 296

Published: Jan. 19, 2022

To clarify the work done by using AI for identifying genomic sequences, development of drugs and vaccines COVID-19 to recognize advantages challenges such technology.A non-systematic review was done. All articles published on Pub-Med, Medline, Google, Google Scholar or digital health regarding sequencing, drug development, were scrutinized summarized.The sequence SARS- CoV-2 identified with help AI. It can also in prompt identification variants concern (VOC) as delta strains Omicron. Furthermore, there are many applied These included Atazanavir, Remdesivir, Efavirenz, Ritonavir, Dolutegravir, PARP1 inhibitors (Olaparib CVL218 which is Mefuparib hydrochloride), Abacavir, Roflumilast, Almitrine, Mesylate. Many developed utilizing new technology bioinformatics, databases, immune-informatics, machine learning, reverse vaccinology whole SARS-CoV-2 proteomes structural proteins. Examples these messenger RNA viral vector vaccines. provides cost-saving agility. However, its usage difficulty collecting data, internal external validation, ethical consideration, therapeutic effect, time needed clinical trials after approval. Moreover, a common problem deep learning (DL) model shortage interpretability.The growth techniques care opened broad gate discovering sequences virus VOC. helps (including repurposing) obtain potential preventive agents controlling pandemic.

Language: Английский

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