Impacts of the advancement in artificial intelligence on laboratory medicine in low‐ and middle‐income countries: Challenges and recommendations—A literature review

Health Science Reports, Journal Year: 2024, Volume and Issue: 7(1)

Published: Jan. 1, 2024

Abstract Background and Aims Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements healthcare delivery. This study explores the potential impact of AI on diagnostics patient management within context with particular focus low‐ middle‐income countries (LMICs). Methods In writing this article, we conducted thorough search databases such PubMed, ResearchGate, Web Science, Scopus, Google Scholar 20 years. The examines AI's capabilities, including learning, reasoning, decision‐making, mirroring human cognitive processes. It highlights adeptness at processing vast data sets, identifying patterns, expediting extraction actionable insights, particularly medical imaging interpretation test analysis. research emphasizes benefits early disease detection, therapeutic interventions, personalized treatment strategies. Results realm demonstrates remarkable precision interpreting images radiography, computed tomography, magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting trajectories informing strategies using comprehensive sets comprising clinical outcomes, records, results. underscores significance addressing challenges, especially resource‐constrained LMICs. Conclusion While acknowledging profound medicine LMICs, recognizes challenges inadequate availability, digital infrastructure deficiencies, ethical considerations. Successful implementation necessitates substantial investments infrastructure, establishment data‐sharing networks, formulation regulatory frameworks. concludes that collaborative efforts among stakeholders, international organizations, governments, nongovernmental entities, are crucial for overcoming obstacles responsibly integrating into A comprehensive, coordinated approach is essential realizing advancing health care

Language: Английский

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Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis

NAM Perspectives, Journal Year: 2022, Volume and Issue: 22(9)

Published: Sept. 26, 2022

Introduction Clinical diagnosis is essentially a data curation and analysis activity through which clinicians seek to gather synthesize enough pieces of information about patient determine their […]

Language: Английский

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COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network

Journal of Applied Biomedicine, Journal Year: 2022, Volume and Issue: 43(1), P. 1 - 16

Published: Nov. 24, 2022

Language: Английский

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Ethical Implications of Artificial Intelligence in Population Health and the Public’s Role in Its Governance: Perspectives From a Citizen and Expert Panel

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e44357 - e44357

Published: March 10, 2023

Artificial intelligence (AI) systems are widely used in the health care sector. Mainly applied for individualized care, AI is increasingly aimed at population health. This raises important ethical considerations but also calls responsible governance, considering that this will affect population. However, literature points to a lack of citizen participation governance Therefore, it necessary investigate and societal implications health.This study explore perspectives attitudes citizens experts regarding ethics health, engagement potential digital app foster engagement.We recruited panel 21 experts. Using web-based survey, we explored their on issues relative role other actors ways which can be supported participate through app. The responses participants were analyzed quantitatively qualitatively.According participants, perceived already present its benefits regarded positively, there consensus has substantial implications. showed high level agreement toward involving into governance. They highlighted aspects considered creation involvement. recognized importance creating an both accessible transparent.These results offer avenues development raise awareness, support citizens' decision-making ethical, legal, social

Language: Английский

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Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants

Vaccines, Journal Year: 2023, Volume and Issue: 11(2), P. 374 - 374

Published: Feb. 6, 2023

Accurate identification at an early stage of infection is critical for effective care any infectious disease. The “coronavirus disease 2019 (COVID-19)” outbreak, caused by the virus “Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)”, corresponds to current and global pandemic, characterized several developing variants, many which are classified as variants concern (VOCs) “World Health Organization (WHO, Geneva, Switzerland)”. primary diagnosis made using either molecular technique RT-PCR, detects parts viral genome’s RNA, or immunodiagnostic procedures, identify proteins antibodies generated host. As demand RT-PCR test grew fast, inexperienced producers joined market with innovative kits, increasing number laboratories diagnostic field, rendering results increasingly prone mistakes. It difficult determine how outcomes one unnoticed result could influence decisions about patient quarantine social isolation, particularly when patients themselves health providers. development point-of-care testing helps in rapid in-field disease, such can also be used a bedside monitor mapping progression patients. In this review, we have provided readers available techniques their pitfalls detecting emerging VOCs SARS-CoV-2, lastly, discussed AI-ML- nanotechnology-based smart SARS-CoV-2 detection.

Language: Английский

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Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic

International Journal of Production Research, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 34

Published: Oct. 3, 2023

The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on topic has been limited fragmented, leading to an incomplete understanding of ‘what’, ‘where’ ‘how’ its application, as well associated benefits challenges. This study proposes a comprehensive AI framework assesses effectiveness within UAE's sector. It provides valuable insights into applications stakeholders that range from molecular population level. covers different computational techniques employed, machine learning computer vision, various types data inputs fed these techniques, including clinical, epidemiological, locational, behavioural genomic data. Additionally, highlights AI's capacity enhance healthcare's operational, quality-related social outcomes, recognises regulatory policies, technological infrastructure, stakeholder cooperation innovation readiness key facilitators adoption. Lastly, we stress importance addressing challenges such privacy, security, generalisability algorithmic bias. Our findings are relevant beyond facilitating development AI-related policy interventions support mechanisms building resilient sector can withstand future

Language: Английский

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Employing Transfer Learning for Diagnosing COVID-19 Disease

International Journal of Online and Biomedical Engineering (iJOE), Journal Year: 2022, Volume and Issue: 18(15), P. 31 - 42

Published: Dec. 6, 2022

Corona virus’s correct and accurate diagnosis is the most important reason for contributing to treatment of this disease. Radiography one simplest methods detect virus infection. In research, a method has been proposed that can diagnose disease based on radiography (X-ray chest) deep learning techniques. We conducted comparative study by using three models; first was developed traditional CNN, while two others are our models (second third models). The COVID-19 infection, normal cases, lung opacity, Viral Pneumonia according four categories in covid19 dataset. transfer technology had used increase robustness reliability model, also, data augmentation reducing overfitting accuracy model scaling rotation, zooming, translation. showed higher training 93.18% compared other dependent convolution neural networks with an 70.28% second uses 90.1%, testing 68.27% 87.55% 86.03% model.

Language: Английский

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Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence

Artificial Intelligence in Medicine, Journal Year: 2023, Volume and Issue: 137, P. 102490 - 102490

Published: Jan. 18, 2023

Language: Английский

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Paper-based devices for rapid diagnosis and wastewater surveillance

TrAC Trends in Analytical Chemistry, Journal Year: 2022, Volume and Issue: 157, P. 116760 - 116760

Published: Aug. 22, 2022

Language: Английский

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A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis

Interdisciplinary Perspectives on Infectious Diseases, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

This paper explores the transformative potential of integrating artificial intelligence (AI) in diagnosis and prognosis infectious diseases. By analyzing diverse datasets, including clinical symptoms, laboratory results, imaging data, AI algorithms can significantly enhance early detection personalized treatment strategies. reviews how AI-driven models improve diagnostic accuracy, predict patient outcomes, contribute to effective disease management. It also addresses challenges ethical considerations associated with AI, data privacy, algorithmic bias, equitable access healthcare. Highlighting case studies recent advancements, underscores AI's role revolutionizing management its implications for future healthcare delivery.

Language: Английский

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