The impact of artificial intelligence on information audit usage: Evidence from developing countries

Journal of Open Innovation Technology Market and Complexity, Год журнала: 2024, Номер 10(2), С. 100298 - 100298

Опубликована: Май 12, 2024

The present study aims to explore the factors influencing utilization of Information audit in context Egypt and Jordan, with specific attention given role artificial intelligence (AI). A sample 443 respondents participated study, data collection was carried out through a non-probability convenience snowball sampling approach. findings reveal that internal determinants are positively associated intention adopt technologies, exhibiting significant impact beta coefficient +0.45 (P-value < 0.01), perceived benefits their implementation. Moreover, underscores critical influence intelligence, dimensions such as cloud computing, mining, e-commerce enhancing advantages (β = 0.35, P-value 0.01) fostering intent use technologies 0.22, 0.01). Additionally, there is robust positive correlation between actual usage, where presence AI amplifies this association, indicated by value 0.48 This significantly enriches existing body knowledge delineating particularly within Middle Eastern context, highlights pivotal shaping these dynamics. provides empirical evidence on audit. Its originality lies its focus underexplored East region literature investigation implications for practitioners, auditors, policymakers operating region. suggest firms should allocate sufficient support resources encourage adoption technologies. auditors need have necessary skills effectively Policymakers can study's develop policies regulations promote

Язык: Английский

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Empowering Glioma Prognosis With Transparent Machine Learning and Interpretative Insights Using Explainable AI

IEEE Access, Год журнала: 2024, Номер 12, С. 31697 - 31718

Опубликована: Янв. 1, 2024

The primary objective of this research is to create a reliable technique determine whether patient has glioma, specific kind brain tumour, by examining various diagnostic markers, using variety machine learning as well deep approaches, and involving XAI (explainable artificial intelligence) methods. Through the integration data, including medical records, genetic profiles, algorithms have ability predict how each individual will react different interventions. To guarantee regulatory compliance inspire confidence in AI-driven healthcare solutions, incorporated. Machine methods employed study includes Random Forest, decision trees, logistic regression, KNN, Adaboost, SVM, Catboost, LGBM classifier, Xgboost whereas include ANN CNN. Four alternative strategies, SHAP, Eli5, LIME, QLattice algorithm, are comprehend predictions model. Xgboost, ML model achieved accuracy, precision, recall, f1 score, AUC 88%, 82%, 94%, 92%, respectively. best characteristics according techniques IDH1, Age at diagnosis, PIK3CA, ATRX, PTEN, CIC, EGFR TP53. By applying data analytic techniques, provide professionals with practical tool that enhances their capacity for decision-making, resource management, ultimately raises bar care. Medical experts can customise treatments improve outcomes taking into account patient's particular characteristics. provides justifications foster faith amongst patients who must rely on AI-assisted diagnosis treatment recommendations.

Язык: Английский

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Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers

Annals of Medicine, Год журнала: 2023, Номер 55(1)

Опубликована: Июль 12, 2023

Objective The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those other respiratory illnesses. reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various diseases, including COVID-19. However, this diagnostic method prone erroneous and false negative results (10% -15%). Therefore, finding an alternative technique validate RT-PCR paramount. Artificial intelligence (AI) machine learning (ML) applications extensively used in medical research. Hence, study focused on developing a decision support system using AI diagnose mild-moderate similar diseases demographic clinical markers. Severe cases were not considered since fatality rates have dropped considerably after introducing vaccines.Methods A custom stacked ensemble model consisting heterogeneous algorithms has been utilized prediction. Four deep also tested compared, such as one-dimensional convolutional neural networks, long short-term memory networks Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor Local Interpretable Model-agnostic Explanations, interpret predictions made by classifiers.Results After Pearson's correlation particle swarm optimization feature selection, final stack obtained maximum accuracy 89%. most important markers which useful Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c TWBC.Conclusion promising suggest

Язык: Английский

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Artificial intelligence in routine blood tests

Frontiers in Medical Engineering, Год журнала: 2024, Номер 2

Опубликована: Март 25, 2024

Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, analysis offers superior accessibility to comprehensive assessment of physiological parameters. These parameters can be integrated automated at scale, allowing for in-depth inference cost-effectiveness compared other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value leveraged by artificial intelligence (AI), using ICD-10 classification reference. A significant gap exists between standard disease-associated features those selected machine learning models. This suggests an amount non-perceived information systems that AI could leverage improved performance metrics. Nonetheless, AI-derived decisions must still harmonized regarding external validation studies, regulatory approvals, deployment strategies. Still, discuss, path is drawn future application scalable (AI) enhance, extract, classify patterns potentially correlated pathological states restricted limitations terms bias representativeness.

Язык: Английский

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Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Information, Год журнала: 2023, Номер 14(8), С. 435 - 435

Опубликована: Авг. 1, 2023

Stroke occurs when a brain’s blood artery ruptures or the supply is interrupted. Due to rupture obstruction, tissues cannot receive enough and oxygen. common cause of mortality among older people. Hence, loss life severe brain damage can be avoided if stroke recognized diagnosed early. Healthcare professionals discover solutions more quickly accurately using artificial intelligence (AI) machine learning (ML). As result, we have shown how predict in patients heterogeneous classifiers explainable (XAI). The multistack ML models surpassed all other classifiers, with accuracy, recall, precision 96%, respectively. Explainable collection frameworks tools that aid understanding interpreting predictions provided by algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) Anchor, been used decipher model predictions. This research aims enable healthcare provide personalized efficient care, while also providing screening architecture automated revolutionize prevention treatment.

Язык: Английский

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Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach

BMC Infectious Diseases, Год журнала: 2025, Номер 25(1)

Опубликована: Март 26, 2025

Monkeypox, a viral zoonotic disease, is an emerging global health concern, with rising incidence and outbreaks extending beyond its endemic regions in Central and, West Africa the world. The disease transmits through contact infected animals humans, leading to fever, rash, lymphadenopathy symptoms. Control efforts include surveillance, tracing, vaccination campaigns; however, increasing number of cases underscores necessity for coordinated response mitigate impact. Since monkeypox has become public issue, new methods efficiently identifying are required. control infections depends on early detection prediction. This study aimed utilize Symptom-Based Detection Monkeypox using machine-learning approach. research presents machine learning approach that integrates various Explainable Artificial Intelligence (XAI) enhance based clinical symptoms, addressing limitations image-based diagnostic systems. In this study, we used publicly available dataset from GitHub containing features about disease. data have been analysed Random Forest, Bagging, Gradient Boosting, CatBoost, XGBoost, LGBMClassifier develop robust predictive model. shows models can accurately diagnose symptoms like other By XAI techniques feature importance, not only achieved high accuracy but also provided transparency decision-making. integration explainable intelligence (AI) enhances trust allows healthcare professionals understand predictions, timely interventions improved responses outbreaks. All Machine compared evaluation matrix. best performance was LGBMClassifier, 89.3%. addition, multiple Techniques tools were help examining explaining output Our combining AI greatly case boosts medical professionals. These result directly involving reader care professional decision-making process, making informed decisions, allocating resources by providing insight into process. potential particularly enhancing infectious diseases such as monkeypox.

Язык: Английский

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Explainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers

Cogent Engineering, Год журнала: 2024, Номер 11(1)

Опубликована: Март 26, 2024

Gestational diabetes is characterized by hyperglycemia diagnosed during pregnancy. High blood sugar levels are likely to affect both the mother and child. This disease frequently goes undiagnosed due its fewer prominent symptoms, resulting in severe unmanaged hyperglycemia, obesity, childbirth complications overt diabetes. Artificial Intelligence increasingly deployed medical field, revolutionizing automating data processing decision-making. Machine learning a subset of artificial intelligence that can create reliable healthcare screening predictive systems. With advent machine learning, detecting gestational getting more profound insights about possible. study explores development clinical decision support system for detection using multiple architectures combinations five balancing methods detect An ensemble stack trained on synthetic minority oversampling technique with edited nearest neighbor obtained highest performance accuracy, sensitivity precision 96%, 95% 99%, respectively. Additionally, layer explainable was added best-performing model libraries such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, Quantum lattice, Explain Like I'm 5 algorithm, Anchor Feature importance. The importance factors Visceral Adipose Deposit contribution toward prediction explored. research aims provide meaningful interpretable aid professionals early improved patient management.

Язык: Английский

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Advanced ensemble machine-learning and explainable ai with hybridized clustering for solar irradiation prediction in Bangladesh

Theoretical and Applied Climatology, Год журнала: 2024, Номер 155(7), С. 5695 - 5725

Опубликована: Апрель 17, 2024

Язык: Английский

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Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Health Science Reports, Год журнала: 2024, Номер 7(2)

Опубликована: Фев. 1, 2024

Due to the COVID-19 pandemic, a precise and reliable diagnosis of this disease is critical. The use clinical decision support systems (CDSS) can help facilitate COVID-19. This scoping review aimed investigate role CDSS in diagnosing

Язык: Английский

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Transparent and Accurate COVID-19 Diagnosis: Integrating Explainable AI with Advanced Deep Learning in CT Imaging

Computer Modeling in Engineering & Sciences, Год журнала: 2024, Номер 139(3), С. 3101 - 3123

Опубликована: Янв. 1, 2024

In the current landscape of COVID-19 pandemic, utilization deep learning in medical imaging, especially chest computed tomography (CT) scan analysis for virus detection, has become increasingly significant.Despite its potential, learning's "black box" nature been a major impediment to broader acceptance clinical environments, where transparency decision-making is imperative.To bridge this gap, our research integrates Explainable AI (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) method, with advanced models.This integration forms sophisticated and transparent framework identification, enhancing capability standard Convolutional Neural Network (CNN) models through transfer data augmentation.Our approach leverages refined DenseNet201 architecture superior feature extraction employs augmentation strategies foster robust model generalization.The pivotal element methodology use LIME, which demystifies process, providing clinicians clear, interpretable insights into AI's reasoning.This unique combination an optimized Deep (DNN) LIME not only elevates precision detecting cases but also equips healthcare professionals deeper understanding diagnostic process.Our validated on SARS-COV-2 CT-Scan dataset, demonstrates exceptional accuracy, performance metrics that reinforce potential seamless modern systems.This innovative marks significant advancement creating explainable trustworthy tools decisionmaking ongoing battle against COVID-19.

Язык: Английский

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