Smart waste management: A paradigm shift enabled by artificial intelligence

Waste Management Bulletin, Год журнала: 2024, Номер 2(2), С. 244 - 263

Опубликована: Май 9, 2024

Waste management poses a pressing global challenge, necessitating innovative solutions for resource optimization and sustainability. Traditional practices often prove insufficient in addressing the escalating volume of waste its environmental impact. However, advent Artificial Intelligence (AI) technologies offers promising avenues tackling complexities systems. This review provides comprehensive examination AI's role management, encompassing collection, sorting, recycling, monitoring. It delineates potential benefits challenges associated with each application while emphasizing imperative improved data quality, privacy measures, cost-effectiveness, ethical considerations. Furthermore, future prospects AI integration Internet Things (IoT), advancements machine learning, importance collaborative frameworks policy initiatives were discussed. In conclusion, holds significant promise enhancing practices, such as concerns, cost implications is paramount. Through concerted efforts ongoing research endeavors, transformative can be fully harnessed to drive sustainable efficient practices.

Язык: Английский

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Leveraging artificial intelligence in vaccine development: A narrative review

Journal of Microbiological Methods, Год журнала: 2024, Номер 224, С. 106998 - 106998

Опубликована: Июль 15, 2024

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity mortality. However, traditional vaccine methods are often time-consuming, costly, inefficient. The advent artificial intelligence (AI) has ushered new era design, offering unprecedented opportunities to expedite the process. This narrative review explores role AI development, focusing on antigen selection, epitope prediction, adjuvant identification, optimization strategies. algorithms, including machine learning deep learning, leverage genomic data, protein structures, immune system interactions predict antigenic epitopes, assess immunogenicity, prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate rational design immunogens identification novel candidates with optimal safety efficacy profiles. Challenges such data heterogeneity, model interpretability, regulatory considerations must be addressed realize full potential development. Integrating emerging technologies, single-cell omics synthetic biology, promises enhance precision scalability. underscores transformative impact highlights need interdisciplinary collaborations harmonization accelerate delivery safe effective vaccines against diseases.

Язык: Английский

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Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency

Journal of Medical Systems, Год журнала: 2025, Номер 49(1)

Опубликована: Янв. 16, 2025

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train professionals, and advance medical research. This paper examines various clinical non-clinical applications of Gen AI. In settings, AI supports the creation customized plans, generation synthetic data, analysis images, nursing workflow management, risk prediction, pandemic preparedness, population health management. By automating administrative tasks such as documentations, reduce clinician burnout, freeing more time for direct care. Furthermore, application may surgical outcomes by providing real-time feedback automation certain operating rooms. The data opens new avenues model training diseases simulation, enhancing research capabilities improving predictive accuracy. contexts, improves education, public relations, revenue cycle marketing etc. Its capacity continuous learning adaptation enables it drive ongoing improvements operational efficiencies, making delivery proactive, predictive, precise.

Язык: Английский

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Advancements and applications of Artificial Intelligence in cardiology: Current trends and future prospects

Journal of Medicine Surgery and Public Health, Год журнала: 2024, Номер 3, С. 100109 - 100109

Опубликована: Апрель 23, 2024

Using Artificial intelligence technologies in cardiology has witnessed rapid advancements across various domains, fostering innovation and reshaping clinical practices. The study aims to provide a comprehensive overview of these AI-driven their implications for enhancing cardiovascular healthcare. A systematic approach was adopted conduct an extensive review scholarly articles peer-reviewed literature focusing on the application AI cardiology. Databases including PubMed/MEDLINE, ScienceDirect, IEEE Xplore, Web Science were systematically searched. Articles screened following defined selection criteria. These articles' synthesis highlighted AI's diverse applications cardiology, but not limited diagnostic innovations, precision medicine, remote monitoring technologies, drug discovery, decision support systems. shows significant role medicine by revolutionising diagnostics, treatment strategies, patient care. showcased this reflect transformative potential technologies. However, challenges such as algorithm accuracy, interoperability, integration into workflows persist. continued strategic promise deliver more personalised, efficient, effective care, ultimately improving outcomes shaping future practice.

Язык: Английский

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A review of medical tourism entrepreneurship and marketing at regional and global levels and a quick glance into the applications of artificial intelligence in medical tourism

AI & Society, Год журнала: 2025, Номер unknown

Опубликована: Янв. 7, 2025

Язык: Английский

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Optimizing Stroke Prediction using Gated Recurrent Unit and Feature Selection in Sub-Saharan Africa

Clinical Neurology and Neurosurgery, Год журнала: 2025, Номер unknown, С. 108761 - 108761

Опубликована: Янв. 1, 2025

Stroke remains a leading cause of death and disability worldwide, with African populations bearing disproportionately high burden due to limited healthcare infrastructure. Early prediction intervention are critical reducing stroke outcomes. This study developed evaluated system using Gated Recurrent Units (GRU), variant Neural Networks (RNN), leveraging the Afrocentric Investigative Research Education Network (SIREN) dataset. The utilized secondary data from SIREN dataset, comprising 4236 records 29 phenotypes. Feature selection reduced these 15 optimal phenotypes based on their significance occurrence. GRU model, designed 128 input neurons four hidden layers (64, 32, 16, 8 neurons), was trained 150 epochs, batch size 8, metrics such as accuracy, AUC, time. Comparisons were made traditional machine learning algorithms (Logistic Regression, SVM, KNN) Long Short-Term Memory (LSTM) networks. GRU-based achieved performance accuracy 77.48 %, an AUC 0.84, time 0.43 seconds, outperforming all other models. Logistic Regression 73.58 while LSTM reached 74.88 % but longer 2.23 seconds. significantly improved model's compared demonstrated superior in prediction, offering efficient scalable tool for healthcare. Future research should focus integrating unstructured data, validating model diverse populations, exploring hybrid architectures enhance predictive accuracy.

Язык: Английский

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Comparing ChatGPT 4.0’s Performance in Interpreting Thyroid Nodule Ultrasound Reports Using ACR-TI-RADS 2017: Analysis Across Different Levels of Ultrasound User Experience

Diagnostics, Год журнала: 2025, Номер 15(5), С. 635 - 635

Опубликована: Март 6, 2025

Background/Objectives: This study evaluates ChatGPT 4.0's ability to interpret thyroid ultrasound (US) reports using ACR-TI-RADS 2017 criteria, comparing its performance with different levels of US users. Methods: A team medical experts, an inexperienced user, and 4.0 analyzed 100 fictitious reports. ChatGPT's was assessed for accuracy, consistency, diagnostic recommendations, including fine-needle aspirations (FNA) follow-ups. Results: demonstrated substantial agreement experts in assessing echogenic foci, but inconsistencies other such as composition margins, were evident both analyses. Interrater reliability between ranged from moderate almost perfect, reflecting AI's potential also limitations achieving expert-level interpretations. The user outperformed a nearly perfect the highlighting critical role traditional training standardized risk stratification tools TI-RADS. Conclusions: showed high specificity recommending FNAs lower sensitivity follow-ups compared student. These findings emphasize supportive tool rather than replacement human expertise. Enhancing AI algorithms could improve clinical utility, enabling better support clinicians managing nodules improving patient care. highlights promise current diagnostics, advocating refinement integration into workflows. However, it emphasizes that must not be compromised, is essential identifying correcting AI-driven errors.

Язык: Английский

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Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Neuroscience, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐Analysis

Health Science Reports, Год журнала: 2025, Номер 8(4)

Опубликована: Март 31, 2025

ABSTRACT Background and Aims Odontogenic keratocyst (OKC) is a radiolucent jaw lesion often mistaken for similar conditions like ameloblastomas on panoramic radiographs. Accurate diagnosis vital effective management, but manual image interpretation can be inconsistent. While deep learning algorithms in AI have shown promise improving diagnostic accuracy OKCs, their performance across studies still unclear. This systematic review meta‐analysis aimed to evaluate the of models detecting OKC from Methods A search was performed 5 databases. Studies were included if they examined PICO question whether (I) could improve (O) radiographs (P) compared reference standards (C). Key metrics including sensitivity, specificity, accuracy, area under curve (AUC) extracted pooled using random‐effects models. Meta‐regression subgroup analyses conducted identify sources heterogeneity. Publication bias evaluated through funnel plots Egger's test. Results Eight meta‐analysis. The sensitivity all 83.66% (95% CI:73.75%–93.57%) specificity 82.89% CI:70.31%–95.47%). YOLO‐based demonstrated superior with 96.4% 96.0%, other architectures. analysis indicated that model architecture significant predictor performance, accounting portion observed However, also revealed publication high variability (Egger's test, p = 0.042). Conclusion models, particularly architectures, OKCs shows strong capabilities simple cases, it should complement, not replace, human expertise, especially complex situations.

Язык: Английский

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Ethical Considerations in the Use of the da Vinci Surgical System in Modern Surgery

Indian Journal of Surgical Oncology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 11, 2025

Язык: Английский

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