Informatics in Medicine Unlocked, Journal Year: 2024, Volume and Issue: 50, P. 101571 - 101571
Published: Jan. 1, 2024
Language: Английский
Internet Research, Journal Year: 2022, Volume and Issue: 32(7), P. 1 - 31
Published: May 2, 2022
Purpose Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these behave, and what their output is based on, a challenge for developers let alone non-technical end users. Design/methodology/approach The authors investigate AI systems decisions ought to be explained users through systematic literature review. Findings authors’ synthesis the suggests that system communication has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability (5) fairness. identified several design recommendations, such as offering personalized on-demand explanations focusing on explainability key functionalities instead aiming explain whole system. There exists multiple trade-offs in explanations, there no single best solution fits all cases. Research limitations/implications Based synthesis, provide framework explaining study contributes work governance by suggesting guidelines make more understandable, fair, trustworthy, controllable transparent. Originality/value This review brings together explainable (XAI) Building previous academic topic, it provides synthesized insights, recommendations future research agenda.
Language: Английский
Citations
93
Informatics in Medicine Unlocked, Journal Year: 2022, Volume and Issue: 29, P. 100832 - 100832
Published: Jan. 1, 2022
Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection cough events by investigating underlying latent features disease diagnosis can play an indispensable role revitalizing healthcare practices. The recent application Artificial Intelligence (AI) advances ubiquitous computing for prediction has created auspicious trend myriad future possibilities medical domain. In particular, there is expeditiously emerging Machine learning (ML) Deep Learning (DL)-based diagnostic algorithms exploiting signatures. enormous body literature on cough-based AI demonstrate that these models a significant detecting onset specific disease. However, it pertinent to collect from all relevant studies exhaustive manner experts scientists analyze decisive AI/ML. This survey offers comprehensive overview data-driven ML/DL preliminary frameworks, along with detailed list features. We investigate mechanism causes modalities. also customized monitoring application, their AI- powered recognition algorithms. Challenges prospective research directions develop practical, robust, solutions are discussed detail.
Language: Английский
Citations
57
Science in One Health, Journal Year: 2023, Volume and Issue: 3, P. 100061 - 100061
Published: Dec. 12, 2023
Zoonotic diseases originating from animals pose a significant threat to global public health. Recent outbreaks, such as COVID-19, have caused widespread illness, death, and socioeconomic disruptions worldwide. To effectively combat these diseases, it is crucial strengthen surveillance capabilities establish rapid response systems. This review examines modern technologies solutions that the potential enhance zoonotic disease outbreak response. The discusses advanced tools including big data analytics, artificial intelligence, Internet of Things, geographic information systems, remote sensing, molecular diagnostics, point-of-care testing, telemedicine, digital contact tracing, early warning These enable real-time monitoring, prediction risks, anomaly detection, diagnosis, targeted interventions during outbreaks. When integrated thoughtfully through collaborative partnerships, they significantly improve speed effectiveness control. However, several challenges persist, particularly in resource-limited settings, infrastructure limitations, costs, integration, training requirements, ethical implementation. With strategic planning coordinated efforts, offer immense bolster response, serving critical arsenal against emerging threats
Language: Английский
Citations
37
Internet of Things, Journal Year: 2023, Volume and Issue: 23, P. 100863 - 100863
Published: July 1, 2023
Language: Английский
Citations
27
Osong Public Health and Research Perspectives, Journal Year: 2024, Volume and Issue: 15(2), P. 115 - 136
Published: March 28, 2024
Objectives: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges the public health sector, including that of United Arab Emirates (UAE). objective this study was assess efficiency and accuracy various deep-learning models in forecasting COVID-19 cases within UAE, thereby aiding nation’s authorities informed decision-making.Methods: This utilized a comprehensive dataset encompassing confirmed cases, demographic statistics, socioeconomic indicators. Several advanced deep learning models, long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, recurrent (RNN) were trained evaluated. Bayesian optimization also implemented fine-tune these models.Results: evaluation framework revealed each model exhibited different levels predictive precision. Specifically, RNN outperformed other architectures even without optimization. Comprehensive perspective analytics conducted scrutinize dataset.Conclusion: transcends academic boundaries by offering critical insights enable UAE deploy targeted data-driven interventions. model, which identified as most reliable accurate for specific context, can significantly influence decisions. Moreover, broader implications research validate capability techniques handling complex datasets, thus transformative potential healthcare sectors.
Language: Английский
Citations
12
SN Computer Science, Journal Year: 2021, Volume and Issue: 2(6)
Published: Oct. 12, 2021
Language: Английский
Citations
54
Applied Soft Computing, Journal Year: 2022, Volume and Issue: 126, P. 109315 - 109315
Published: July 15, 2022
Language: Английский
Citations
37
Information, Journal Year: 2023, Volume and Issue: 14(12), P. 665 - 665
Published: Dec. 18, 2023
The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide modeling, screening, or creation. Machine learning techniques used leverage pre-existing data for drug detection vaccine advancement, while models these purposes. Models based on intelligence evaluate recognize the best candidate targets future therapeutic development. Artificial strategies be address issues with safety efficacy candidates, as well manufacturing, storage, logistics. Because antigenic peptides effective at eliciting immune responses, algorithms assist in identifying most promising candidates. Following vaccination, first phase vaccine-induced response occurs when major histocompatibility complex (MHC) class II molecules (typically bind 12–25 amino acids) peptides. Therefore, AI-based identify candidates ensure responses. This study explores use approaches logistics, safety, effectiveness associated several Additionally, we will potential next-generation treatments examine role that play considering triggering aim this project gain insights into how could revolutionize development they leveraged challenges In work, highlight barriers solutions focus recent improvements produce drugs vaccines, prospects intelligent training treatment discovery.
Language: Английский
Citations
19
Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 253, P. 124293 - 124293
Published: June 1, 2024
Language: Английский
Citations
6
BMC Medicine, Journal Year: 2024, Volume and Issue: 22(1)
Published: Sept. 2, 2024
Abstract The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding the complex interplay between biological, social, environmental factors that shape health trajectories across lifespan. This perspective summarizes current applications, discusses future potential challenges, provides recommendations for harnessing ML AI technologies develop innovative public solutions. have been increasingly applied epidemiological studies, demonstrating their ability handle large, datasets, identify intricate patterns associations, integrate multiple multimodal data types, improve predictive accuracy, enhance causal inference methods. In epidemiology, these can help sensitive periods critical windows intervention, model interactions risk factors, predict individual population-level disease trajectories, strengthen observational studies. By leveraging five principles research proposed by Elder Shanahan—lifespan development, agency, time place, timing, linked lives—we discuss a framework applying uncover novel insights inform targeted interventions. However, successful faces challenges related quality, interpretability, bias, privacy, equity. To fully realize fostering interdisciplinary collaborations, developing standardized guidelines, advocating decision-making, prioritizing fairness, investing training capacity building are essential. responsibly power AI, we take significant steps towards creating healthier more equitable futures life course.
Language: Английский
Citations
6