Artificial intelligence tool development: what clinicians need to know?

BMC Medicine, Год журнала: 2025, Номер 23(1)

Опубликована: Апрель 24, 2025

Digital medicine and smart healthcare will not be realised without the cognizant participation of clinicians. Artificial intelligence (AI) today primarily involves computers or machines designed to simulate aspects human using mathematically neural networks, although early AI systems relied on a variety non-neural network techniques. With increased complexity layers, deep machine learning (ML) can self-learn augment many tasks that require decision-making basis multiple sources data. Clinicians are important stakeholders in use ML tools. The review questions as follows: What is typical process tool development full cycle? concepts technical each step? This synthesises targeted literature reports summarises online structured materials present succinct explanation whole tools series cyclical processes: (1) identifying clinical problems suitable for solutions, (2) forming project teams collaborating with experts, (3) organising curating relevant data, (4) establishing robust physical virtual infrastructure, computer systems' architecture support subsequent stages, (5) exploring networks open access platforms before making new decision, (6) validating AI/ML models, (7) registration, (8) deployment continuous performance monitoring (9) improving ecosystem ensures its adaptability evolving needs. A sound understanding this would help clinicians appreciate engage codesigning, evaluating facilitate broader closer regulation settings.

Язык: Английский

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Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review

Sensors, Год журнала: 2024, Номер 24(13), С. 4350 - 4350

Опубликована: Июль 4, 2024

Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing is detect contaminants the water. Various methods proposed for water quality testing, such as spectroscopy, chromatography, electrochemical techniques. However, traditional testing require utilization laboratory equipment, which large not suitable real-time field. Microfluidic devices can overcome limitations instruments become an efficient convenient tool analysis. At same time, artificial intelligence ideal means recognizing, classifying, predicting data obtained from microfluidic systems. based on machine learning are being developed with great significance next generation monitoring This review begins brief introduction algorithms involved materials used fabrication detection techniques platforms. Then, latest research development combining two pollutant bodies, including heavy metals, pesticides, micro- nanoplastics, microalgae, mainly introduced. Finally, challenges encountered future directions industrial chips discussed.

Язык: Английский

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Global contribution of statistical control charts to epidemiology monitoring: A 23-year analysis with optimized EWMA real-life application on COVID-19

Medicine, Год журнала: 2024, Номер 103(27), С. e38766 - e38766

Опубликована: Июль 5, 2024

Control charts help epidemiologists and healthcare professionals monitor disease incidence prevalence in real time, preventing outbreaks health emergencies. However, there remains a notable gap the comprehensive exploration application of these techniques, particularly context monitoring managing outbreaks. This study analyses categorizes worldwide control chart applications from 2000 to 2023 outbreak over 20 countries, focusing on corona-virus (COVID-19), chooses optimal for US COVID-19 death waves February 2020 December 2023. The systematic literature review analyzes available 35 articles, categorizing data by year, variable, country, type, design. A selected is applied patterns USA. adoption epidemiology increased during pandemic, with annual showing rise 2021 (18%, 36%, 41%). Important variables 2019 include influenza counts, Salmonella cases, infection rates, while studies focus more symptoms, deaths. Among 22 USA (29%) top applier charts. deaths reveals 8 varying severity > . associated JN.1 variant, highlights ongoing challenges. emphasizes significance early diagnosis intervention. workers manage epidemics using data-driven methods, improving public health. mortality analysis their importance, encouraging use.

Язык: Английский

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Voice feature-based diagnosis of Parkinson’s disease using nature inspired squirrel search algorithm with ensemble learning classifiers

Iran Journal of Computer Science, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Язык: Английский

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A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis

Computer Methods and Programs in Biomedicine Update, Год журнала: 2025, Номер unknown, С. 100184 - 100184

Опубликована: Фев. 1, 2025

Язык: Английский

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Predicting the Complexity of Minimally Invasive Liver Resection for Hepatocellular Carcinoma Using Machine Learning

HPB, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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Intelligent health model for medical imaging to guide laymen using neural cellular automata

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 20, 2025

A layman in health systems is a person who doesn't have any knowledge about data i.e., X-ray, MRI, CT scan, and examination reports, etc. The motivation behind the proposed invention to help laymen make medical images understandable. model trained using neural network approach that analyses user data; predicts type level of disease advises precaution user. Cellular Automata (CA) technology has been integrated with networks segment image. CA analyzes pixel by generates robust threshold value which helps efficiently image identify accurate abnormal spots from method experimented 10000+ are taken various open datasets. Various text analysis measures BLEU, ROUGE, WER used research validate produced report. BLEU ROUGE calculate similarity decide how generated report closer original scores approximately 0.62 0.90, claims very close score 0.14, contains most relevant words. overall summary it provides fruitful precautions laymen.

Язык: Английский

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Enhancing reliability in coronary artery disease prediction using ensemble learning for key attribute identification

Life Cycle Reliability and Safety Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 20, 2025

Язык: Английский

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A comprehensive survey on intrusion detection algorithms

Computers & Electrical Engineering, Год журнала: 2024, Номер 121, С. 109863 - 109863

Опубликована: Ноя. 23, 2024

Язык: Английский

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Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects

Data, Год журнала: 2024, Номер 9(10), С. 121 - 121

Опубликована: Окт. 20, 2024

Clustering is a very common means of analysis the data present in large datasets, with aims understanding and summarizing discovering similarities, among other goals. However, despite success use subsymbolic methods for clustering, description obtained clusters cannot rely on intricacies processing. For expressed Resource Description Framework (RDF), we extend implement an optimized, previously proposed, logic-based methodology that computes RDF structure—called Common Subsumer—describing commonalities all resources. We tested our implementation two open, different, datasets: one devoted to public procurement, drugs pharmacology. both were able provide reasonably concise readable descriptions up 1800 Our shows viability computation, paves way general cluster explanations be provided lay users.

Язык: Английский

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