Revolutionizing healthcare: the role of artificial intelligence in clinical practice

BMC Medical Education, Journal Year: 2023, Volume and Issue: 23(1)

Published: Sept. 22, 2023

Abstract Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in practice is crucial successful implementation equipping providers essential knowledge tools. Research Significance This review article provides a comprehensive up-to-date overview current state practice, its applications disease diagnosis, treatment recommendations, engagement. It also discusses associated challenges, covering ethical legal considerations need human expertise. By doing so, enhances understanding significance supports organizations effectively adopting technologies. Materials Methods The investigation analyzed use system relevant indexed literature, such as PubMed/Medline, Scopus, EMBASE, no time constraints limited articles published English. focused question explores impact applying settings outcomes this application. Results Integrating holds excellent improving selection, laboratory testing. tools leverage large datasets identify patterns surpass performance several aspects. offers increased accuracy, reduced costs, savings while minimizing errors. personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual assistants, support mental care, education, influence patient-physician trust. Conclusion be used diagnose diseases, develop plans, assist clinicians decision-making. Rather than simply automating tasks, about developing technologies that across settings. However, challenges related data privacy, bias, expertise must addressed responsible effective healthcare.

Language: Английский

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Bridging the Digital Divide: A Practical Roadmap for Deploying Medical Artificial Intelligence Technologies in Low-Resource Settings

Population Health Management, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and delivery. low-resource settings, AI offers significant potential to address disparities exacerbated by shortages medical professionals other resources. However, implementing effectively responsibly in these settings requires careful consideration context-specific needs barriers equitable care. This article explores practical deployment environments through a review existing literature interviews with experts, ranging from providers administrators tool developers government consultants. The authors highlight 4 critical areas for effective deployment: infrastructure requirements, data management, education training, responsible practices. By addressing aspects, proposed framework aims guide sustainable integration, minimizing risk, enhancing access underserved regions.

Language: Английский

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Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 80, P. 104223 - 104223

Published: Oct. 20, 2022

Language: Английский

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Triage and priority-based healthcare diagnosis using artificial intelligence for autism spectrum disorder and gene contribution: A systematic review

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 146, P. 105553 - 105553

Published: May 9, 2022

Language: Английский

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Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder

Molecular Psychiatry, Journal Year: 2023, Volume and Issue: 28(12), P. 4995 - 5008

Published: April 17, 2023

Abstract Autism-spectrum disorders (ASDs) are developmental disabilities that manifest in early childhood and characterized by qualitative abnormalities social behaviors, communication skills, restrictive or repetitive behaviors. To explore the neurobiological mechanisms ASD, extensive research has been done to identify potential diagnostic biomarkers through a neuroimaging genetics approach. Neuroimaging helps ASD-risk genes contribute structural functional variations brain circuitry validate biological changes elucidating pathways confer genetic risk. Integrating artificial intelligence models with data lays groundwork for accurate diagnosis facilitates identification of ASD. This review discusses significance approaches gaining better understanding perturbed neurochemical system molecular ASD how these can detect structural, functional, metabolic lead discovery novel

Language: Английский

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Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases

Journal of Neurodevelopmental Disorders, Journal Year: 2022, Volume and Issue: 14(1)

Published: May 2, 2022

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X Rett autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual adaptive functioning, both genetic environmental factors underpin IDD biology. Molecular stratification of remain challenging mainly due to overlapping comorbidity. Advances high throughput sequencing, imaging, tools record behavioral data scale have greatly enhanced our understanding the molecular, cellular, structural, basis some IDDs. Fueled "big data" revolution, artificial intelligence (AI) machine learning (ML) technologies brought a whole new paradigm shift computational Evidently, ML-driven approach clinical diagnoses has potential augment classical methods that use symptoms external observations, hoping push personalized treatment plan forward. Therefore, integrative analyses applications ML technology direct bearing on discoveries The application can potentially improve screening diagnosis, advance complexity comorbidity, accelerate identification biomarkers for research drug development. For more than five decades, IDDRC network supported nexus investigators centers across USA, all striving understand interplay between various underlying In this review, we introduced fast-increasing multi-modal types, highlighted example studies employed illuminate biological mechanisms IDDs, well recent advances their other neurological diseases. We discussed clinical, collection modes, including genetic, phenotypical, along with multiple repositories store share data. Furthermore, outlined fundamental concepts algorithms presented opinion specific gaps will need be filled accomplish, example, reliable implementation ML-based diagnosis clinics. anticipate review guide researchers formulate AI approaches investigate related conditions.

Language: Английский

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Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks

IEEE Journal of Selected Topics in Signal Processing, Journal Year: 2022, Volume and Issue: 16(2), P. 276 - 288

Published: Feb. 1, 2022

The Coronavirus disease 2019 (COVID-19) is a respiratory illness that can spread from person to person. Since the COVID-19 pandemic spreading rapidly over world and its outbreak has affected different people in ways, it significant study or predict evolution of epidemic trend. However, most studies focused solely on either classical epidemiological models machine learning for forecasting, which suffer limitation generalization ability scalability lack surveillance data. In this work, we propose T-SIRGAN integrates strengths theories deep be able represent complex processes model non-linear relationship more accurate prediction growth COVID-19. first adopts Susceptible-Infectious-Recovered (SIR) generate epidemiological-based simulation data, are then fed into generative adversarial network (GAN) as examples data augmentation. Then, Transformers used future trends based generated synthetic Extensive experiments real-world datasets demonstrate superiority our method. We also discuss effectiveness vaccine difference between predicted reported number cases.

Language: Английский

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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network

Baghdad Science Journal, Journal Year: 2023, Volume and Issue: 20(3(Suppl.)), P. 1182 - 1182

Published: June 20, 2023

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning field of artificial intelligence focuses on creating algorithms can learn patterns make ASD classification based input data. The results using machine to categorize have been inconsistent. More research needed improve the accuracy ASD. To address this, deep such 1D CNN has proposed an alternative for detection. techniques are evaluated publicly available three different datasets (children, Adults, adolescents). Results strongly suggest CNNs shown improved in compared traditional algorithms, all these with higher 99.45%, 98.66%, 90% Autistic Disorder Screening Data Children, Adolescents respectively they better suited analysis time series data commonly used diagnosis this disorder

Language: Английский

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Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

Frontiers in Cell and Developmental Biology, Journal Year: 2024, Volume and Issue: 12

Published: July 2, 2024

The connection and causality between cancer neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, mutations lead to pathologies with vastly different clinical presentations? And why do individuals disorders, such as autism schizophrenia, face higher chances of emerging throughout their lifetime? Our broad review emphasizes multi-scale aspect this type reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at new understanding that be gained. Within framework, our calls attention computational strategies which powerful in discovering connections, causalities, predicting outcomes, are vital for drug discovery. Thus, centering features, draw rapidly increasing data molecular level, including mutations, isoforms, three-dimensional structures, expression levels respective disease-associated genes. Their integrated analysis, together chromatin states, delineate how, despite being connected, differ, how symptoms. Here, seek uncover cancer, pediatric tumors, tantalizing questions raises.

Language: Английский

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RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease

Future Generation Computer Systems, Journal Year: 2021, Volume and Issue: 128, P. 205 - 218

Published: Oct. 19, 2021

Language: Английский

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