The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

Bioengineering, Journal Year: 2024, Volume and Issue: 11(4), P. 337 - 337

Published: March 29, 2024

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging a key force transformation. This review motivated by urgent need to harness AI’s potential mitigate these issues aims critically assess integration in different domains. We explore how AI empowers clinical decision-making, optimizes hospital operation management, refines medical image analysis, revolutionizes patient care monitoring through AI-powered wearables. Through several case studies, we has transformed specific domains discuss remaining possible solutions. Additionally, will methodologies assessing solutions, ethical of deployment, importance data privacy bias mitigation responsible technology use. By presenting critical assessment transformative potential, this equips researchers with deeper understanding current future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, technologists navigate complexities implementation, fostering development AI-driven solutions that prioritize standards, equity, patient-centered approach.

Language: Английский

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The shaky foundations of large language models and foundation models for electronic health records

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: July 29, 2023

The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar for electronic medical records (EMRs) to improve patient care hospital operations. However, recent hype obscured critical gaps our understanding these models' capabilities. In this narrative review, we examine 84 trained on non-imaging EMR data (i.e., clinical text and/or structured data) create a taxonomy delineating their architectures, training data, potential use cases. We find that most are small, narrowly-scoped datasets (e.g., MIMIC-III) or broad, public biomedical corpora PubMed) evaluated tasks do not provide meaningful insights usefulness health systems. Considering findings, propose an improved evaluation framework measuring the benefits is more closely grounded metrics matter healthcare.

Language: Английский

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A scoping review of artificial intelligence-based methods for diabetes risk prediction

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: Oct. 25, 2023

Abstract The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis intervention. While many artificial intelligence (AI) T2DM risk prediction have emerged, a comprehensive review their advancements challenges is currently lacking. This scoping maps out existing literature on AI-based prediction, adhering PRISMA extension Scoping Reviews guidelines. A systematic search longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, Google Scholar. Forty that met our inclusion criteria were reviewed. Classical machine learning (ML) dominated these studies, with electronic records (EHR) being predominant data modality, followed by multi-omics, while medical imaging least utilized. Most employed unimodal AI models, only ten adopting multimodal approaches. Both showed promising results, latter superior. Almost all performed internal validation, but five external validation. utilized area under curve (AUC) discrimination measures. Notably, provided insights into calibration models. Half used interpretability methods identify key predictors revealed Although minority highlighted novel predictors, majority reported commonly known ones. Our provides valuable current state limitations highlights development clinical integration.

Language: Английский

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Comparing three-dimensional and two-dimensional deep-learning, radiomics, and fusion models for predicting occult lymph node metastasis in laryngeal squamous cell carcinoma based on CT imaging: a multicentre, retrospective, diagnostic study

EClinicalMedicine, Journal Year: 2024, Volume and Issue: 67, P. 102385 - 102385

Published: Jan. 1, 2024

BackgroundThe occult lymph node metastasis (LNM) of laryngeal squamous cell carcinoma (LSCC) affects the treatment and prognosis patients. This study aimed to comprehensively compare performance three-dimensional two-dimensional deep learning models, radiomics model, fusion models for predicting LNM in LSCC.MethodsIn this retrospective diagnostic study, a total 553 patients with clinical N0 stage LSCC, who underwent surgical without distant multiple primary cancers, were consecutively enrolled from four Chinese medical centres between January 01, 2016 December 30, 2020. The participant data manually retrieved records, imaging databases, pathology reports. cohort was divided into training set (n = 300), an internal test 89), two external sets 120 44, respectively). (3D DL), (2D model developed using CT images tumor. constructed based on radiological features. Two strategies utilized develop model: feature-based DLRad_FB decision-based DLRad_DB model. discriminative ability correlation 3D DL, 2D DL features analysed comprehensively. performances predictive evaluated pathological diagnosis.FindingsThe had superior lower redundancy compared achieved highest AUC (0.89–0.90) among all sets, significantly outperforming (AUC 0.73–0.78, P 0.0001–0.042, Delong test). Compared values DLRad_FB, 0.82–0.84 (P 0.025–0.46), 0.86–0.89 0.75–0.97), 0.83–0.86 0.029–0.66), 0.79–0.82 0.0072–0.10), respectively sets. Additionally, exhibited best sensitivity (82–88%) specificity (79–85%) sets.InterpretationThe DLRad_DB, which combines radiomics, data, can be predict LSCC. has potential minimize unnecessary dissection prophylactic radiotherapy cN0 disease.FundingNational Natural Science Foundation China, Shandong Province.

Language: Английский

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Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(4)

Published: March 15, 2024

Abstract Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale literature this field based on quantitative approaches. This study performed bibliometric and topic modeling examination 683 articles from 2002 to 2022, focusing topics trends, journals, countries/regions, institutions, authors, scientific collaborations. Results showed that, firstly, the number has grown 1 220 with majority being published interdisciplinary journals that link healthcare medical information technology AI. Secondly, significant rise quantity can be attributed increasing contribution scholars non-English speaking countries/regions noteworthy contributions made by authors USA India. Thirdly, researchers show high interest issues, especially, cross-modality magnetic resonance imaging (MRI) brain tumor analysis, cancer prognosis through multi-dimensional AI-assisted diagnostics personalization healthcare, each experiencing increase interest. an emerging trend towards issues such as applying generative adversarial networks contrastive learning image fusion synthesis utilizing combined spatiotemporal resolution functional MRI electroencephalogram data-centric manner. valuable enhancing researchers’ practitioners’ understanding present focal points upcoming trajectories AI-powered analysis.

Language: Английский

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Artificial Intelligence for Neuroimaging in Pediatric Cancer

Cancers, Journal Year: 2025, Volume and Issue: 17(4), P. 622 - 622

Published: Feb. 12, 2025

Background/Objectives: Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer remain limited. This review assesses the current state, potential applications, challenges of AI for cancer, emphasizing unique needs population. Methods: A comprehensive literature was conducted, focusing on AI’s impact through accelerated image acquisition, reduced radiation, improved tumor detection. Key methods include convolutional neural networks segmentation, radiomics characterization, several tools functional imaging. Challenges such as limited datasets, developmental variability, ethical concerns, need explainable models were analyzed. Results: has shown significant to improve imaging quality, reduce scan times, enhance accuracy neuroimaging, resulting segmentation outcome prediction treatment. progress hindered scarcity issues with data sharing, implications applying vulnerable populations. Conclusions: To overcome limitations, future research should focus building robust fostering multi-institutional collaborations developing interpretable that align clinical practice standards. These efforts are essential harnessing full improving outcomes children cancer.

Language: Английский

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Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine

Journal of Personalized Medicine, Journal Year: 2023, Volume and Issue: 13(8), P. 1268 - 1268

Published: Aug. 16, 2023

Precision medicine has the potential to revolutionize way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies individual characteristics of each patient. Artificial intelligence (AI) recently emerged as a promising tool for improving accuracy efficiency precision medicine. In this scoping review, we aimed identify summarize current state literature on use AI in A comprehensive search electronic databases, including Scopes, Google Scholar, PubMed, was conducted relevant studies. After applying inclusion exclusion criteria, total 28 studies were included review. We found that is being increasingly applied various areas medicine, diagnosis, prognosis diseases, risk prediction stratification, planning. As result, most these focused (50%), followed diagnosis (21%), phenotyping (14%), stratification (14%). variety machine learning models utilized studies, with logistic regression used (36%), random forest (32%), support vector (25%), deep such neural networks (18%). Other models, hierarchical clustering (11%), Cox natural language processing (4%), also utilized. The data sources health records (79%), imaging (43%), omics (4%). results review showed improve performance disease prognosis, well individuals at high developing diseases. However, further research needed fully evaluate clinical utility effectiveness AI-based approaches Overall, our provided overview knowledge field methods offered new insights researchers interested area.

Language: Английский

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Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Future Internet, Journal Year: 2023, Volume and Issue: 15(11), P. 370 - 370

Published: Nov. 18, 2023

Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. AI encompasses data analytics and artificial (AI) using machine learning, deep federated learning models deployed executed at the of network, far from centralized centers. careful analysis large datasets derived multiple sources, including electronic records, wearable demographic information, making it possible to identify intricate patterns predict person’s future health. Federated novel approach further enhances this prediction by enabling collaborative training on devices while maintaining privacy. Using computing, can be processed analyzed locally, reducing latency instant decision making. This article reviews role highlights its potential improve public Topics covered include use algorithms for detection chronic diseases such as diabetes cancer computing detect spread infectious diseases. In addition discussing challenges limitations prediction, emphasizes research directions address these concerns integration existing healthcare systems explore full technologies improving

Language: Английский

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DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: April 23, 2024

In the big data era, integrating diverse modalities poses significant challenges, particularly in complex fields like healthcare. This paper introduces a new process model for multimodal Data Fusion Mining, embeddings and Cross-Industry Standard Process Mining with existing Information Group model. Our aims to decrease computational costs, complexity, bias while improving efficiency reliability. We also propose "disentangled dense fusion," novel embedding fusion method designed optimize mutual information facilitate inter-modality feature interaction, thereby minimizing redundant information.We demonstrate model's efficacy through three use cases: predicting diabetic retinopathy using retinal images patient metadata, domestic violence prediction employing satellite imagery, internet, census data, identifying clinical demographic features from radiography notes. The achieved Macro F1 score of 0.92 prediction, an R-squared 0.854 sMAPE 24.868 macro AUC 0.99 disease sex classification, respectively, radiological analysis. These results underscore potential significantly impact processing, promoting its adoption diverse, resource-constrained settings.

Language: Английский

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Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Abstract Artificial intelligence (AI) is rapidly advancing, yet its applications in radiology remain relatively nascent. From a spatiotemporal perspective, this review examines the forces driving AI development and integration with medicine radiology, particular focus on advancements addressing major diseases that significantly threaten human health. Temporally, advent of foundational model architectures, combined underlying drivers development, accelerating progress interventions their practical applications. Spatially, discussion explores potential evolving methodologies to strengthen interdisciplinary within medicine, emphasizing four critical points imaging process, as well application disease management, including emergence commercial products. Additionally, current utilization deep learning reviewed, future through multimodal foundation models Generative Pre‐trained Transformer are anticipated.

Language: Английский

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