The Application of artificial intelligence in periprosthetic joint infection

Journal of Advanced Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Periprosthetic joint infection (PJI) represents one of the most devastating complications following total arthroplasty, often necessitating additional surgeries and antimicrobial therapy, potentially leading to disability. This significantly increases burden on both patients healthcare system. Given considerable suffering caused by PJI, its prevention treatment have long been focal points concern. However, challenges remain in accurately assessing individual risk, preventing infection, improving diagnostic methods, enhancing outcomes. The development application artificial intelligence (AI) technologies introduced new, more efficient possibilities for management many diseases. In this article, we review applications AI prevention, diagnosis, explore how methodologies might achieve individualized risk prediction, improve algorithms through biomarkers pathology, enhance efficacy surgical treatments. We hope that multimodal applications, intelligent PJI can be realized future.

Language: Английский

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Large model-driven hyperscale healthcare data fusion analysis in complex multi-sensors

Information Fusion, Journal Year: 2024, Volume and Issue: 115, P. 102780 - 102780

Published: Nov. 4, 2024

Language: Английский

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Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review

JMIR Medical Informatics, Journal Year: 2024, Volume and Issue: 12, P. e60164 - e60164

Published: Oct. 21, 2024

Background In response to the intricate language, specialized terminology outside everyday life, and frequent presence of abbreviations acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial transformer-based models. This refinement knowledge language models (LMs) allows for a better understanding medical textual which results an improvement downstream tasks, such information extraction (IE). We identified gap literature regarding LMs. Therefore, this study presents scoping review investigating methods transformers care, differentiating between English non-English languages, focusing on Portuguese. Most specifically, we investigated development LMs, with aim comparing Portuguese other more developed languages guide path non–English-language fewer resources. Objective aimed research IE models, regardless understand efficacy what are entities most commonly extracted. Methods was conducted using PRISMA-ScR (Preferred Reporting Items Systematic reviews Meta-Analyses extension Scoping Reviews) methodology Scopus Web Science Core Collection databases. Only studies that mentioned creation LMs or were included, while large (LLMs) excluded. The latest not included since wanted LLMs, architecturally different distinct purposes. Results Our search query retrieved 137 studies, 60 met inclusion criteria, none them systematic reviews. Chinese developed. These already disease-specific others only general–health European does any public LM should take examples from develop, first, general-health then, advanced phase, Regarding used method, named entity recognition popular topic, few mentioning Assertion Status addressing lexical problems. extracted diagnosis, posology, symptoms. Conclusions findings indicate is beneficial, achieving tasks. analysis allowed us use languages. lacks relevant draw develop these drive progress AI. Health professionals could benefit highlighting medically optimizing reading be create patient timelines, allowing profiling.

Language: Английский

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Web Development and Evaluation of Instructional Objectives’ Quality Using Natural Language Processing

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 379 - 391

Published: Jan. 1, 2025

Language: Английский

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CMedRAGBot: A Chinese Medical Chatbot Based on Graph RAG and Large Language Models

Interdisciplinary Sciences Computational Life Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: June 5, 2025

Language: Английский

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Exploring the Potential of Artificial Intelligence in Airway Management

Trends in Anaesthesia and Critical Care, Journal Year: 2024, Volume and Issue: 59, P. 101512 - 101512

Published: Dec. 1, 2024

Language: Английский

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Artificial intelligence-based power market price prediction in smart renewable energy systems: Combining prophet and transformer models

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e38227 - e38227

Published: Oct. 1, 2024

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It not only crucial for interests suppliers regulators but also plays a key role in ensuring reliable flexible operation system, during extreme weather events or abnormal conditions. This study develops hybrid time series forecasting model that combines Prophet Transformer, which takes advantage deep learning to provide new solution forecasting. By introducing Stacking optimization strategy, this improves accuracy stability sequence prediction. In addition, tries integrate traditional methods (such as model) with models Transformer model), aiming make full use their respective advantages achieve more accurate stable predictions. Through experimental evaluation on four data sets, finds forecast exhibits significant performance improvements enhancing method provides tool prediction, solid technical support efficient sustainable development systems. Experimental results show combination introduction strategies improving helps us better understand design systems, management strategies, thereby providing an effective achieving transmission.

Language: Английский

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Clinical Text Classification in Healthcare: Leveraging BERT for NLP

Published: Dec. 29, 2023

The use of the Bidirectional Encoder Representations from Transformers (BERT) model for clinical text classification in healthcare industry is investigated this study. Using a descriptive design and secondary data collection, study takes deductive approach interpretivism as its guiding philosophy. results, which emphasize accuracy interpretability, demonstrate BERT's superior efficacy over conventional methods. Its revolutionary effect on analytics demonstrated by comparative analysis. significance smooth integration, ongoing improvement, ethical considerations highlighted knowledge about practical application. Subsequent research endeavors ought to concentrate refining domain-specific fine-tuning, improving user interfaces, investigating decentralized learning strategies, maximizing BERT resource utilization.

Language: Английский

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Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review (Preprint)

Published: May 3, 2024

In response to the intricate language, specialized terminology outside everyday life, and frequent presence of abbreviations acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial transformer-based models. This refinement knowledge language models (LMs) allows for a better understanding medical textual which results an improvement downstream tasks, such information extraction (IE). We identified gap literature regarding LMs. Therefore, this study presents scoping review investigating methods transformers care, differentiating between English non-English languages, focusing on Portuguese. Most specifically, we investigated development LMs, with aim comparing Portuguese other more developed languages guide path non–English-language fewer resources. aimed research IE models, regardless understand efficacy what are entities most commonly extracted. was conducted using PRISMA-ScR (Preferred Reporting Items Systematic reviews Meta-Analyses extension Scoping Reviews) methodology Scopus Web Science Core Collection databases. Only studies that mentioned creation LMs or were included, while large (LLMs) excluded. The latest not included since wanted LLMs, architecturally different distinct purposes. Our search query retrieved 137 studies, 60 met inclusion criteria, none them systematic reviews. Chinese developed. These already disease-specific others only general–health European does any public LM should take examples from develop, first, general-health then, advanced phase, Regarding used method, named entity recognition popular topic, few mentioning Assertion Status addressing lexical problems. extracted diagnosis, posology, symptoms. findings indicate is beneficial, achieving tasks. analysis allowed us use languages. lacks relevant draw develop these drive progress AI. Health professionals could benefit highlighting medically optimizing reading be create patient timelines, allowing profiling.

Language: Английский

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Toward an enhanced automatic medical report generator based on large transformer models

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 16, 2024

Language: Английский

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