Computers in Industry, Journal Year: 2024, Volume and Issue: 165, P. 104233 - 104233
Published: Dec. 27, 2024
Language: Английский
Current Cardiology Reports, Journal Year: 2025, Volume and Issue: 27(1)
Published: Feb. 1, 2025
Language: Английский
Citations
0
Bioengineering, Journal Year: 2025, Volume and Issue: 12(5), P. 440 - 440
Published: April 23, 2025
Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing field of medicine. LLMs possess capability to analyze scientific literature and genomic data by comprehending producing human-like text. This enhances accuracy, precision, efficiency extensive analyses through contextualization. have made significant advancements in their ability understand complex genetic terminology accurately predict medical outcomes. These capabilities allow for a more thorough understanding influences on health issues creation effective therapies. review emphasizes LLMs’ impact healthcare, evaluates triumphs limitations processing, makes recommendations addressing these order enhance system. It explores latest analysis, focusing enhancing disease diagnosis treatment accuracy taking account an individual’s composition. also anticipates future which AI-driven analysis commonplace clinical practice, suggesting potential research areas. To effectively leverage personalized medicine, it vital actively support innovation across multiple sectors, ensuring that AI developments directly contribute solutions tailored individual patients.
Language: Английский
Citations
0
Cureus, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 29, 2025
Transcatheter aortic valve replacement (TAVR) is a minimally invasive procedure used to replace damaged with prosthetic valve. TAVR has exceeded surgical (SAVR) due shorter procedures and recovery times. Though initially approved for patients stenosis at high risk, TAVR's indications have now broadened include high, intermediate, low-risk patients. This review focuses on the evolving role of in bicuspid valves (BAV). We examine anatomical hemodynamic differences between tricuspid BAV, highlighting unique challenges faces BAV
Language: Английский
Citations
0
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2025, Volume and Issue: 15(2)
Published: May 7, 2025
ABSTRACT Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation explanation share common ancient roots. This is further enforced by lack review works jointly covering these two fields. In this paper, we investigate literature to try understand how what extent causality XAI are intertwined. More precisely, seek uncover kinds relationships exist between one can benefit from them, for instance, building trust AI systems. As a result, three main perspectives identified. first one, seen major limitations current approaches, “optimal” form explanations investigated. The second pragmatic perspective considers tool foster scientific exploration causal inquiry, via identification pursue‐worthy experimental manipulations. Finally, third supports idea that propaedeutic possible manners: exploiting borrowed support or improve XAI, utilizing counterfactuals explainability, considering accessing model explaining itself. To complement our analysis, also provide relevant software solutions used automate tasks. We believe work provides unified view highlighting potential domain bridges uncovering limitations.
Language: Английский
Citations
0
Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 465 - 494
Published: Dec. 13, 2024
The integration of Artificial Intelligence (AI) in heart disease care is transforming the landscape precision medicine, enabling more accurate diagnosis, improved prediction progression, and personalized treatment strategies. Utilising AI cardiology improves patient outcomes maximises healthcare systems' efficiency. By leveraging machine learning algorithms data analytics, AI-powered tools can analyze vast amounts medical data, including electrocardiograms, images, records, to identify patterns insights that may not be apparent human experts. This chapter explores how improve care, early detection abnormalities through image analysis, proactive risk prediction, working with wearable devices plans tailored individual needs. Explainable (XAI) enhances management by providing transparent into decision-making processes
Language: Английский
Citations
0
Computers in Industry, Journal Year: 2024, Volume and Issue: 165, P. 104233 - 104233
Published: Dec. 27, 2024
Language: Английский
Citations
0