Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

Journal of Clinical Medicine, Journal Year: 2024, Volume and Issue: 13(5), P. 1505 - 1505

Published: March 5, 2024

The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). digitalization healthcare and the implementation artificial intelligence (AI) machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common ICUs. Most patients require MV. Prolonged MV associated with increased length stay, morbidity, mortality. Shortening duration both clinical economic benefits emphasizes need for better weaning management. AI ML models can assist physician from by providing predictive tools based on big data. Many have been developed recent years, dealing this unmet need. Such provide important prediction regarding success individual patient’s weaning. Some shown notable impact However, there are challenges integrating into practice due to unfamiliar nature many physicians complexity some models. Our review explores evolution methods up including as aids.

Language: Английский

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The Use of Artificial Intelligence for Skin Cancer Detection in Asia—A Systematic Review

Diagnostics, Journal Year: 2025, Volume and Issue: 15(7), P. 939 - 939

Published: April 7, 2025

Background: Artificial intelligence (AI) developed for skin cancer recognition has been shown to have comparable or superior performance dermatologists. However, it is uncertain if current AI models trained predominantly with lighter Fitzpatrick types can be effectively adapted Asian populations. Objectives: A systematic review was performed summarize the existing use of artificial detection in Methods: Systematic search conducted on PubMed and EMBASE articles published regarding amongst Information study characteristics, model outcomes collected. Conclusions: Current studies show optimistic results utilizing Asia. comparison image abilities might not a true representation diagnostic versus dermatologists real-world setting. To ensure appropriate implementation, maximize potential AI, improve transferability across various genotypes cancers, crucial focus prospective, real-world-based practice, as well expansion diversification databases used training validation.

Language: Английский

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Artificial intelligence and machine learning for anaphylaxis algorithms

Current Opinion in Allergy and Clinical Immunology, Journal Year: 2024, Volume and Issue: 24(5), P. 305 - 312

Published: July 24, 2024

Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Current management includes early recognition, prompt administration of epinephrine, immediate medical attention. However, challenges remain in accurate diagnosis, timely treatment, personalized care. This article reviews the integration artificial intelligence machine learning enhancing anaphylaxis management.

Language: Английский

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Telemedicine in dermatology

Biomedical engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 29, 2024

Telemedicine is known as the practice of diagnosing and treating patients by medical professionals from a distant location. In dermatology, telemedicine offers transformative approach to healthcare services, particularly in remote or rural areas. allows access care conveniently, ensuring both doctor patient’s safety. Multiple advantages have been described, including lowering necessity for expensive hospital trips enabling consultations. Dermatology specialized field that not universally accessible all regions ideally required. Therefore, serves useful tool facilitate evaluations various dermatological conditions. However, despite its benefits, dermatology also encounters certain obstacles. this chapter, we explore dynamic impact telemedicine, specifically dermatology.

Language: Английский

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The Role of Artificial Intelligence in the Diagnosis of Melanoma

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

The incidence of melanoma, the most aggressive form skin cancer, continues to rise globally, particularly among fair-skinned populations (type I and II). Early detection is crucial for improving patient outcomes, recent advancements in artificial intelligence (AI) have shown promise enhancing accuracy efficiency melanoma diagnosis management. This review examines role AI lesion diagnostics, highlighting two main approaches: machine learning, convolutional neural networks (CNNs), expert systems. techniques demonstrated high classifying dermoscopic images, often matching or surpassing dermatologists' performance. Integrating into dermatology has improved tasks, such as classification, segmentation, risk prediction, facilitating earlier more accurate interventions. Despite these advancements, challenges remain, including biases training data, interpretability issues, integration clinical workflows. Ensuring diverse data representation maintaining standards image quality are essential reliable Future directions involve development sophisticated models, vision-language multimodal federated learning address privacy generalizability concerns. Continuous validation ethical practice vital realizing its full potential care.

Language: Английский

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Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 15

Published: Oct. 8, 2024

The skin, the largest organ of human body, covers body surface and serves as a crucial barrier for maintaining internal environmental stability. Various microorganisms such bacteria, fungi, viruses reside on skin surface, densely arranged keratinocytes exhibit inhibitory effects pathogenic microorganisms. is an essential against microbial infections, many which manifest lesions. Therefore, rapid diagnosis related lesions utmost importance early treatment intervention infectious diseases. With continuous development artificial intelligence, significant progress has been made in healthcare, transforming healthcare services, disease diagnosis, management, including impact field dermatology. In this review, we provide detailed overview application intelligence sexually transmitted diseases caused by microorganisms, auxiliary decisions, analysis prediction epidemiological characteristics.

Language: Английский

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