Applications of Artificial Intelligence in Healthcare

Quazi, Shagufta; Saha, Rudra P.; Singh, Manoj Kumar

doi:10.18006/2022.10(1).211.226

Cited by 11 publications

(4 citation statements)

References 90 publications

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“…It has been demonstrated that these controllers can outperform traditional model-based controllers, particularly when used with challenging-to-model nonlinear systems. There are four main components that make up the FLC configuration at its core [71,72]:…”

Section: Robotic Process Automationmentioning

confidence: 99%

Current Trends in Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic Control: Systematic Literature Review

Raharja

Suwarno

Sugiyarta³

2023

Journal of Robotics and Control

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Incubator Control for Premature Babies has benefited greatly from the development of creative methods and uses of artificial intelligence. Due to the immaturity of the epidermis, premature infants lose fluid and heat early in life, which causes hyperosmolar dehydration and hypothermia. Water loss through the epidermis. Therefore, in order to maintain the baby's healthy temperature, an incubator is required. As a result, it is anticipated that the baby will maintain the same temperature as in the mother's womb. A temperature regulation system with good measurement and regulation quality is necessary due to the necessity of Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic in treating premature infants. The purpose of this research is to assess current trends in artificial intelligence-based fuzzy logic incubator control for preterm infants. The Preferred Reporting Items for Systematic Review (PRISMA) were used in this study's systematic literature review. 188 suitable articles that fit the inclusion requirements were found after the articles were screened and chosen. The outcomes demonstrated that the Incubator Control for Premature Infants offered the best environment for newborns with growth or disease-related issues (premature babies). An incubator is a sealed space free of dust and bacteria with the ability to regulate temperature, humidity, and oxygen to maintain a stable environment.

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Section: Robotic Process Automationmentioning

confidence: 99%

Current Trends in Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic Control: Systematic Literature Review

Raharja

Suwarno

Sugiyarta³

2023

Journal of Robotics and Control

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“…They are now used in clinical settings to help healthcare workers and patients. For instance, hospitals and clinics are deploying robots for various tasks to help reduce pathogen exposure during the COVID-19 pandemic [9].…”

Section: Healthcare Fieldmentioning

confidence: 99%

Security Vulnerabilities and Threats in Robotic Systems: A Comprehensive Review

Abed¹,

Al-Doori²,

Abdullah³

et al. 2023

IJSSE

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The recent digital revolution has resulted in robots being integrated more than ever into various domains, such as agriculture, healthcare, and the military. Robots are dedicated to serving, facilitating, and improving human life. However, the growing prevalence of robotics has brought to light the need for robust security measures. While unintentional accidents are inevitable, this paper focuses on the increasingly challenging problem of malicious cyber-attacks against robotic systems. One specific incident highlighting the severity of this issue occurred in 2022 when a healthcare robot was hacked, resulting in the misadministration of medication to several patients, leading to severe health complications and unnecessary loss of human lives. Such instances underscore the urgency of understanding the robotics domain's security vulnerabilities, threats, and consequences. In conclusion, this paper highlights the critical aspects of securing robotic systems in today's technologically advanced world. By identifying and analyzing the primary security vulnerabilities, this paper examines the primary security vulnerabilities, the type of application, then the impact of vulnerabilities; we can pave the way for effective security measures and ultimately ensure the safety and reliability of robotic systems.

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“…In subsequent years, accuracy in classifying pulmonary nodules has increased to 95.3% using unlabeled C.T. (Quazi et al 2022).…”

Section: Predicting Lung Diseases Via DL Modelsmentioning

confidence: 99%

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Vohra,

Mittal

2023

J Exp Bio & Ag Sci

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Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy.

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Applications of Artificial Intelligence in Healthcare

Cited by 11 publications

References 90 publications

Current Trends in Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic Control: Systematic Literature Review

Current Trends in Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic Control: Systematic Literature Review

Security Vulnerabilities and Threats in Robotic Systems: A Comprehensive Review

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

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