Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review (Preprint)

Support Vector ◽

Quantitative Studies ◽

Safety Outcomes ◽

Tree Models

BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

JMIR Medical Informatics ◽

10.2196/18599 ◽

2020 ◽

Vol 8 (7) ◽

pp. e18599 ◽

Cited By ~ 2

Author(s):

Avishek Choudhury ◽

Onur Asan

Keyword(s):

Artificial Intelligence ◽

Patient Safety ◽

Literature Review ◽

Decision Tree ◽

Support Vector ◽

Quantitative Studies ◽

Safety Outcomes ◽

Tree Models

Background Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

What databases should we include in a comprehensive search strategy protocol for systematic reviews of artificial intelligence? (Preprint)

10.2196/preprints.22918 ◽

2020 ◽

Author(s):

Dylan Mordaunt

Keyword(s):

Artificial Intelligence ◽

Patient Safety ◽

Literature Review ◽

Systematic Reviews ◽

Search Strategy ◽

Safety Outcomes ◽

Comprehensive Search ◽

Comprehensive Search Strategy

UNSTRUCTURED This is a commentary of the article by Choudhury and Asan entitled, “Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review”.

Use of AI and Machine Learning for Asthma Patients: A Systematic Literature Review

Malaysian Journal of Medical and Biological Research ◽

10.18034/mjmbr.v7i2.517 ◽

2020 ◽

Vol 7 (2) ◽

pp. 129-134

Author(s):

Takudzwa Fadziso

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Literature Review ◽

Learning Algorithms ◽

Modern Times ◽

Predictive Algorithms ◽

Asthma Patients

In modern times, the collection of data is not a big deal but using it in a meaningful is a challenging task. Different organizations are using artificial intelligence and machine learning for collecting and utilizing the data. These should also be used in the medical because different disease requires the prediction. One of these diseases is asthma that is continuously increasing and affecting more and more people. The major issue is that it is difficult to diagnose in children. Machine learning algorithms can help in diagnosing it early so that the doctors can start the treatment early. Machine learning algorithms can perform this prediction so this study will be helpful for both the doctors and patients. There are different machine learning predictive algorithms are available that have been used for this purpose.

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

Electronics ◽

10.3390/electronics11020198 ◽

2022 ◽

Vol 11 (2) ◽

pp. 198

Author(s):

Mujaheed Abdullahi ◽

Yahia Baashar ◽

Hitham Alhussian ◽

Ayed Alwadain ◽

Norshakirah Aziz ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Internet Of Things ◽

Literature Review ◽

Rapid Development ◽

Security And Privacy ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

Machines ◽

10.3390/machines9120351 ◽

2021 ◽

Vol 9 (12) ◽

pp. 351

Author(s):

Lorenzo Colantonio ◽

Lucas Equeter ◽

Pierre Dehombreux ◽

François Ducobu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Literature Review ◽

Research Question ◽

Support Vector ◽

Self Organizing Map ◽

Holistic View ◽

Benchmark Database ◽

Depth Analysis

In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.

Pain Relief and Safety Outcomes with Cervical 10 kHz Spinal Cord Stimulation: Systematic Literature Review and Meta-analysis

Pain and Therapy ◽

10.1007/s40122-021-00269-6 ◽

2021 ◽

Author(s):

Ganesan Baranidharan ◽

Beatrice Bretherton ◽

Craig Montgomery ◽

John Titterington ◽

Tracey Crowther ◽

...

Keyword(s):

Spinal Cord ◽

Pain Relief ◽

Literature Review ◽

Spinal Cord Stimulation ◽

Meta Analysis ◽

Safety Outcomes

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01403-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Alan Brnabic ◽

Lisa M. Hess

Keyword(s):

Machine Learning ◽

Decision Making ◽

Literature Review ◽

Real World ◽

Learning Algorithms ◽

External Validation ◽

Learning Methods ◽

Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.