Artificial Intelligence for diagnosis of fractures on plain radiographs: a scoping review of current literature

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

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Artificial intelligence for good health: a scoping review of the ethics literature

BMC Medical Ethics ◽

10.1186/s12910-021-00577-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Kathleen Murphy ◽

Erica Di Ruggiero ◽

Ross Upshur ◽

Donald J. Willison ◽

Neha Malhotra ◽

...

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Global Health ◽

Population Health ◽

Health Systems ◽

Scoping Review ◽

Industrial Revolution ◽

Ethical Issues ◽

Grey Literature ◽

Ethical Implications

Abstract Background Artificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective? Methods Eight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed. Results Upon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs). Conclusions The ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere.

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Artificial Intelligence and Ethics in Dentistry: A Scoping Review

Journal of Dental Research ◽

10.1177/00220345211013808 ◽

2021 ◽

pp. 002203452110138

Author(s):

C.M. Mörch ◽

S. Atsu ◽

W. Cai ◽

X. Li ◽

S.A. Madathil ◽

...

Keyword(s):

Artificial Intelligence ◽

Scoping Review ◽

Ethical Issues ◽

Data Extraction ◽

Cochrane Library ◽

Data Sets ◽

Ethical Challenges ◽

Internal Validation ◽

Neural Learning ◽

Learning Techniques

Dentistry increasingly integrates artificial intelligence (AI) to help improve the current state of clinical dental practice. However, this revolutionary technological field raises various complex ethical challenges. The objective of this systematic scoping review is to document the current uses of AI in dentistry and the ethical concerns or challenges they imply. Three health care databases (MEDLINE [PubMed], SciVerse Scopus, and Cochrane Library) and 2 computer science databases (ArXiv, IEEE Xplore) were searched. After identifying 1,553 records, the documents were filtered, and a full-text screening was performed. In total, 178 studies were retained and analyzed by 8 researchers specialized in dentistry, AI, and ethics. The team used Covidence for data extraction and Dedoose for the identification of ethics-related information. PRISMA guidelines were followed. Among the included studies, 130 (73.0%) studies were published after 2016, and 93 (52.2%) were published in journals specialized in computer sciences. The technologies used were neural learning techniques for 75 (42.1%), traditional learning techniques for 76 (42.7%), or a combination of several technologies for 20 (11.2%). Overall, 7 countries contributed to 109 (61.2%) studies. A total of 53 different applications of AI in dentistry were identified, involving most dental specialties. The use of initial data sets for internal validation was reported in 152 (85.4%) studies. Forty-five ethical issues (related to the use AI in dentistry) were reported in 22 (12.4%) studies around 6 principles: prudence (10 times), equity (8), privacy (8), responsibility (6), democratic participation (4), and solidarity (4). The ratio of studies mentioning AI-related ethical issues has remained similar in the past years, showing that there is no increasing interest in the field of dentistry on this topic. This study confirms the growing presence of AI in dentistry and highlights a current lack of information on the ethical challenges surrounding its use. In addition, the scarcity of studies sharing their code could prevent future replications. The authors formulate recommendations to contribute to a more responsible use of AI technologies in dentistry.

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Artificial intelligence in outcomes research: a systematic scoping review

Expert Review of Pharmacoeconomics & Outcomes Research ◽

10.1080/14737167.2021.1886083 ◽

2021 ◽

pp. 1-23

Author(s):

Pooyeh Graili ◽

Luciano Ieraci ◽

Nazanin Hosseinkhah ◽

Mary Argent-Katwala

Keyword(s):

Artificial Intelligence ◽

Scoping Review ◽

Outcomes Research

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Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

npj Digital Medicine ◽

10.1038/s41746-021-00459-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Ania Syrowatka ◽

Masha Kuznetsova ◽

Ava Alsubai ◽

Adam L. Beckman ◽

Paul A. Bain ◽

...

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Real Time ◽

Scoping Review ◽

Grey Literature ◽

Use Cases ◽

Outbreak Detection ◽

Response To Treatment ◽

Pandemic Preparedness ◽

Modeling Approaches

AbstractArtificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

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Maternal periconceptional nutrition matters. A scoping review of the current literature

The Journal of Maternal-Fetal & Neonatal Medicine ◽

10.1080/14767058.2021.1962843 ◽

2021 ◽

pp. 1-18

Author(s):

Caterina Montagnoli ◽

Chiara Beatrice Santoro ◽

Tanita Buzzi ◽

Renata Bortolus

Keyword(s):

Current Literature ◽

Scoping Review

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Online or Offline: Does It Matter? A Review of Existing Interpretation Approaches and Their Effect on Screening Mammography Metrics, Patient Satisfaction, and Cost

Journal of Breast Imaging ◽

10.1093/jbi/wbab086 ◽

2021 ◽

Author(s):

Shruthi Ram ◽

Tyler Campbell ◽

Ana P Lourenco

Keyword(s):

Artificial Intelligence ◽

Patient Satisfaction ◽

Current Literature ◽

Performance Metrics ◽

Screening Mammography ◽

Online Reading ◽

The Future ◽

Workflow Efficiency ◽

Ideal Practice ◽

The Ideal

Abstract The ideal practice routine for screening mammography would optimize performance metrics and minimize costs, while also maximizing patient satisfaction. The main approaches to screening mammography interpretation include batch offline, non-batch offline, interrupted online, and uninterrupted online reading, each of which has its own advantages and drawbacks. This article reviews the current literature on approaches to screening mammography interpretation, potential effects of newer technologies, and promising artificial intelligence resources that could improve workflow efficiency in the future.

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Artificial Intelligence in Predicting Cardiac Arrest: A Scoping Review (Preprint)

10.2196/preprints.30798 ◽

2021 ◽

Author(s):

Asma Alamgir ◽

Osama Mousa 2nd ◽

Zubair Shah 3rd

Keyword(s):

Artificial Intelligence ◽

At Risk ◽

Cardiac Arrest ◽

Scoping Review ◽

Pediatric Population ◽

Data Extraction ◽

Warning System ◽

Reference List ◽

Evaluation Tool ◽

Patients At Risk

BACKGROUND Cardiac arrest is a life-threatening cessation of heart activity. Early prediction of cardiac arrest is important as it provides an opportunity to take the necessary measures to prevent or intervene during the onset. Artificial intelligence technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS Scoping review was conducted in line with guidelines of PRISMA Extension for Scoping Review (PRISMA-ScR). Scopus, Science Direct, Embase, IEEE, and Google Scholar were searched to identify relevant studies. Backward reference list checking of included studies was also conducted. The study selection and data extraction were conducted independently by two reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. We were able to classify the approach taken by the studies in three different categories - 26 studies predicted cardiac arrest by analyzing specific parameters or variables of the patients while 16 studies developed an AI-based warning system. The rest of the 5 studies focused on distinguishing high-risk cardiac arrest patients from patients, not at risk. 2 studies focused on the pediatric population, and the rest focused on adults (n=45). The majority of the studies used datasets with a size of less than 10,000 (n=32). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (n=38) and the most used algorithm belonged to the neural network (n=23). K-Fold cross-validation was the most used algorithm evaluation tool reported in the studies (n=24). CONCLUSIONS : AI is extensively being used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in changing cardiac medicine for the better. There is a need for more reviews to learn the obstacles of implementing AI technologies in the clinical setting. Moreover, research focusing on how to best provide clinicians support to understand, adapt and implement the technology in their practice is also required.

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Emerging Artificial Intelligence-Empowered Mobile Health: A scoping review (Preprint)

10.2196/preprints.35053 ◽

2021 ◽

Author(s):

Paras Bhatt ◽

Jia Liu ◽

Yanmin Gong ◽

Jing Wang ◽

Yuanxiong Guo

Keyword(s):

Mental Health ◽

Artificial Intelligence ◽

Disease Management ◽

Scoping Review ◽

Predictive Models ◽

Healthcare Delivery ◽

Chronic Health Conditions ◽

Health Conditions ◽

Health Issues ◽

Mental Health Issues

BACKGROUND Artificial Intelligence (AI) has revolutionized healthcare delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions. OBJECTIVE Currently, little is known about the use of AI-powered mHealth settings. Therefore, this scoping review aims to map current research on the emerging use of AI-powered mHealth (AIM) for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for healthcare delivery in the last two years. METHODS Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we review AIM literature from the past two years in the fields of Biomedical Technology, AI, and Information Systems (IS). We searched three databases - informs PubsOnline, e-journal archive at MIS Quarterly, and ACM Digital Library using keywords such as mobile healthcare, wearable medical sensors, smartphones and AI. We include AIM articles and exclude technical articles focused only on AI models. Also, we use the PRISMA technique for identifying articles that represent a comprehensive view of current research in the AIM domain. RESULTS We screened 108 articles focusing on developing AIM models for ensuring better healthcare delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion. A majority of the articles were published last year (31/37). In the selected articles, AI models were used to detect serious mental health issues such as depression and suicidal tendencies and chronic health conditions such as sleep apnea and diabetes. The articles also discussed the application of AIM models for remote patient monitoring and disease management. The primary health concerns addressed relate to three categories: mental health, physical health, and health promotion & wellness. Of these, AIM applications were majorly used to research physical health, representing 46% of the total studies. Finally, a majority of studies use proprietary datasets (28/37) rather than public datasets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available datasets for AIM research. CONCLUSIONS The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the healthcare domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques such as Federated Learning (FL) and Explainable AI (XAI) can act as a catalyst to increase the adoption of AIM and enable secure data sharing across the healthcare industry.

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The Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry – A Scoping Review (Preprint)

10.2196/preprints.33560 ◽

2021 ◽

Author(s):

Victoria Welch ◽

Tom Joshua Wy ◽

Anna Ligezka ◽

Leslie C. Hassett ◽

Paul E. Croarkin ◽

...

Keyword(s):

Mental Health ◽

Artificial Intelligence ◽

Scoping Review ◽

Adolescent Psychiatry ◽

Child And Adolescent ◽

Mental Health Disorders ◽

Wearable Devices ◽

Autism Spectrum ◽

Child And Adolescent Psychiatry ◽

Health Disorders

BACKGROUND Mental health disorders across the life span are a leading cause of medical disabilities. This burden is particularly significant in children and adolescents due to challenges in diagnoses and lack of precision medicine approaches. The advent and widespread adoption of wearable devices (e.g., smartwatches) that generate large volumes of passively collected data that are conducive for artificial intelligence applications to remotely diagnose and manage child and adolescent mental health disorders is promising. OBJECTIVE This study conducted a scoping review to study, characterize and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of mental health disorders in child and adolescent psychiatry. METHODS This scoping review used PRISMA’s information as a guide. A comprehensive search of several databases from 2011 to June 25, 2021, limited to English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE (R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Web of Science, and Scopus. RESULTS The initial search yielded 344 articles. 19 articles were left on the final source list for this scoping review. Articles were divided into three main groups: Studies with the main focus on Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorders (ADHD) and Internalizing disorders such as anxiety disorders. Majority of the studies used either ECG strap or wrist worn biosensor. CONCLUSIONS Our scoping review found large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gaps identified in this scoping review are the lack of randomized control trials, most available studies are pilot feasibility trials.

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