Artificial Intelligence and Ethics in Dentistry: A Scoping Review

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.

Health Equity in Artificial Intelligence and Primary Care Research: Protocol for a Scoping Review (Preprint)

2021 ◽  
Author(s):  
Jonathan Xin Wang ◽  
Sulaiman Somani ◽  
Jonathan H Chen ◽  
Sara Murray ◽  
Urmimala Sarkar
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Health Equity ◽  
Scoping Review ◽  
Underserved Populations ◽  
Care Delivery ◽  
Data Extraction ◽  
Cochrane Library ◽  
Preventative Care ◽  
The Impact

BACKGROUND Though artificial intelligence (AI) has potential to augment the patient-physician relationship in primary care, bias in intelligent healthcare systems has the potential to differentially impact vulnerable patient populations. OBJECTIVE The purpose of this scoping review is to summarize the extent to which AI systems in primary care examine the inherent bias towards or against vulnerable populations and appraise how these systems have mitigated the impact of such biases during their development. METHODS We will conduct a search update from an existing scoping review to identify AI and primary care articles in the following databases: Medline-OVID,Embase,CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, and arXiv. Two screeners will independently review all abstracts, titles and full-texts. The team will extract data using structured data extraction form and synthesize the results according to PRISMA-Scr guidelines. RESULTS This review will provide an assessment of the current state of healthcare equity within AI for primary care. Specifically, we will identify the degree to which vulnerable patients have been included, assess how bias is interpreted and documented, and understand the extent harmful biases are addressed. As of October 2020, the scoping review is in the title and abstract screening stage. The results are expected to be submitted for publication in fall of 2021. CONCLUSIONS AI applications in primary care are becoming an increasingly common tool in health care delivery, including in preventative care efforts for underserved populations. This scoping review aims to understand to what extent AI-primary care studies employ a health equity lens and take steps to mitigate bias.

scholarly journals Artificial Intelligence in the Fight Against COVID-19: Scoping Review (Preprint)

2020 ◽  
Author(s):  
Alaa Abd-Alrazaq ◽  
Mohannad Alajlani ◽  
Dari Alhuwail ◽  
Jens Schneider ◽  
Saif Al-Kuwari ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Data Extraction ◽  
Length Of Hospital Stay ◽  
Support Vector ◽  
Data Sets ◽  
Daily Lives ◽  
Scoping Reviews ◽  
Target Disease ◽  
Meta Analyses

BACKGROUND In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. OBJECTIVE This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. METHODS A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. RESULTS We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. CONCLUSIONS The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

scholarly journals Artificial Intelligence in the Fight Against COVID-19: Scoping Review

10.2196/20756 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e20756 ◽  
Author(s):  
Alaa Abd-Alrazaq ◽  
Mohannad Alajlani ◽  
Dari Alhuwail ◽  
Jens Schneider ◽  
Saif Al-Kuwari ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Data Extraction ◽  
Length Of Hospital Stay ◽  
Support Vector ◽  
Data Sets ◽  
Daily Lives ◽  
Scoping Reviews ◽  
Target Disease ◽  
Meta Analyses

Background In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. Objective This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. Methods A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. Results We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. Conclusions The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Evaluation Metrics ◽  
Cancer Type ◽  
Computer Algorithms ◽  
Scoping Reviews

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.

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

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.

scholarly journals Addressing barriers to the conduct and application of research in complementary and alternative medicine: a scoping review

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yasamin Veziari ◽  
Saravana Kumar ◽  
Matthew Leach
Keyword(s):  
Alternative Medicine ◽  
Complementary And Alternative Medicine ◽  
Scoping Review ◽  
Data Extraction ◽  
Grey Literature ◽  
Evidence Base ◽  
Cochrane Library ◽  
Educational Strategies ◽  
The Cochrane Library ◽  
Complementary And Alternative

Abstract Background Over the past few decades, the popularity of complementary and alternative medicine (CAM) has grown considerably and along with it, scrutiny regarding its evidence base. While this is to be expected, and is in line with other health disciplines, research in CAM is confronted by numerous obstacles. This scoping review aims to identify and report the strategies implemented to address barriers to the conduct and application of research in CAM. Methods The scoping review was undertaken using the Arksey and O’Malley framework. The search was conducted using MEDLINE, EMBASE, EMCARE, ERIC, Scopus, Web of Science, The Cochrane Library, JBI and the grey literature. Two reviewers independently screened the records, following which data extraction was completed for the included studies. Descriptive synthesis was used to summarise the data. Results Of the 7945 records identified, 15 studies met the inclusion criteria. Using the oBSTACLES instrument as a framework, the included studies reported diverse strategies to address barriers to the conduct and application of research in CAM. All included studies reported the use of educational strategies and collaborative initiatives with CAM stakeholders, including targeted funding, to address a range of barriers. Conclusions While the importance of addressing barriers to the conduct and application of research in CAM has been recognised, to date, much of the focus has been limited to initiatives originating from a handful of jurisdictions, for a small group of CAM disciplines, and addressing few barriers. Myriad barriers continue to persist, which will require concerted effort and collaboration across a range of CAM stakeholders and across multiple sectors. Further research can contribute to the evidence base on how best to address these barriers to promote the conduct and application of research in CAM.

scholarly journals Ethics of Corporeal, Co-present Robots as Agents of Influence: a Review

2021 ◽  
Author(s):  
AJung Moon ◽  
Shalaleh Rismani ◽  
H. F. Machiel Van der Loos
Keyword(s):  
Artificial Intelligence ◽  
Physical Environment ◽  
Ethical Issues ◽  
Human Robot Interaction ◽  
Physical Interaction ◽  
Ethical Challenges ◽  
Natural Tendency ◽  
Ai Ethics ◽  
Recent Trends ◽  
The Impact

Abstract Purpose of Review To summarize the set of roboethics issues that uniquely arise due to the corporeality and physical interaction modalities afforded by robots, irrespective of the degree of artificial intelligence present in the system. Recent Findings One of the recent trends in the discussion of ethics of emerging technologies has been the treatment of roboethics issues as those of “embodied AI,” a subset of AI ethics. In contrast to AI, however, robots leverage human’s natural tendency to be influenced by our physical environment. Recent work in human-robot interaction highlights the impact a robot’s presence, capacity to touch, and move in our physical environment has on people, and helping to articulate the ethical issues particular to the design of interactive robotic systems. Summary The corporeality of interactive robots poses unique sets of ethical challenges. These issues should be considered in the design irrespective of and in addition to the ethics of artificial intelligence implemented in them.

scholarly journals Clinical parameters and biomarkers predicting spontaneous operational tolerance after liver transplantation: a scoping review protocol

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 2059
Author(s):  
Christian Appenzeller-Herzog ◽  
Steffen Hartleif ◽  
Julien Vionnet
Keyword(s):  
Autoimmune Disease ◽  
Viral Hepatitis ◽  
Scoping Review ◽  
Data Extraction ◽  
Clinical Parameter ◽  
Cochrane Library ◽  
Narrative Form ◽  
Clinical Parameters ◽  
Clinical Trial Registries ◽  
Operational Tolerance

Objective: This scoping review aims at systematically mapping reported prognostic factors for spontaneous immunosuppression (IS) free allograft tolerance (operational tolerance, OT) in non-viral hepatitis and non-autoimmune disease liver transplant (LT) recipients who are undergoing immunosuppression withdrawal (ISW). The results may inform the subsequent conduct of a systematic review with a more specific review question. Background: LT is currently the most effective treatment for end-stage liver diseases. Whereas the short-term outcomes after LT have dramatically improved over the last decades, the long-term outcomes remain unsatisfactory, mainly because of side effects of lifelong IS, such as infections, cardiovascular diseases, malignancies, and nephrotoxicity. ISW studies have shown that OT can be achieved by a subset of LT recipients and recent research has identified biomarkers of OT in these patients. However, an evidence-based selection algorithm for patients that can predictably benefit from ISW is not available to date. The planned review will, therefore, map existing knowledge on prognostic clinical parameters and biomarkers for OT. Inclusion criteria: We will consider studies that record any clinical parameter or biomarker before the initiation of ISW in paediatric or adult non-viral hepatitis and non-autoimmune disease LT recipients and analyse their possible association with ISW outcomes (OT or non-tolerance). Studies addressing the effectiveness of OT-inducing treatments will be excluded. Methods: Embase, MEDLINE, and Cochrane Library will be searched for relevant articles or conference abstracts. Full-texts of selected abstracts will be independently screened for inclusion by two reviewers. References and citing articles of included records will be screened for additional relevant records. Clinical trial registries will be searched for ongoing studies, and their investigators contacted for the sharing of unpublished data. Data from included records will be independently extracted by two reviewers using a prespecified data extraction table and presented in both tabular and narrative form.

scholarly journals Functionalized Nanoparticles Activated by Photodynamic Therapy as an Antimicrobial Strategy in Endodontics: A Scoping Review

Antibiotics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1064
Author(s):  
Pablo Betancourt ◽  
Nadia Brocal ◽  
Eulàlia Sans-Serramitjana ◽  
Carlos Zaror
Keyword(s):  
Photodynamic Therapy ◽  
Scoping Review ◽  
Selection Process ◽  
Data Extraction ◽  
Cochrane Library ◽  
Qualitative Synthesis ◽  
Promising Alternative ◽  
Root Canals ◽  
Low Cytotoxicity

The eradication of endodontic pathogens continues to be the focus of the search for new root canal system (RCS) disinfection strategies. This scoping review provides a comprehensive synthesis of antimicrobial photodynamic therapy (aPDT) using nanoparticles (NPs) as an alternative to optimize RCS disinfection. A systematic search up to March 2021 was carried out using the MEDLINE, EMBASE, Scopus, Lilacs, Central Cochrane Library, and BBO databases. We included studies focused on evaluating the activation of NPs by aPDT in inoculated root canals of human or animal teeth or bacterial cultures in the laboratory. The selection process and data extraction were carried out by two researchers independently. A qualitative synthesis of the results was performed. A total of seventeen studies were included, of which twelve showed a substantial antibacterial efficacy, two assessed the substantivity of the disinfection effect, and three showed low cytotoxicity. No adverse effects were reported. The use of functionalized NPs with photosensitizer molecules in aPDT has been shown to be effective in reducing the bacteria count, making it a promising alternative in endodontic disinfection. Further studies are needed to assess the development of this therapy in in vivo conditions, with detailed information about the laser parameters used to allow the development of safe and standardized protocols.

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

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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