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.

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.

scholarly journals The Impact of Artificial Intelligence on Health Equity in Oncology: A Scoping Review

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4934-4934
Author(s):  
Paul Istasy ◽  
Wen Shen Lee ◽  
Alla Iansavitchene ◽  
Ross Upshur ◽  
Bekim Sadikovic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health Equity ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Ethical Issues ◽  
Clinical Decision ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
The Impact

Abstract Introduction: The expanding use of Artificial Intelligence (AI) in hematology and oncology research and practice creates an urgent need to consider the potential impact of these technologies on health equity at both local and global levels. Fairness and equity are issues of growing concern in AI ethics, raising problems ranging from bias in datasets and algorithms to disparities in access to technology. The impact of AI on health equity in oncology, however, remains underexplored. We conducted a scoping review to characterize, evaluate, and identify gaps in the existing literature on AI applications in oncology and their implications for health equity in cancer care. Methodology: We performed a systematic literature search of MEDLINE (Ovid) and EMBASE from January 1, 2000 to March 28, 2021 using key terms for AI, health equity, and cancer. Our search was restricted to English language abstracts with no restrictions by publication type. Two reviewers screened a total of 9519 abstracts, and 321 met inclusion criteria for full-text review. 247 were included in the final analysis after assessment by three reviewers. Studies were analysed descriptively, by location, type of cancer and AI application, as well as thematically, based on issues pertaining to health equity in oncology. Results: Of the 247 studies included in our analysis, 150 (60.7%) were based in North America, 57 (23.0%) in Asia, 29 (11.7%) in Europe, 5 (2.1%) in Central/South America, 4 (1.6%) in Oceania, and 2 (0.9%) in Africa. 71 (28.6%) were reviews and commentaries, and 176 were (71.3%) clinical studies. 25 (10.1%) focused on AI applications in screening, 42 (17.0%) in diagnostics, 46 (18.6%) in prognostication, and 7 (2.9%) in treatment. 40 (16.3%) used AI as a tool for clinical/epidemiological research and 87 (35.2%) discussed multiple applications of AI. A diverse range of cancers were represented in the studies, including hematologic malignancies. Our scoping review identified three overarching themes in the literature, which largely focused on how AI might improve health equity in oncology. These included: (1) the potential for AI reduce disparities by improving access to health services in resource-limited settings through applications such as low-cost cancer screening technologies and decision support systems; (2) the ability of AI to mitigate bias in clinical decision-making through algorithms that alert clinicians to potential sources of bias thereby allowing for more equitable and individualized care; (3) the use of AI as a research tool to identify disparities in cancer outcomes based on factors such as race, gender and socioeconomic status, and thus inform health policy. While most of the literature emphasized the positive impact of AI in oncology, there was only limited discussion of AI's potential adverse effects on health equity . Despite engaging with the use of AI in resource-limited settings, ethical issues surrounding data extraction and AI trials in low-resource settings were infrequently raised. Similarly, AI's potential to reinforce bias and widen disparities in cancer care was under-examined despite engagement with related topics of bias in clinical decision-making. Conclusion: The overwhelming majority of the literature identified by our scoping review highlights the benefits of AI applications in oncology, including its potential to improve access to care in low-resource settings, mitigate bias in clinical decision-making, and identify disparities in cancer outcomes. However, AI's potential negative impacts on health equity in oncology remain underexplored: ethical issues arising from deploying AI technologies in low-resources settings, and issues of bias in datasets and algorithms were infrequently discussed in articles dealing with related themes. Disclosures No relevant conflicts of interest to declare.

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 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 Primary care teams’ experiences of delivering mental health care during the COVID-19 pandemic: a qualitative study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rachelle Ashcroft ◽  
Catherine Donnelly ◽  
Maya Dancey ◽  
Sandeep Gill ◽  
Simon Lam ◽  
...  
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Health Care ◽  
Qualitative Study ◽  
Mental Health Care ◽  
Focus Groups ◽  
Care Delivery ◽  
Virtual Care ◽  
Primary Care Teams ◽  
The Impact

Abstract Background Integrated primary care teams are ideally positioned to support the mental health care needs arising during the COVID-19 pandemic. Understanding how COVID-19 has affected mental health care delivery within primary care settings will be critical to inform future policy and practice decisions during the later phases of the pandemic and beyond. The objective of our study was to describe the impact of the COVID-19 pandemic on primary care teams’ delivery of mental health care. Methods A qualitative study using focus groups conducted with primary care teams in Ontario, Canada. Focus group data was analysed using thematic analysis. Results We conducted 11 focus groups with 10 primary care teams and a total of 48 participants. With respect to the impact of the COVID-19 pandemic on mental health care in primary care teams, we identified three key themes: i) the high demand for mental health care, ii) the rapid transformation to virtual care, and iii) the impact on providers. Conclusions From the outset of the COVID-19 pandemic, primary care quickly responded to the rising mental health care demands of their patients. Despite the numerous challenges they faced with the rapid transition to virtual care, primary care teams have persevered. It is essential that policy and decision-makers take note of the toll that these demands have placed on providers. There is an immediate need to enhance primary care’s capacity for mental health care for the duration of the pandemic and beyond.

Virtual Primary Care Implementation during COVID-19 in High-Income Countries: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Kristina De Vera ◽  
Priyanka Challa ◽  
Rebecca H Liu ◽  
Kaitlin Fuller ◽  
Anam Shahil Feroz ◽  
...  
Keyword(s):  
Primary Care ◽  
Scoping Review ◽  
Primary Care Physicians ◽  
Underserved Populations ◽  
High Income ◽  
Lessons Learned ◽  
Regulatory Policies ◽  
Care Services ◽  
Virtual Care ◽  
Primary Care Services

BACKGROUND Primary care physicians across the world are grappling with adopting virtual services to provide appropriate patient care during the COVID-19 pandemic. As the crisis continues, it is imperative to recognize the wide-scale barriers and seek strategies to mitigate the challenges of rapid adoption to virtual care felt by patients and physicians alike. OBJECTIVE The purpose of this scoping review was to map the challenges, strategies, and lessons learned from high-income countries that can be mobilized to inform decision-makers on how to best implement virtual primary care services during and after the COVID-19 pandemic. Moreover, the findings of our scoping review identified the barriers and strategies within the Quadruple Aim components, which may prove to be an effective implementation strategy for virtual care adoption in primary care settings. METHODS The two concepts of virtual care and COVID-19 were searched in MEDLINE, EMBASE, and CINAHL on Aug 10, 2020, and Scopus was searched on Aug 15, 2020. The database searches returned 10,549 citations and an additional 766 citations were retrieved from searching the citations from the reference lists of articles that met all inclusion criteria. After deduplication, 6,580 unique citations remained. Following title and abstract screening, 1,260 full-text articles were reviewed, of which 49 articles were included for data extraction, and 38 articles met the eligibility criteria for inclusion in the review. RESULTS Seven factors were identified as major barriers to the implementation of virtual primary care. Of the 38 articles included in this scoping review, 20 (53%) articles focused on challenges to equitable access to care, specifically regarding the lack of access to internet, smartphones, and Internet bandwidth for rural, seniors, and underserved populations. The second most common factor discussed in the articles was the lack of funding for virtual care (n= 14; 37%), such as inadequate reimbursement policies for virtual care. Other factors included negative patient and clinician perceptions of virtual care (n=11; 29%), lack of appropriate regulatory policies (n= 10, 26%), inappropriate clinical workflows (n= 9, 24%), lack of virtual care infrastructure (n= 8; 21%), and lastly, a need for appropriate virtual care training and education for clinicians (n=5;13%). CONCLUSIONS This review identified several barriers and strategies to mitigate those barriers that address the challenges of virtual primary care implementation related to equity, regulatory policies, technology and infrastructure, education, clinician and patient experience, clinical workflows, and funding for virtual care. These strategies included providing equitable alternatives to access care for patients with limited technical literacy and English proficiency and altering clinical workflows to integrate virtual care services. As many countries enter potential subsequent waves of the COVID-19 pandemic, applying early lessons learned to mitigate implementation barriers can help with the transition to equitable and appropriate virtual primary care services.

Big data and artificial intelligence for health system sustainability: The case of Veneto Region

2021 ◽  
pp. 31-52
Author(s):  
Grazia Dicuonzo ◽  
Francesca Donofrio ◽  
Antonio Fusco ◽  
Vittorio Dell’Atti
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Healthcare System ◽  
Care Delivery ◽  
Research Question ◽  
Health Agency ◽  
Time Frame ◽  
Structured Interviews ◽  
Case Study Methodology ◽  
The Impact

This paper investigates the digitalization challenges facing the Italian healthcare system. The aim of the paper is to support healthcare organizations as they take advantage of the potential of big data and artificial intelligence (AI) to promote sustainable healthcare systems. Both the development of innovative processes in the management of health care activities and the introduction of healthcare forecasting systems are valuable resources for clinical and care activities and enable a more efficient use of inputs in essential-level care delivery. By examining an innovative project developed by the Regional Social Health Agency (ARSS) of Veneto, this study analyses the impact of big data and AI on the sustainability of a healthcare system. In order to answer the research question, we used a case study methodology. We conducted semi-structured interviews with key members of the organizational group involved in the case. The results show that the implementation of AI algorithms based on big data in healthcare both improves the interpretation and processing of data, and reduces the time frame necessary for clinical processes, having a positive effect on sustainability.

scholarly journals Complex Lifestyle and Psychological Intervention in Knee Osteoarthritis: Scoping Review of Randomized Controlled Trials

2021 ◽  
Vol 18 (23) ◽  
pp. 12757
Author(s):  
Bryan Yijia Tan ◽  
Tivona Thach ◽  
Yasmin Lynda Munro ◽  
Soren Thorgaard Skou ◽  
Julian Thumboo ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Scoping Review ◽  
Psychological Intervention ◽  
Care Delivery ◽  
Data Extraction ◽  
Psychosocial Interventions ◽  
Current Evidence ◽  
Future Research ◽  
Controlled Trials ◽  
Knee Oa

Knee osteoarthritis (OA) causes pain, disability and poor quality of life in the elderly. The primary aim was to identify and map out the current evidence for randomised controlled trials (RCTs) on complex lifestyle and psychosocial interventions for knee OA. The secondary aim was to outline different components of complex lifestyle and psychosocial interventions. Our scoping review searched five databases from 2000 to 2021 where complex lifestyle or psychosocial interventions for patients with knee OA were compared to other interventions. Screening and data extraction were performed by two review authors independently and discrepancies resolved through consensus and in parallel with a third reviewer. A total of 38 articles were selected: 9 studied the effectiveness of psychological interventions; 11 were on self-management and lifestyle interventions; 18 looked at multifaceted interventions. This review highlights the substantial variation in knee OA interventions and the overall lack of quality in the current literature. Potential areas of future research, including identifying prognostic social factors, stratified care models, transdisciplinary care delivery and technology augmented interventions, have been identified. Further high-quality RCTs utilizing process evaluations and economic evaluation in accordance with the MRC guidelines are critical for the development of evidence-based knee OA programs globally.

scholarly journals User involvement in developing community-based public health services: a scoping review of methods

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
K S Petersen ◽  
J F Pedersen ◽  
B Egilstroed ◽  
C Overgaard
Keyword(s):  
Public Health ◽  
Health Services ◽  
Scoping Review ◽  
User Involvement ◽  
Community Services ◽  
Cochrane Library ◽  
Public Health Services ◽  
Community Based ◽  
Comprehensive Overview ◽  
The Impact

Abstract Background User involvement in developing community-based public health services has been on the agenda for decades. User involvement refers to the variety of ways in which service users or public citizens participate in the development of health services: from proving information on their needs to actively being involved in decisions about future services. Former studies found that user involvement is meaningful to the people involved and could have a favorable impact on the quality of services. Thus, it is timely to systematically identify and provide a comprehensive overview of user involvement methods used in public health studies. The aim of this scoping review is to provide an overview of the current body of empirical research where user involvement methods have been used to develop community public health services and identify its possible impact on the individual as well as services. Methods A systematic scoping review of user involvement methods aiming to develop public health services followed Arksey and O'Malley, 2005 framework. Six databases: CINAHL, Cochrane Library, Embase, PsycINFO. PubMed, Scopus and ProQuest, were searched from October till November, 2019. Search terms were: user involvement, methods and health care with corresponding synonym. All hits were double screened. Results 6.044 studies were identified of which 38 studies lived up to the criteria. Preliminary findings from coding and synthesizing studies have identified a variety of user involvement Methods 19 of the studies used complex, multi-facetted packages of methods aiming to identify needs, prioritize and formulate recommendations for future services. 19 studies used different kinds of group meetings and some used certain techniques to facilitate the process. Many reported the impact, and 13 evaluated the methods. The impact of using the methods varied from impact on individual, group, or service/political level. Final results will be presented at the conference. Key messages Studies on user involvement methods in developing community public health services and its impact are sparse. User involvement is privotal in developing sustainable public health community services.

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