The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.
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ABSTRACT: Background
Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.Methods
We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.Results
Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.Conclusions
Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.
SUBMITTER: Okeibunor JC
PROVIDER: S-EPMC10352788 | biostudies-literature | 2023
REPOSITORIES: biostudies-literature
Publications
The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.
Okeibunor Joseph Chukwudi JC Jaca Anelisa A Iwu-Jaja Chinwe Juliana CJ Idemili-Aronu Ngozi N Ba Housseynou H Zantsi Zukiswa Pamela ZP Ndlambe Asiphe Mavis AM Mavundza Edison E Muneene Derrick D Wiysonge Charles Shey CS Makubalo Lindiwe L
Frontiers in public health 20230704
<h4>Background</h4>Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.<h4>Methods</h4>We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studi ...[more]
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Project description:ObjectiveTo examine the extent and nature of evidence on the use of the environmental scan (ES) in the health services delivery literature.DesignScoping review.MethodsThis scoping review followed the five-stage scoping review methodology outlined by Khalil et al. A Peer Review of Electronic Search Strategies was completed. Seven electronic databases and the grey literature were searched. Pairs of researchers independently performed two levels of screening and data extraction. Data were analysed using qualitative content and thematic analysis.ResultsNinety-six studies were included in the scoping review. Researchers conducted ESs for many purposes, the most common being to examine the current state of programmes, services or policies. Recommendations were informed by ESs in 20% of studies. Most common data collection methods were literature review (71%), key informant or semistructured interviews (46%) and surveys (35%). Over half (53%) of the studies used a combination of passive (looking at information eg, literature, policies, guidelines) and active (looking for information eg, surveys, interviews) approaches to data collection. Person sources of data (eg, healthcare stakeholders, community representatives) and non-person sources of data (eg, documents, electronic databases, the web) were drawn on to a similar extent. The thematic analysis of the definitions/descriptions yielded several themes including instrument of discovery, knowledge synthesis, forward-looking and decision making. Research gaps identified included absence of a standard definition, inconsistencies in terminology and lack of guiding frameworks in the health services delivery context.ConclusionESs were conducted to gather evidence and to help inform decision making on a range of policy and health services delivery issues across the continuum of care. Consistency in terminology, a consensus definition and more guidance on ES design may help provide structure for researchers and other stakeholders, and ultimately advance ES as a methodological approach. A working definition of ES in a health services delivery context is presented.
| S-EPMC8587593 | biostudies-literature
Project description:PurposeArtificial intelligence (AI) technology is being rapidly adopted into many different branches of medicine. Although research has started to highlight the impact of AI on health care, the focus on patient perspectives of AI is scarce. This scoping review aimed to explore the literature on adult patients' perspectives on the use of an array of AI technologies in the health care setting for design and deployment.MethodsThis scoping review followed Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR). To evaluate patient perspectives, we conducted a comprehensive literature search using eight interdisciplinary electronic databases, including grey literature. Articles published from 2015 to 2022 that focused on patient views regarding AI technology in health care were included. Thematic analysis was performed on the extracted articles.ResultsOf the 10,571 imported studies, 37 articles were included and extracted. From the 33 peer-reviewed and 4 grey literature articles, the following themes on AI emerged: (i) Patient attitudes, (ii) Influences on patient attitudes, (iii) Considerations for design, and (iv) Considerations for use.ConclusionsPatients are key stakeholders essential to the uptake of AI in health care. The findings indicate that patients' needs and expectations are not fully considered in the application of AI in health care. Therefore, there is a need for patient voices in the development of AI in health care.
| S-EPMC11000703 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood.ObjectiveThe aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible?MethodsA scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized.ResultsOf the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare.ConclusionsOur current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.
| S-EPMC9582911 | biostudies-literature
Project description:BackgroundThere is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health.ObjectiveThis study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care.MethodsWe searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review.ResultsWe identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients.ConclusionsThis scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.
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Project description:BackgroundArtificial intelligence (AI)-driven serious games have been used in health care to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state to plan on how to leverage them in the current and future health care needs.ObjectiveThis study aimed to explore the features of AI-driven serious games in health care as reported by previous research.MethodsWe conducted a scoping review to achieve the abovementioned objective. The most popular databases in the information technology and health fields (ie, MEDLINE, PsycInfo, Embase, CINAHL, IEEE Xplore, ACM Digital Library, and Google Scholar) were searched using keywords related to serious games and AI. Two reviewers independently performed the study selection process. Three reviewers independently extracted data from the included studies. A narrative approach was used for data synthesis.ResultsThe search process returned 1470 records. Of these 1470 records, 46 (31.29%) met all eligibility criteria. A total of 64 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games were used for rehabilitation in most of the studies. The most common genres of serious games were role-playing games, puzzle games, and platform games. Unity was the most prominent game engine used to develop serious games. PCs were the most common platform used to play serious games. The most common algorithm used in the included studies was support vector machine. The most common purposes of AI were the detection of disease and the evaluation of user performance. The size of the data set ranged from 36 to 795,600. The most common validation techniques used in the included studies were k-fold cross-validation and training-test split validation. Accuracy was the most commonly used metric for evaluating the performance of AI models.ConclusionsThe last decade witnessed an increase in the development of AI-driven serious games for health care purposes, targeting various health conditions, and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. Although the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions.
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Project description:BackgroundAs the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education.ObjectiveWith a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness.MethodsAfter the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career).ResultsOf the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective.ConclusionsThis review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.
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Project description:BackgroundPolicymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking.MethodsWe searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords "artificial intelligence" and "policymaking." We used Walt and Gilson's policy triangle framework for charting the data.ResultsThe results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts.ConclusionNumerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI's potential to foster the meaningful use of evidence-based policymaking.
| S-EPMC10722786 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals' personal or protected attributes.ObjectiveThis study aimed to describe the attempts, strategies, and methods used to mitigate bias in AI models within primary health care, to identify the diverse groups or protected attributes considered, and to evaluate the results of these approaches on both bias reduction and AI model performance.MethodsWe conducted a scoping review following Joanna Briggs Institute (JBI) guidelines, searching Medline (Ovid), CINAHL (EBSCO), PsycINFO (Ovid), and Web of Science databases for studies published between January 1, 2017, and November 15, 2022. Pairs of reviewers independently screened titles and abstracts, applied selection criteria, and performed full-text screening. Discrepancies regarding study inclusion were resolved by consensus. Following reporting standards for AI in health care, we extracted data on study objectives, model features, targeted diverse groups, mitigation strategies used, and results. Using the mixed methods appraisal tool, we appraised the quality of the studies.ResultsAfter removing 585 duplicates, we screened 1018 titles and abstracts. From the remaining 189 full-text articles, we included 17 studies. The most frequently investigated protected attributes were race (or ethnicity), examined in 12 of the 17 studies, and sex (often identified as gender), typically classified as "male versus female" in 10 of the studies. We categorized bias mitigation approaches into four clusters: (1) modifying existing AI models or datasets, (2) sourcing data from electronic health records, (3) developing tools with a "human-in-the-loop" approach, and (4) identifying ethical principles for informed decision-making. Algorithmic preprocessing methods, such as relabeling and reweighing data, along with natural language processing techniques that extract data from unstructured notes, showed the greatest potential for bias mitigation. Other methods aimed at enhancing model fairness included group recalibration and the application of the equalized odds metric. However, these approaches sometimes exacerbated prediction errors across groups or led to overall model miscalibrations.ConclusionsThe results suggest that biases toward diverse groups are more easily mitigated when data are open-sourced, multiple stakeholders are engaged, and during the algorithm's preprocessing stage. Further empirical studies that include a broader range of groups, such as Indigenous peoples in Canada, are needed to validate and expand upon these findings.Trial registrationOSF Registry osf.io/9ngz5/; https://osf.io/9ngz5/.International registered report identifier (irrid)RR2-10.2196/46684.
| S-EPMC11751650 | biostudies-literature
Project description:INTRODUCTION:The environmental scan has been described as an important tool to inform decision-making on policy, planning and programme development in the healthcare sector. Despite the wide adoption of environmental scans, there is no consensus on a working definition within the health services delivery context and methodological guidance on the design and implementation of this approach is lacking in the literature. The objectives of this study are to map the extent, range and nature of evidence that describe the definitions, characteristics, conceptualisations, theoretical underpinnings, study limitations and other features of the environmental scan in the health services delivery literature and to propose a working definition specific to this context. METHODS AND ANALYSIS:This protocol describes a scoping review based on the methodology outlined by Khalil and colleagues. A comprehensive search strategy was developed by experienced health science librarians in consultation with the research team. A Peer Review of Electronic Search Strategies (PRESS) was completed. Two reviewers will independently screen titles, abstracts and full-text articles and select studies meeting the inclusion criteria from seven electronic databases: Academic Search Premier, Canadian Business & Current Affairs (CBCA), CINAHL, ERIC, Embase, MEDLINE and PsycINFO. The grey literature and reference lists of included articles will also be searched. The data will be analysed and presented in tabular format, and will include a descriptive numerical summary as well as a qualitative thematic analysis. ETHICS AND DISSEMINATION:This protocol provides an audit trail for a scoping review that will advance understanding about the environmental scan and its application in the health services delivery context. The review will propose a working definition and will inform future research to explore the development of a conceptual framework in this context. Findings will be disseminated through a peer-reviewed journal and conference presentations. The scoping review does not require ethics approval.
| S-EPMC6731933 | biostudies-literature
Project description:BackgroundThe field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity.ObjectiveWe aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?"MethodsFollowing PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology.ResultsOf the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes.ConclusionsOur scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.
| S-EPMC9667381 | biostudies-literature