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

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          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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          Most cited references75

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

            Scoping reviews, a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. Although more scoping reviews are being done, their methodological and reporting quality need improvement. This document presents the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and explanation. The checklist was developed by a 24-member expert panel and 2 research leads following published guidance from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. The final checklist contains 20 essential reporting items and 2 optional items. The authors provide a rationale and an example of good reporting for each item. The intent of the PRISMA-ScR is to help readers (including researchers, publishers, commissioners, policymakers, health care providers, guideline developers, and patients or consumers) develop a greater understanding of relevant terminology, core concepts, and key items to report for scoping reviews.
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              Scoping studies: advancing the methodology

              Background Scoping studies are an increasingly popular approach to reviewing health research evidence. In 2005, Arksey and O'Malley published the first methodological framework for conducting scoping studies. While this framework provides an excellent foundation for scoping study methodology, further clarifying and enhancing this framework will help support the consistency with which authors undertake and report scoping studies and may encourage researchers and clinicians to engage in this process. Discussion We build upon our experiences conducting three scoping studies using the Arksey and O'Malley methodology to propose recommendations that clarify and enhance each stage of the framework. Recommendations include: clarifying and linking the purpose and research question (stage one); balancing feasibility with breadth and comprehensiveness of the scoping process (stage two); using an iterative team approach to selecting studies (stage three) and extracting data (stage four); incorporating a numerical summary and qualitative thematic analysis, reporting results, and considering the implications of study findings to policy, practice, or research (stage five); and incorporating consultation with stakeholders as a required knowledge translation component of scoping study methodology (stage six). Lastly, we propose additional considerations for scoping study methodology in order to support the advancement, application and relevance of scoping studies in health research. Summary Specific recommendations to clarify and enhance this methodology are outlined for each stage of the Arksey and O'Malley framework. Continued debate and development about scoping study methodology will help to maximize the usefulness and rigor of scoping study findings within healthcare research and practice.
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                Author and article information

                Contributors
                kathleenmurphy73@gmail.com
                e.diruggiero@utoronto.ca
                ross.upshur@gmail.com
                don.willison@utoronto.ca
                nehamalhotra1@outlook.com
                jia.cai@mail.utoronto.ca
                nakul.malhotra@icloud.com
                vincci.lui@utoronto.ca
                jennifer.gibson@utoronto.ca
                Journal
                BMC Med Ethics
                BMC Med Ethics
                BMC Medical Ethics
                BioMed Central (London )
                1472-6939
                15 February 2021
                15 February 2021
                2021
                : 22
                : 14
                Affiliations
                [1 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Joint Centre for Bioethics, Dalla Lana School of Public Health, , University of Toronto, ; 155 College Street, Suite 754, Toronto, ON M5T 1P8 Canada
                [2 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Office of Global Health Education and Training, Dalla Lana School of Public Health, , University of Toronto, ; 155 College Street, Room 408, Toronto, ON M5T 3M7 Canada
                [3 ]Division of Clinical Public Health, Dalla Lana School of Public Health, 155 College Street, Toronto, ON M5T 3M7 Canada
                [4 ]GRID grid.250674.2, ISNI 0000 0004 0626 6184, Bridgepoint Collaboratory for Research and Innovation, , Lunenfeld Tanenbaum Research Institute, Sinai Health System, ; 1 Bridgepoint Drive, Toronto, ON M4M 2B5 Canada
                [5 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public, Health Sciences Building, , Health University of Toronto, ; 155 College Street, Suite 425, Toronto, ON M5T 3M6 Canada
                [6 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Gerstein Science Information Centre, , University of Toronto, ; 9 King’s College Circle, Toronto, ON M7A 1A5 Canada
                Article
                577
                10.1186/s12910-021-00577-8
                7885243
                33588803
                66719a22-fc0e-4ebc-8c4a-db6969ed126d
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 30 April 2020
                : 20 January 2021
                Funding
                Funded by: Joint Centre for Bioethics Jus Innovation Fund
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Medicine
                artificial intelligence,ethics,health care,public and population health,global health
                Medicine
                artificial intelligence, ethics, health care, public and population health, global health

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