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      Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey

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          Abstract

          Background

          Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants’ level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions.

          Methods

          We developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently.

          Results

          Participants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 ± 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions.

          Conclusions

          Participants’ openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12911-021-01586-8.

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          Most cited references74

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          Sources of method bias in social science research and recommendations on how to control it.

          Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
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            Dermatologist-level classification of skin cancer with deep neural networks

            Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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              High-performance medicine: the convergence of human and artificial intelligence

              Eric Topol (2019)
              The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
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                Author and article information

                Contributors
                aantes@wustl.edu
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                20 July 2021
                20 July 2021
                2021
                : 21
                : 221
                Affiliations
                [1 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Bioethics Research Center, , Washington University School of Medicine in St. Louis, ; St. Louis, MO USA
                [2 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Pediatrics, Division of Hematology and Oncology, , Washington University School of Medicine in St. Louis, ; St. Louis, MO USA
                [3 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Division of Biostatistics, , Washington University School of Medicine in St. Louis, ; St. Louis, MO USA
                [4 ]GRID grid.262962.b, ISNI 0000 0004 1936 9342, Departments of Surgery and Health Care Ethics, Bander Center for Medical Business Ethics, , Saint Louis University, ; St. Louis, MO USA
                Article
                1586
                10.1186/s12911-021-01586-8
                8293482
                34284756
                710f21b1-df1b-44cb-99bb-1b9536fab7de
                © 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
                : 1 December 2020
                : 2 July 2021
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Bioinformatics & Computational biology
                artificial intelligence,machine learning,acceptance of healthcare,openness,benefits,concerns,perceptions,bioethics

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