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Bouhouita-Guermech S, Haidar H. Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context. Asian Bioeth Rev 2024; 16:315-344. [PMID: 39022380 PMCID: PMC11250714 DOI: 10.1007/s41649-024-00292-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 07/20/2024] Open
Abstract
The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.
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Affiliation(s)
| | - Hazar Haidar
- Ethics Programs, Department of Letters and Humanities, University of Quebec at Rimouski, Rimouski, Québec Canada
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Frost EK, Bosward R, Aquino YSJ, Braunack-Mayer A, Carter SM. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. Int J Med Inform 2024; 186:105417. [PMID: 38564959 DOI: 10.1016/j.ijmedinf.2024.105417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance. MATERIALS AND METHODS We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations. RESULTS Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI. DISCUSSION Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation. CONCLUSION This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.
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Affiliation(s)
- Emma Kellie Frost
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Rebecca Bosward
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Annette Braunack-Mayer
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
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Lin S, Ma Y, Jiang Y, Li W, Peng Y, Yu T, Xu Y, Zhu J, Lu L, Zou H. Service Quality and Residents' Preferences for Facilitated Self-Service Fundus Disease Screening: Cross-Sectional Study. J Med Internet Res 2024; 26:e45545. [PMID: 38630535 PMCID: PMC11063888 DOI: 10.2196/45545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 10/15/2023] [Accepted: 03/12/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Fundus photography is the most important examination in eye disease screening. A facilitated self-service eye screening pattern based on the fully automatic fundus camera was developed in 2022 in Shanghai, China; it may help solve the problem of insufficient human resources in primary health care institutions. However, the service quality and residents' preference for this new pattern are unclear. OBJECTIVE This study aimed to compare the service quality and residents' preferences between facilitated self-service eye screening and traditional manual screening and to explore the relationships between the screening service's quality and residents' preferences. METHODS We conducted a cross-sectional study in Shanghai, China. Residents who underwent facilitated self-service fundus disease screening at one of the screening sites were assigned to the exposure group; those who were screened with a traditional fundus camera operated by an optometrist at an adjacent site comprised the control group. The primary outcome was the screening service quality, including effectiveness (image quality and screening efficiency), physiological discomfort, safety, convenience, and trustworthiness. The secondary outcome was the participants' preferences. Differences in service quality and the participants' preferences between the 2 groups were compared using chi-square tests separately. Subgroup analyses for exploring the relationships between the screening service's quality and residents' preference were conducted using generalized logit models. RESULTS A total of 358 residents enrolled; among them, 176 (49.16%) were included in the exposure group and the remaining 182 (50.84%) in the control group. Residents' basic characteristics were balanced between the 2 groups. There was no significant difference in service quality between the 2 groups (image quality pass rate: P=.79; average screening time: P=.57; no physiological discomfort rate: P=.92; safety rate: P=.78; convenience rate: P=.95; trustworthiness rate: P=.20). However, the proportion of participants who were willing to use the same technology for their next screening was significantly lower in the exposure group than in the control group (P<.001). Subgroup analyses suggest that distrust in the facilitated self-service eye screening might increase the probability of refusal to undergo screening (P=.02). CONCLUSIONS This study confirms that the facilitated self-service fundus disease screening pattern could achieve good service quality. However, it was difficult to reverse residents' preferences for manual screening in a short period, especially when the original manual service was already excellent. Therefore, the digital transformation of health care must be cautious. We suggest that attention be paid to the residents' individual needs. More efficient man-machine collaboration and personalized health management solutions based on large language models are both needed.
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Affiliation(s)
- Senlin Lin
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yingyan Ma
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yanwei Jiang
- Shanghai Hongkou Center for Disease Control and Prevention, Shanghai, China
| | - Wenwen Li
- School of Management, Fudan University, Shanghai, China
| | - Yajun Peng
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Tao Yu
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yi Xu
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Jianfeng Zhu
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Lina Lu
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Haidong Zou
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Evans RP, Bryant LD, Russell G, Absolom K. Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review. Int J Med Inform 2024; 183:105342. [PMID: 38266426 DOI: 10.1016/j.ijmedinf.2024.105342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/08/2023] [Accepted: 01/14/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Increasing attention is being given to the analysis of large health datasets to derive new clinical decision support systems (CDSS). However, few data-driven CDSS are being adopted into clinical practice. Trust in these tools is believed to be fundamental for acceptance and uptake but to date little attention has been given to defining or evaluating trust in clinical settings. OBJECTIVES A scoping review was conducted to explore how and where acceptability and trustworthiness of data-driven CDSS have been assessed from the health professional's perspective. METHODS Medline, Embase, PsycInfo, Web of Science, Scopus, ACM Digital, IEEE Xplore and Google Scholar were searched in March 2022 using terms expanded from: "data-driven" AND "clinical decision support" AND "acceptability". Included studies focused on healthcare practitioner-facing data-driven CDSS, relating directly to clinical care. They included trust or a proxy as an outcome, or in the discussion. The preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) is followed in the reporting of this review. RESULTS 3291 papers were screened, with 85 primary research studies eligible for inclusion. Studies covered a diverse range of clinical specialisms and intended contexts, but hypothetical systems (24) outnumbered those in clinical use (18). Twenty-five studies measured trust, via a wide variety of quantitative, qualitative and mixed methods. A further 24 discussed themes of trust without it being explicitly evaluated, and from these, themes of transparency, explainability, and supporting evidence were identified as factors influencing healthcare practitioner trust in data-driven CDSS. CONCLUSION There is a growing body of research on data-driven CDSS, but few studies have explored stakeholder perceptions in depth, with limited focused research on trustworthiness. Further research on healthcare practitioner acceptance, including requirements for transparency and explainability, should inform clinical implementation.
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Affiliation(s)
- Ruth P Evans
- University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.
| | | | - Gregor Russell
- Bradford District Care Trust, Bradford, New Mill, Victoria Rd, BD18 3LD, UK.
| | - Kate Absolom
- University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.
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Hwang EJ, Jeong WG, David PM, Arentz M, Ruhwald M, Yoon SH. AI for Detection of Tuberculosis: Implications for Global Health. Radiol Artif Intell 2024; 6:e230327. [PMID: 38197795 PMCID: PMC10982823 DOI: 10.1148/ryai.230327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/03/2023] [Accepted: 12/18/2023] [Indexed: 01/11/2024]
Abstract
Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Eui Jin Hwang
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Won Gi Jeong
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Pierre-Marie David
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Matthew Arentz
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Morten Ruhwald
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Soon Ho Yoon
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
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Sageshima J, Than P, Goussous N, Mineyev N, Perez R. Prediction of High-Risk Donors for Kidney Discard and Nonrecovery Using Structured Donor Characteristics and Unstructured Donor Narratives. JAMA Surg 2024; 159:60-68. [PMID: 37910090 PMCID: PMC10620675 DOI: 10.1001/jamasurg.2023.4679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/27/2023] [Indexed: 11/03/2023]
Abstract
Importance Despite the unmet need, many deceased-donor kidneys are discarded or not recovered. Inefficient allocation and prolonged ischemia time are contributing factors, and early detection of high-risk donors may reduce organ loss. Objective To evaluate the feasibility of machine learning (ML) and natural language processing (NLP) classification of donors with kidneys that are used vs not used for organ transplant. Design, Setting, and Participants This retrospective cohort study used donor information (structured donor characteristics and unstructured donor narratives) from the United Network for Organ Sharing (UNOS). All donor offers to a single transplant center between January 2015 and December 2020 were used to train and validate ML models to predict donors who had at least 1 kidney transplanted (at our center or another center). The donor data from 2021 were used to test each model. Exposures Donor information was provided by UNOS to the transplant centers with potential transplant candidates. Each center evaluated the donor and decided within an allotted time whether to accept the kidney for organ transplant. Main Outcomes and Measures Outcome metrics of the test cohort included area under the receiver operating characteristic curve (AUROC), F1 score, accuracy, precision, and recall of each ML classifier. Feature importance and Shapley additive explanation (SHAP) summaries were assessed for model explainability. Results The training/validation cohort included 9555 donors (median [IQR] age, 50 [36-58] years; 5571 male [58.3%]), and the test cohort included 2481 donors (median [IQR] age, 52 [40-59] years; 1496 male [60.3%]). Only 20% to 30% of potential donors had at least 1 kidney transplanted. The ML model with a single variable (Kidney Donor Profile Index) showed an AUROC of 0.69, F1 score of 0.42, and accuracy of 0.64. Multivariable ML models based on basic a priori structured donor data showed similar metrics (logistic regression: AUROC = 0.70; F1 score = 0.42; accuracy = 0.62; random forest classifier: AUROC = 0.69; F1 score = 0.42; accuracy = 0.64). The classic NLP model (bag-of-words model) showed its best metrics (AUROC = 0.60; F1 score = 0.35; accuracy = 0.59) by the logistic regression classifier. The advanced Bidirectional Encoder Representations From Transformers model showed comparable metrics (AUROC = 0.62; F1 score = 0.39; accuracy = 0.69) only after appending basic donor information. Feature importance and SHAP detected the variables (and words) that affected the models most. Conclusions and Relevance Results of this cohort study suggest that models using ML can be applied to predict donors with high-risk kidneys not used for organ transplant, but the models still need further elaboration. The use of unstructured data is likely to expand the possibilities; further exploration of new approaches will be necessary to develop models with better predictive metrics.
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Affiliation(s)
| | - Peter Than
- Department of Surgery, University of California, Davis Health, Sacramento
| | - Naeem Goussous
- Department of Surgery, University of California, Davis Health, Sacramento
| | - Neal Mineyev
- Department of Surgery, University of California, Davis Health, Sacramento
| | - Richard Perez
- Department of Surgery, University of California, Davis Health, Sacramento
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Holm S, Ploug T. Population preferences for AI system features across eight different decision-making contexts. PLoS One 2023; 18:e0295277. [PMID: 38039320 PMCID: PMC10691677 DOI: 10.1371/journal.pone.0295277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023] Open
Abstract
Artificial intelligence systems based on deep learning architectures are being investigated as decision-support systems for human decision-makers across a wide range of decision-making contexts. It is known from the literature on AI in medicine that patients and the public hold relatively strong preferences in relation to desirable features of AI systems and their implementation, e.g. in relation to explainability and accuracy, and in relation to the role of the human decision-maker in the decision chain. The features that are preferred can be seen as 'protective' of the patient's interests. These types of preferences may plausibly vary across decision-making contexts, but the research on this question has so far been almost exclusively performed in relation to medical AI. In this cross-sectional survey study we investigate the preferences of the adult Danish population for five specific protective features of AI systems and implementation across a range of eight different use cases in the public and commercial sectors ranging from medical diagnostics to the issuance of parking tickets. We find that all five features are seen as important across all eight contexts, but that they are deemed to be slightly less important when the implications of the decision made are less significant to the respondents.
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Affiliation(s)
- Søren Holm
- Centre for Social Ethics and Policy, School of Law, University of Manchester, Manchester, United Kingdom
- Faculty of Medicine, Centre for Medical Ethics, HELSAM, University of Oslo, Oslo, Norway
| | - Thomas Ploug
- Department of Communication and Psychology, Centre of Applied Ethics and Philosophy of Science, Aalborg University, Copenhagen, Denmark
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Aziz D, Maganti K, Yanamala N, Sengupta P. The Role of Artificial Intelligence in Echocardiography: A Clinical Update. Curr Cardiol Rep 2023; 25:1897-1907. [PMID: 38091196 DOI: 10.1007/s11886-023-02005-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/21/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE OF REVIEW In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action. RECENT FINDINGS Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems. AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.
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Affiliation(s)
- Daniel Aziz
- Department of Internal Medicine, Rutgers - Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Kameswari Maganti
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Naveena Yanamala
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Partho Sengupta
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA.
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Gould DJ, Dowsey MM, Glanville-Hearst M, Spelman T, Bailey JA, Choong PFM, Bunzli S. Patients' Views on AI for Risk Prediction in Shared Decision-Making for Knee Replacement Surgery: Qualitative Interview Study. J Med Internet Res 2023; 25:e43632. [PMID: 37721797 PMCID: PMC10546266 DOI: 10.2196/43632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 05/04/2023] [Accepted: 08/21/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND The use of artificial intelligence (AI) in decision-making around knee replacement surgery is increasing, and this technology holds promise to improve the prediction of patient outcomes. Ambiguity surrounds the definition of AI, and there are mixed views on its application in clinical settings. OBJECTIVE In this study, we aimed to explore the understanding and attitudes of patients who underwent knee replacement surgery regarding AI in the context of risk prediction for shared clinical decision-making. METHODS This qualitative study involved patients who underwent knee replacement surgery at a tertiary referral center for joint replacement surgery. The participants were selected based on their age and sex. Semistructured interviews explored the participants' understanding of AI and their opinions on its use in shared clinical decision-making. Data collection and reflexive thematic analyses were conducted concurrently. Recruitment continued until thematic saturation was achieved. RESULTS Thematic saturation was achieved with 19 interviews and confirmed with 1 additional interview, resulting in 20 participants being interviewed (female participants: n=11, 55%; male participants: n=9, 45%; median age: 66 years). A total of 11 (55%) participants had a substantial postoperative complication. Three themes captured the participants' understanding of AI and their perceptions of its use in shared clinical decision-making. The theme Expectations captured the participants' views of themselves as individuals with the right to self-determination as they sought therapeutic solutions tailored to their circumstances, needs, and desires, including whether to use AI at all. The theme Empowerment highlighted the potential of AI to enable patients to develop realistic expectations and equip them with personalized risk information to discuss in shared decision-making conversations with the surgeon. The theme Partnership captured the importance of symbiosis between AI and clinicians because AI has varied levels of interpretability and understanding of human emotions and empathy. CONCLUSIONS Patients who underwent knee replacement surgery in this study had varied levels of familiarity with AI and diverse conceptualizations of its definitions and capabilities. Educating patients about AI through nontechnical explanations and illustrative scenarios could help inform their decision to use it for risk prediction in the shared decision-making process with their surgeon. These findings could be used in the process of developing a questionnaire to ascertain the views of patients undergoing knee replacement surgery on the acceptability of AI in shared clinical decision-making. Future work could investigate the accuracy of this patient group's understanding of AI, beyond their familiarity with it, and how this influences their acceptance of its use. Surgeons may play a key role in finding a place for AI in the clinical setting as the uptake of this technology in health care continues to grow.
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Affiliation(s)
- Daniel J Gould
- St Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Michelle M Dowsey
- St Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia
- Department of Orthopaedics, St Vincent's Hospital Melbourne, Melbourne, Australia
| | | | - Tim Spelman
- St Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - James A Bailey
- School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Peter F M Choong
- St Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia
- Department of Orthopaedics, St Vincent's Hospital Melbourne, Melbourne, Australia
| | - Samantha Bunzli
- School of Health Sciences and Social Work, Griffith University, Brisbane, Australia
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Wang H, Wu W, Dou Z, He L, Yang L. Performance and exploration of ChatGPT in medical examination, records and education in Chinese: Pave the way for medical AI. Int J Med Inform 2023; 177:105173. [PMID: 37549499 DOI: 10.1016/j.ijmedinf.2023.105173] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/01/2023] [Accepted: 07/08/2023] [Indexed: 08/09/2023]
Abstract
BACKGROUND Although chat generative pre-trained transformer (ChatGPT) has made several successful attempts in the medical field, most notably in answering medical questions in English, no studies have evaluated ChatGPT's performance in a Chinese context for a medical task. OBJECTIVE The aim of this study was to evaluate ChatGPT's ability to understand medical knowledge in Chinese, as well as its potential to serve as an electronic health infrastructure for medical development, by evaluating its performance in medical examinations, records, and education. METHOD The Chinese (CNMLE) and English (ENMLE) datasets of the China National Medical Licensing Examination and the Chinese dataset (NEEPM) of the China National Entrance Examination for Postgraduate Clinical Medicine Comprehensive Ability were used to evaluate the performance of ChatGPT (GPT-3.5 and GPT-4). We assessed answer accuracy, verbal fluency, and the classification of incorrect responses owing to hallucinations on multiple occasions. In addition, we tested ChatGPT's performance on discharge summaries and group learning in a Chinese context on a small scale. RESULTS The accuracy of GPT-3.5 in CNMLE, ENMLE, and NEEPM was 56% (56/100), 76% (76/100), and 62% (62/100), respectively, compared to that of GPT-4, which was of 84% (84/100), 86% (86/100), and 82% (82/100). The verbal fluency of all the ChatGPT responses exceeded 95%. Among the GPT-3.5 incorrect responses, the proportions of open-domain hallucinations were 66 % (29/44), 54 % (14/24), and 63 % (24/38), whereas close-domain hallucinations accounted for 34 % (15/44), 46 % (14/24), and 37 % (14/38), respectively. By contrast, GPT-4 open-domain hallucinations accounted for 56% (9/16), 43% (6/14), and 83% (15/18), while close-domain hallucinations accounted for 44% (7/16), 57% (8/14), and 17% (3/18), respectively. In the discharge summary, ChatGPT demonstrated logical coherence, however GPT-3.5 could not fulfill the quality requirements, while GPT-4 met the qualification of 60% (6/10). In group learning, the verbal fluency and interaction satisfaction with ChatGPT were 100% (10/10). CONCLUSION ChatGPT based on GPT-4 is at par with Chinese medical practitioners who passed the CNMLE and at the standard required for admission to clinical medical graduate programs in China. The GPT-4 shows promising potential for discharge summarization and group learning. Additionally, it shows high verbal fluency, resulting in a positive human-computer interaction experience. GPT-4 significantly improves multiple capabilities and reduces hallucinations compared to the previous GPT-3.5 model, with a particular leap forward in the Chinese comprehension capability of medical tasks. Artificial intelligence (AI) systems face the challenges of hallucinations, legal risks, and ethical issues. However, we discovered ChatGPT's potential to promote medical development as an electronic health infrastructure, paving the way for Medical AI to become necessary.
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Affiliation(s)
- Hongyan Wang
- Department of Pain Management, Xuanwu Hospital, Capital Medical University
| | - WeiZhen Wu
- Department of Anesthesia, China-Japan Union Hospital of Jilin University
| | - Zhi Dou
- Department of Pain Management, Xuanwu Hospital, Capital Medical University
| | - Liangliang He
- Department of Pain Management, Xuanwu Hospital, Capital Medical University
| | - Liqiang Yang
- Department of Pain Management, Xuanwu Hospital, Capital Medical University.
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11
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Borondy Kitts A. Patient Perspectives on Artificial Intelligence in Radiology. J Am Coll Radiol 2023; 20:863-867. [PMID: 37453601 DOI: 10.1016/j.jacr.2023.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 07/18/2023]
Abstract
There are two major areas for patient engagement in radiology artificial intelligence (AI). One is in the sharing of data for AI development; the second is the use of AI in patient care. In general, individuals support sharing deidentified data if used for the common good, to help others with similar health conditions, or for research. However, there is concern with risk to privacy including reidentification and use for other than intended purposes. Lack of trust is mentioned as a barrier for data sharing. Individuals want to be involved in the data-sharing process. In the use of AI in medical care, patients generally support AI as an assist to the radiologist but lack trust in unsupervised AI. Patients worry about liability in case of bad outcomes. Patients are concerned about loss of the human connection and the loss of empathy during a vulnerable time in their lives. Patients expressed concern about risk of discrimination due to bias in AI algorithms. Building trust in AI requires transparency, explainability, security, and privacy protection. Radiologists can take action to prepare their patients to become more trusting of AI. Developing and implementing data-sharing agreements allows patients to voluntarily help in the algorithm development process. Developing AI disclosure guidelines and having AI use disclosure discussions with patients will help them understand the use of AI in their care. As the use of AI increases, there is an opportunity for radiologists to develop and maintain close relationships with their patients and to become more involved in their care.
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Herbert P, Hou K, Bradley C, Hager G, Boland MV, Ramulu P, Unberath M, Yohannan J. Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data. Ophthalmol Glaucoma 2023; 6:466-473. [PMID: 36944385 PMCID: PMC10509314 DOI: 10.1016/j.ogla.2023.03.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/20/2023] [Accepted: 03/10/2023] [Indexed: 03/22/2023]
Abstract
PURPOSE To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. DESIGN A retrospective cohort study. SUBJECTS In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs). METHODS We included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF1, VF2, VF3, VF4, and VF5); and (3) had 1 reliable baseline OCT scan (OCT1) and 1 set of baseline clinical measurements (clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict the eye's risk of rapid VF worsening across the 5 VFs. We compared the performance of models with differing inputs by computing area under the curve (AUC) in the test set. Specifically, we trained models with the following inputs: (1) model V: VF1; (2) VC: VF1+ Clinical1; (3) VO: VF1+ OCT1; (4) VOC: VF1+ Clinical1+ OCT1; (5) V2: VF1 + VF2; (6) V2OC: VF1 + VF2 + Clinical1 + OCT1; (7) V3: VF1 + VF2 + VF3; and (8) V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1. MAIN OUTCOME MEASURES The AUC of DLMs when forecasting rapidly worsening eyes. RESULTS Model V3OC best forecasted rapid worsening with an AUC (95% confidence interval [CI]) of 0.87 (0.77-0.97). Remaining models in descending order of performance and their respective AUC (95% CI) were as follows: (1) model V3 (0.84 [0.74-0.95]), (2) model V2OC (0.81 [0.70-0.92]), (3) model V2 (0.81 [0.70-0.82]), (4) model VOC (0.77 [0.65-0.88]), (5) model VO (0.75 [0.64-0.88]), (6) model VC (0.75 [0.63-0.87]), and (7) model V (0.74 [0.62-0.86]). CONCLUSIONS Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Patrick Herbert
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Kaihua Hou
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland
| | - Greg Hager
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Michael V Boland
- Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland
| | - Mathias Unberath
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Jithin Yohannan
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland; Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland.
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13
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Hong S, Hwang EJ, Kim S, Song J, Lee T, Jo GD, Choi Y, Park CM, Goo JM. Methods of Visualizing the Results of an Artificial-Intelligence-Based Computer-Aided Detection System for Chest Radiographs: Effect on the Diagnostic Performance of Radiologists. Diagnostics (Basel) 2023; 13:diagnostics13061089. [PMID: 36980397 PMCID: PMC10046978 DOI: 10.3390/diagnostics13061089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/02/2023] [Accepted: 03/12/2023] [Indexed: 03/16/2023] Open
Abstract
It is unclear whether the visualization methods for artificial-intelligence-based computer-aided detection (AI-CAD) of chest radiographs influence the accuracy of readers’ interpretation. We aimed to evaluate the accuracy of radiologists’ interpretations of chest radiographs using different visualization methods for the same AI-CAD. Initial chest radiographs of patients with acute respiratory symptoms were retrospectively collected. A commercialized AI-CAD using three different methods of visualizing was applied: (a) closed-line method, (b) heat map method, and (c) combined method. A reader test was conducted with five trainee radiologists over three interpretation sessions. In each session, the chest radiographs were interpreted using AI-CAD with one of the three visualization methods in random order. Examination-level sensitivity and accuracy, and lesion-level detection rates for clinically significant abnormalities were evaluated for the three visualization methods. The sensitivity (p = 0.007) and accuracy (p = 0.037) of the combined method are significantly higher than that of the closed-line method. Detection rates using the heat map method (p = 0.043) and the combined method (p = 0.004) are significantly higher than those using the closed-line method. The methods for visualizing AI-CAD results for chest radiographs influenced the performance of radiologists’ interpretations. Combining the closed-line and heat map methods for visualizing AI-CAD results led to the highest sensitivity and accuracy of radiologists.
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Affiliation(s)
- Sungho Hong
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
| | - Eui Jin Hwang
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul 03082, Republic of Korea
- Correspondence: ; Tel.: +82-2-2072-2057
| | - Soojin Kim
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
| | - Jiyoung Song
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
| | - Taehee Lee
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
| | - Gyeong Deok Jo
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
| | - Yelim Choi
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul 03082, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 03082, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, Seoul 03082, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul 03082, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 03082, Republic of Korea
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14
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Calvas P. Chapitre 7. Un regard de généticien. JOURNAL INTERNATIONAL DE BIOETHIQUE ET D'ETHIQUE DES SCIENCES 2023; 34:111-120. [PMID: 37684198 DOI: 10.3917/jibes.342.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Examined through the eyes of the geneticist, the modifications of the bioethics law seem relatively modest with regard to the supervision of the practices of his discipline. The introduction of rules concerning the use of algorithms in medical practice is the truly new point. It seemed beneficial to take into account “the interference of thinking machines” in medical decision-making and to initiate the outlines of a framework. We will debate the proposals and terms. Precisions made to the obligation to inform relatives of the existence of a genetic anomaly are defined around the concept of solidarity. Without neglecting this latter, we will recall other determinants, the complexity and the issues underlying the delivery of predictive genetic information as well as the risks that informed persons may incur. It seems appropriate to also consider the ethical tensions that can impose themselves on the physicians involved in the mandatory process of information..
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15
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Tang L, Li J, Fantus S. Medical artificial intelligence ethics: A systematic review of empirical studies. Digit Health 2023; 9:20552076231186064. [PMID: 37434728 PMCID: PMC10331228 DOI: 10.1177/20552076231186064] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 06/16/2023] [Indexed: 07/13/2023] Open
Abstract
Background Artificial intelligence (AI) technologies are transforming medicine and healthcare. Scholars and practitioners have debated the philosophical, ethical, legal, and regulatory implications of medical AI, and empirical research on stakeholders' knowledge, attitude, and practices has started to emerge. This study is a systematic review of published empirical studies of medical AI ethics with the goal of mapping the main approaches, findings, and limitations of scholarship to inform future practice considerations. Methods We searched seven databases for published peer-reviewed empirical studies on medical AI ethics and evaluated them in terms of types of technologies studied, geographic locations, stakeholders involved, research methods used, ethical principles studied, and major findings. Findings Thirty-six studies were included (published 2013-2022). They typically belonged to one of the three topics: exploratory studies of stakeholder knowledge and attitude toward medical AI, theory-building studies testing hypotheses regarding factors contributing to stakeholders' acceptance of medical AI, and studies identifying and correcting bias in medical AI. Interpretation There is a disconnect between high-level ethical principles and guidelines developed by ethicists and empirical research on the topic and a need to embed ethicists in tandem with AI developers, clinicians, patients, and scholars of innovation and technology adoption in studying medical AI ethics.
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Affiliation(s)
- Lu Tang
- Department of Communication and Journalism, Texas A&M University, College Station, TX, USA
| | - Jinxu Li
- Department of Communication and Journalism, Texas A&M University, College Station, TX, USA
| | - Sophia Fantus
- School of Social Work, University of Texas at Arlington, Arlington, TX, USA
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16
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Wellnhofer E. Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging. Front Cardiovasc Med 2022; 9:890809. [PMID: 35935648 PMCID: PMC9354141 DOI: 10.3389/fcvm.2022.890809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 06/13/2022] [Indexed: 12/02/2022] Open
Abstract
Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and essential insights from the vast amount of data generated during health care delivery every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications and a lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must participate in the quest to find a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and data control must account for bias, error, inappropriate use, drifts, and shifts, particularly in real-world data. Regulators worldwide are tackling twenty-first century challenges raised by “learning” medical devices. Ethical concerns and regulatory approaches are presented. The paper concludes with a discussion on the future of responsible artificial intelligence.
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Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit Health 2022; 8:20552076221116772. [PMID: 35983102 PMCID: PMC9380417 DOI: 10.1177/20552076221116772] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/13/2022] [Indexed: 12/23/2022] Open
Abstract
Objective The attitudes about the usage of artificial intelligence in healthcare are
controversial. Unlike the perception of healthcare professionals, the
attitudes of patients and their companions have been of less interest so
far. In this study, we aimed to investigate the perception of artificial
intelligence in healthcare among this highly relevant group along with the
influence of digital affinity and sociodemographic factors. Methods We conducted a cross-sectional study using a paper-based questionnaire with
patients and their companions at a German tertiary referral hospital from
December 2019 to February 2020. The questionnaire consisted of three
sections examining (a) the respondents’ technical affinity, (b) their
perception of different aspects of artificial intelligence in healthcare and
(c) sociodemographic characteristics. Results From a total of 452 participants, more than 90% already read or heard about
artificial intelligence, but only 24% reported good or expert knowledge.
Asked on their general perception, 53.18% of the respondents rated the use
of artificial intelligence in medicine as positive or very positive, but
only 4.77% negative or very negative. The respondents denied concerns about
artificial intelligence, but strongly agreed that artificial intelligence
must be controlled by a physician. Older patients, women, persons with lower
education and technical affinity were more cautious on the
healthcare-related artificial intelligence usage. Conclusions German patients and their companions are open towards the usage of artificial
intelligence in healthcare. Although showing only a mediocre knowledge about
artificial intelligence, a majority rated artificial intelligence in
healthcare as positive. Particularly, patients insist that a physician
supervises the artificial intelligence and keeps ultimate responsibility for
diagnosis and therapy.
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Affiliation(s)
- Sebastian J Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich, Germany
| | - Andrea Blankenheim
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
| | - Alina Wahl
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
| | - Petra Hetfeld
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Oliver Maassen
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Julian Kunze
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Department of Anesthesiology, University Hospital RWTH Aachen, Germany
| | - Rolf Rossaint
- Department of Anesthesiology, University Hospital RWTH Aachen, Germany
| | - Morris Riedel
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich, Germany
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
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