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Baghdadi LR, Mobeirek AA, Alhudaithi DR, Albenmousa FA, Alhadlaq LS, Alaql MS, Alhamlan SA. Patients' Attitudes Toward the Use of Artificial Intelligence as a Diagnostic Tool in Radiology in Saudi Arabia: Cross-Sectional Study. JMIR Hum Factors 2024; 11:e53108. [PMID: 39110973 PMCID: PMC11339559 DOI: 10.2196/53108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 03/15/2024] [Accepted: 06/22/2024] [Indexed: 08/25/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to mitigate delays in diagnosis, which could, in turn, impact patients' prognosis and treatment outcomes. The literature shows conflicting results regarding patients' attitudes to AI as a diagnostic tool. To the best of our knowledge, no similar study has been conducted in Saudi Arabia. OBJECTIVE The objectives of this study are to examine patients' attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia. Additionally, we sought to explore potential associations between patients' attitudes and various sociodemographic factors. METHODS This descriptive-analytical cross-sectional study was conducted in a tertiary care hospital. Data were collected from patients scheduled for radiological imaging through a validated self-administered questionnaire. The main outcome was to measure patients' attitudes to the use of AI in radiology by calculating mean scores of 5 factors, distrust and accountability (factor 1), procedural knowledge (factor 2), personal interaction and communication (factor 3), efficiency (factor 4), and methods of providing information to patients (factor 5). Data were analyzed using the student t test, one-way analysis of variance followed by post hoc and multivariable analysis. RESULTS A total of 382 participants (n=273, 71.5% women and n=109, 28.5% men) completed the surveys and were included in the analysis. The mean age of the respondents was 39.51 (SD 13.26) years. Participants favored physicians over AI for procedural knowledge, personal interaction, and being informed. However, the participants demonstrated a neutral attitude for distrust and accountability and for efficiency. Marital status was found to be associated with distrust and accountability, procedural knowledge, and personal interaction. Associations were also found between self-reported health status and being informed and between the field of specialization and distrust and accountability. CONCLUSIONS Patients were keen to understand the work of AI in radiology but favored personal interaction with a radiologist. Patients were impartial toward AI replacing radiologists and the efficiency of AI, which should be a consideration in future policy development and integration. Future research involving multicenter studies in different regions of Saudi Arabia is required.
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Affiliation(s)
- Leena R Baghdadi
- Department of Family and Community Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Arwa A Mobeirek
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | | | | | - Leen S Alhadlaq
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Maisa S Alaql
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
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Cè M, Ibba S, Cellina M, Tancredi C, Fantesini A, Fazzini D, Fortunati A, Perazzo C, Presta R, Montanari R, Forzenigo L, Carrafiello G, Papa S, Alì M. Radiologists' perceptions on AI integration: An in-depth survey study. Eur J Radiol 2024; 177:111590. [PMID: 38959557 DOI: 10.1016/j.ejrad.2024.111590] [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: 03/15/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE To assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice. METHODS A survey was conducted among members of the SIRM Lombardy. Radiologists' attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists' attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns. RESULTS The survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists' opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI's significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI's relevance to future radiologists' career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential. CONCLUSION Our results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI's potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
| | - Simona Ibba
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.
| | - Chiara Tancredi
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | | | - Deborah Fazzini
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Alice Fortunati
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Chiara Perazzo
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Roberta Presta
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Roberto Montanari
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Laura Forzenigo
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Marco Alì
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy; Bracco Imaging SpA, Via Caduti di Marcinelle, 20134 Milan, Italy.
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Albinsaad LS, Alkhawajah AA, Abuageelah BM, Alkhalaf RA, Alfaifi MH, Oberi IA, Alnajjad AI, Albalawi IA, Alessa MY, Khan A. The Saudi Community View of the Use of Artificial Intelligence in Health Care. Ann Afr Med 2024; 23:343-351. [PMID: 39034557 DOI: 10.4103/aam.aam_192_23] [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: 11/13/2023] [Accepted: 01/05/2024] [Indexed: 07/23/2024] Open
Abstract
OBJECTIVES Artificial intelligence (AI) holds the promise to revolutionize the field of medicine and enhance the well-being of countless patients. Its capabilities span various areas, including disease prevention, accurate diagnosis, and the development of innovative treatments. Moreover, AI has the potential to streamline health-care delivery and lower expenses. The community should be aware of the potential applications of AI in health care, so that they can advocate for its development and adoption. Hence, the objective of this study is to assess the community's perspectives regarding the utilization of AI in health care. METHODS A cross-sectional, questionnaire-based study was conducted in Saudi Arabia during the period of June to October 2023. The questionnaire was distributed to people on various social media platforms using a convenience sampling method. The collected data were analyzed using Statistical Package for the Social Sciences. RESULTS The study included 771 individuals, with 42.5% having a positive outlook on the use of AI in health care, 31.8% having a neutral view, and 7.5% having a negative view. The only factor associated with a positive opinion was regional differences (P = 0.006). Moreover, participants who used medical apps or programs (P = 0.026), wearables (P = 0.027), felt more confident in using technology (P < 0.001), enjoyed using technology (P < 0.001), found it easier to familiarize themselves with new devices or programs (P < 0.001), and had more knowledge about AI (P < 0.001) had more positive opinions regarding the use of AI in health care. CONCLUSIONS The study found that most Saudis, especially those who were familiar with the use of technology, support the use of AI in health care, with a positive or neutral view. Yet, targeted campaigns in certain regions are needed to educate the entire community about AI's potential benefits.
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Affiliation(s)
- Loai S Albinsaad
- Department of Surgery, College of Medicine, King Faisal University, Al Ahsa, SAU
| | | | | | | | - Mona H Alfaifi
- Department of Medicine and Surgery, Batterjee Medical College, Aseer, SAU
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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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Godoy Junior CA, Miele F, Mäkitie L, Fiorenzato E, Koivu M, Bakker LJ, Groot CUD, Redekop WK, van Deen WK. Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson's Disease Management: Perspectives of Patients and Neurologists. THE PATIENT 2024; 17:275-285. [PMID: 38182935 DOI: 10.1007/s40271-023-00669-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/10/2023] [Indexed: 01/07/2024]
Abstract
OBJECTIVE Early detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understanding how end users view these technologies. This research explores patient and neurologist perspectives on AI-assisted RMS. METHODS Qualitative interviews and focus-groups were conducted with 27 persons with PD (PwPD) and six neurologists from Finland and Italy. The discussions covered traditional disease progression detection and the prospects of integrating AI and RMS. Sessions were recorded, transcribed, and underwent thematic analysis. RESULTS The study involved five individual interviews (four Italian participants and one Finnish) and six focus-groups (four Finnish and two Italian) with PwPD. Additionally, six neurologists (three from each country) were interviewed. Both cohorts voiced frustration with current monitoring methods due to their limited real-time detection capabilities. However, there was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond. While some PwPD had privacy and trust concerns, the anticipated advantages in symptom regulation seemed to outweigh these apprehensions. DISCUSSION The study reveals a willingness among PwPD and neurologists to integrate RMS and AI into PD management. Widespread adoption requires these technologies to provide tangible clinical benefits, remain user-friendly, and uphold trust within the physician-patient relationship. CONCLUSION This study offers insights into the potential drivers and barriers for adopting AI-assisted RMS in PD care. Recognizing these factors is pivotal for the successful integration of these digital health tools in PD management.
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Affiliation(s)
- Carlos Antonio Godoy Junior
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands.
| | - Francesco Miele
- Department of Political and Social Sciences, University of Trieste, Trieste, Italy
| | - Laura Mäkitie
- Department of Neurology, Brain Center, Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | | | - Maija Koivu
- Department of Neurology, Brain Center, Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | - Lytske Jantien Bakker
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - Carin Uyl-de Groot
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - William Ken Redekop
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - Welmoed Kirsten van Deen
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
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Moy S, Irannejad M, Manning SJ, Farahani M, Ahmed Y, Gao E, Prabhune R, Lorenz S, Mirza R, Klinger C. Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review. J Patient Cent Res Rev 2024; 11:51-62. [PMID: 38596349 PMCID: PMC11000703 DOI: 10.17294/2330-0698.2029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Abstract
Purpose Artificial 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. Methods This 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. Results Of 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. Conclusions Patients 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.
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Affiliation(s)
- Sally Moy
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Mona Irannejad
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | | | - Mehrdad Farahani
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Yomna Ahmed
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Ellis Gao
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Radhika Prabhune
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Suzan Lorenz
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Raza Mirza
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Christopher Klinger
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- National Initiative for the Care of the Elderly, Toronto, Canada
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Lepri G, Oddi F, Gulino RA, Giansanti D. Beyond the Clinic Walls: Examining Radiology Technicians' Experiences in Home-Based Radiography. Healthcare (Basel) 2024; 12:732. [PMID: 38610154 PMCID: PMC11011261 DOI: 10.3390/healthcare12070732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
In recent years, the landscape of diagnostic imaging has undergone a significant transformation with the emergence of home radiology, challenging the traditional paradigm. This shift, bringing diagnostic imaging directly to patients, has gained momentum and has been further accelerated by the global COVID-19 pandemic, highlighting the increasing importance and convenience of decentralized healthcare services. This study aims to offer a nuanced understanding of the attitudes and experiences influencing the integration of in-home radiography into contemporary healthcare practices. The research methodology involves a survey administered through Computer-Aided Web Interviewing (CAWI) tools, enabling real-time engagement with a diverse cohort of medical radiology technicians in the health domain. A second CAWI tool is submitted to experts to assess their feedback on the methodology. The survey explores key themes, including perceived advantages and challenges associated with domiciliary imaging, its impact on patient care, and the technological intricacies specific to conducting radiologic procedures outside the conventional clinical environment. Findings from a sample of 26 medical radiology technicians (drawn from a larger pool of 186 respondents) highlight a spectrum of opinions and constructive feedback. Enthusiasm is evident for the potential of domiciliary imaging to enhance patient convenience and provide a more patient-centric approach to healthcare. Simultaneously, this study suggests areas of intervention to improve the diffusion of home-based radiology. The methodology based on CAWI tools proves instrumental in the efficiency and depth of data collection, as evaluated by 16 experts from diverse professional backgrounds. The dynamic and responsive nature of this approach allows for a more allocated exploration of technicians' opinions, contributing to a comprehensive understanding of the evolving landscape of medical imaging services. Emphasis is placed on the need for national and international initiatives in the field, supported by scientific societies, to further explore the evolving landscape of teleradiology and the integration of artificial intelligence in radiology. This study encourages expansion involving other key figures in this practice, including, naturally, medical radiologists, general practitioners, medical physicists, and other stakeholders.
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Affiliation(s)
- Graziano Lepri
- Azienda Unità Sanitaria Locale Umbria 1, Via Guerriero Guerra 21, 06127 Perugia, Italy;
| | - Francesco Oddi
- Facoltà di Ingegneria, Università di Tor Vergata, Via del Politecnico, 1, 00133 Rome, Italy; (F.O.); (R.A.G.)
| | - Rosario Alfio Gulino
- Facoltà di Ingegneria, Università di Tor Vergata, Via del Politecnico, 1, 00133 Rome, Italy; (F.O.); (R.A.G.)
| | - Daniele Giansanti
- Centro Nazionale TISP, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
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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: 2] [Impact Index Per Article: 2.0] [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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Sheth S, Baker HP, Prescher H, Strelzow JA. Ethical Considerations of Artificial Intelligence in Health Care: Examining the Role of Generative Pretrained Transformer-4. J Am Acad Orthop Surg 2024; 32:205-210. [PMID: 38175996 DOI: 10.5435/jaaos-d-23-00787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/26/2023] [Indexed: 01/06/2024] Open
Abstract
The integration of artificial intelligence technologies, such as large language models (LLMs), in health care holds potential for improved efficiency and decision support. However, ethical concerns must be addressed before widespread adoption. This article focuses on the ethical principles surrounding the use of Generative Pretrained Transformer-4 and its conversational model, ChatGPT, in healthcare settings. One concern is potential inaccuracies in generated content. LLMs can produce believable yet incorrect information, risking errors in medical records. Opacity of training data exacerbates this, hindering accuracy assessment. To mitigate, LLMs should train on precise, validated medical data sets. Model bias is another critical concern because LLMs may perpetuate biases from their training, leading to medically inaccurate and discriminatory responses. Sampling, programming, and compliance biases contribute necessitating careful consideration to avoid perpetuating harmful stereotypes. Privacy is paramount in health care, using public LLMs raises risks. Strict data-sharing agreements and Health Insurance Portability and Accountability Act (HIPAA)-compliant training protocols are necessary to protect patient privacy. Although artificial intelligence technologies offer promising opportunities in health care, careful consideration of ethical principles is crucial. Addressing concerns of inaccuracy, bias, and privacy will ensure responsible and patient-centered implementation, benefiting both healthcare professionals and patients.
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Affiliation(s)
- Suraj Sheth
- From the Department of Orthopaedic Surgery, The University of Chicago, Chicago, IL
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Park HJ. Patient perspectives on informed consent for medical AI: A web-based experiment. Digit Health 2024; 10:20552076241247938. [PMID: 38698829 PMCID: PMC11064747 DOI: 10.1177/20552076241247938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/28/2024] [Indexed: 05/05/2024] Open
Abstract
Objective Despite the increasing use of AI applications as a clinical decision support tool in healthcare, patients are often unaware of their use in the physician's decision-making process. This study aims to determine whether doctors should disclose the use of AI tools in diagnosis and what kind of information should be provided. Methods A survey experiment with 1000 respondents in South Korea was conducted to estimate the patients' perceived importance of information regarding the use of an AI tool in diagnosis in deciding whether to receive the treatment. Results The study found that the use of an AI tool increases the perceived importance of information related to its use, compared with when a physician consults with a human radiologist. Information regarding the AI tool when AI is used was perceived by participants either as more important than or similar to the regularly disclosed information regarding short-term effects when AI is not used. Further analysis revealed that gender, age, and income have a statistically significant effect on the perceived importance of every piece of AI information. Conclusions This study supports the disclosure of AI use in diagnosis during the informed consent process. However, the disclosure should be tailored to the individual patient's needs, as patient preferences for information regarding AI use vary across gender, age and income levels. It is recommended that ethical guidelines be developed for informed consent when using AI in diagnoses that go beyond mere legal requirements.
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Affiliation(s)
- Hai Jin Park
- Center for AI and Law, Hanyang University Law School, Seoul, South Korea
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Long X, Deng H, Zhang Z, Liu T, Yu X, Gong P, Tian L. Development and evaluation of acceptance scale for artificial intelligence in digestive endoscopy by subjects. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2023; 48:1844-1853. [PMID: 38448378 PMCID: PMC10930752 DOI: 10.11817/j.issn.1672-7347.2023.230225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Indexed: 03/08/2024]
Abstract
OBJECTIVES Digestive endoscopy is an important diagnostic and therapeutic tool for digestive system diseases. The artificial intelligence (AI)-assisted system in endoscopy (hereinafter referred to as AI in digestive endoscopy) has broad application prospects in the field of digestive endoscopy. The trust and acceptance of endoscopic subjects are the cornerstone of the research, application, and promotion of AI in digestive endoscopy. Currently, the tools for measuring the acceptance of AI in digestive endoscopy by subjects are limited at home and abroad. This study aims to develop a scale for measuring the acceptance of AI in digestive endoscopy by subjects, then to evaluate its reliability and validity. METHODS By conducting literature research, an item pool and dimensions were constructed, and a preliminary scale was constructed using Delphi method. Through the first stage of the survey on the subjects, the reliability and validity of the scale were tested, and the revised scale was used for the second stage of survey on the subjects to further verify the structural validity of the scale. RESULTS The acceptance scale for AI in digestive endoscopy included 11 items in 3 dimensions: accuracy, ethics, benefit and willingness. In the first stage of the survey, 351 valid questionnaires were collected, and the Cronbach's α was 0.864. The correlation coefficient between the total score of the scale and the score of the test item was 0.636, and the Kaiser-Meyer-Olkin (KMO) value in exploratory factor analysis was 0.788. In the second stage of the survey, 335 valid questionnaires were collected, and in confirmatory factor analysis, the χ2/df was 3.774, while the root mean squared error of approximation (RMSEA) was 0.091. CONCLUSIONS Acceptance scale for AI in digestive endoscopy by subjects developed in this study has good reliability and validity.
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Affiliation(s)
- Xiuyan Long
- Department of Pediatrics, Third Xiangya Hospital, Central South University, Changsha 410013.
| | - Haijun Deng
- School of Mathematics & Statistics, Guizhou University of Finance and Economics, Guiyang 550025
| | - Zinan Zhang
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050
| | - Tao Liu
- Eight-Years Program of Clinical Medicine, Xiangya School of Medicine, Central South University, Changsha 410013
| | - Xiaoyu Yu
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Pan Gong
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Li Tian
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
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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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Vo V, Chen G, Aquino YSJ, Carter SM, Do QN, Woode ME. Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Soc Sci Med 2023; 338:116357. [PMID: 37949020 DOI: 10.1016/j.socscimed.2023.116357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/04/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals' perspectives to understand these issues from multiple perspectives. METHODOLOGY A search for original research articles using qualitative, quantitative, and mixed methods published between 1 Jan 2001 to 24 Aug 2021 was conducted on six bibliographic databases. Data were extracted and classified into different themes representing views on: (i) knowledge and familiarity of AI, (ii) AI benefits, risks, and challenges, (iii) AI acceptability, (iv) AI development, (v) AI implementation, (vi) AI regulations, and (vii) Human - AI relationship. RESULTS The final search identified 7,490 different records of which 105 publications were selected based on predefined inclusion/exclusion criteria. While the majority of patients, the general public and health professionals generally had a positive attitude towards the use of AI in healthcare, all groups indicated some perceived risks and challenges. Commonly perceived risks included data privacy; reduced professional autonomy; algorithmic bias; healthcare inequities; and greater burnout to acquire AI-related skills. While patients had mixed opinions on whether healthcare workers suffer from job loss due to the use of AI, health professionals strongly indicated that AI would not be able to completely replace them in their professions. Both groups shared similar doubts about AI's ability to deliver empathic care. The need for AI validation, transparency, explainability, and patient and clinical involvement in the development of AI was emphasised. To help successfully implement AI in health care, most participants envisioned that an investment in training and education campaigns was necessary, especially for health professionals. Lack of familiarity, lack of trust, and regulatory uncertainties were identified as factors hindering AI implementation. Regarding AI regulations, key themes included data access and data privacy. While the general public and patients exhibited a willingness to share anonymised data for AI development, there remained concerns about sharing data with insurance or technology companies. One key domain under this theme was the question of who should be held accountable in the case of adverse events arising from using AI. CONCLUSIONS While overall positivity persists in attitudes and preferences toward AI use in healthcare, some prevalent problems require more attention. There is a need to go beyond addressing algorithm-related issues to look at the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethics in AI.
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Affiliation(s)
- Vinh Vo
- Centre for Health Economics, Monash University, Australia.
| | - Gang Chen
- Centre for Health Economics, Monash University, Australia
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Soceity, University of Wollongong, Australia
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Soceity, University of Wollongong, Australia
| | - Quynh Nga Do
- Department of Economics, Monash University, Australia
| | - Maame Esi Woode
- Centre for Health Economics, Monash University, Australia; Monash Data Futures Research Institute, Australia
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Rodler S, Kopliku R, Ulrich D, Kaltenhauser A, Casuscelli J, Eismann L, Waidelich R, Buchner A, Butz A, Cacciamani GE, Stief CG, Westhofen T. Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial. Eur Urol Focus 2023:S2405-4569(23)00237-7. [PMID: 37923632 DOI: 10.1016/j.euf.2023.10.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/04/2023] [Accepted: 10/21/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and improve treatment outcomes. However, AI integration into clinical workflows and patient perspectives remain unclear. OBJECTIVE To determine patients' trust in AI and their perception of urologists relying on AI, and future diagnostic and therapeutic AI applications for patients. DESIGN, SETTING, AND PARTICIPANTS A prospective trial was conducted involving patients who received diagnostic or therapeutic interventions for prostate cancer (PC). INTERVENTION Patients were asked to complete a survey before magnetic resonance imaging, prostate biopsy, or radical prostatectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The primary outcome was patient trust in AI. Secondary outcomes were the choice of AI in treatment settings and traits attributed to AI and urologists. RESULTS AND LIMITATIONS Data for 466 patients were analyzed. The cumulative affinity for technology was positively correlated with trust in AI (correlation coefficient 0.094; p = 0.04), whereas patient age, level of education, and subjective perception of illness were not (p > 0.05). The mean score (± standard deviation) for trust in capability was higher for physicians than for AI for responding in an individualized way when communicating a diagnosis (4.51 ± 0.76 vs 3.38 ± 1.07; mean difference [MD] 1.130, 95% confidence interval [CI] 1.010-1.250; t924 = 18.52, p < 0.001; Cohen's d = 1.040) and for explaining information in an understandable way (4.57 ± vs 3.18 ± 1.09; MD 1.392, 95% CI 1.275-1.509; t921 = 27.27, p < 0.001; Cohen's d = 1.216). Patients stated that they had higher trust in a diagnosis made by AI controlled by a physician versus AI not controlled by a physician (4.31 ± 0.88 vs 1.75 ± 0.93; MD 2.561, 95% CI 2.444-2.678; t925 = 42.89, p < 0.001; Cohen's d = 2.818). AI-assisted physicians (66.74%) were preferred over physicians alone (29.61%), physicians controlled by AI (2.36%), and AI alone (0.64%) for treatment in the current clinical scenario. CONCLUSIONS Trust in future diagnostic and therapeutic AI-based treatment relies on optimal integration with urologists as the human-machine interface to leverage human and AI capabilities. PATIENT SUMMARY Artificial intelligence (AI) will play a role in diagnostic decisions in prostate cancer in the future. At present, patients prefer AI-assisted urologists over urologists alone, AI alone, and AI-controlled urologists. Specific traits of AI and urologists could be used to optimize diagnosis and treatment for patients with prostate cancer.
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Affiliation(s)
- Severin Rodler
- Department of Urology, LMU University Hospital, Munich, Germany; USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Rega Kopliku
- Department of Urology, LMU University Hospital, Munich, Germany
| | - Daniel Ulrich
- Department of Informatics, Ludwig-Maximilian-Universität München, Munich, Germany
| | - Annika Kaltenhauser
- Department of Informatics, Ludwig-Maximilian-Universität München, Munich, Germany
| | | | - Lennert Eismann
- Department of Urology, LMU University Hospital, Munich, Germany
| | | | | | - Andreas Butz
- Department of Informatics, Ludwig-Maximilian-Universität München, Munich, Germany
| | - Giovanni E Cacciamani
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Thilo Westhofen
- Department of Urology, LMU University Hospital, Munich, Germany
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Li LT, Haley LC, Boyd AK, Bernstam EV. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. J Biomed Inform 2023; 147:104531. [PMID: 37884177 DOI: 10.1016/j.jbi.2023.104531] [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: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges. METHODS We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine. RESULTS We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education. CONCLUSION We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers.
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Affiliation(s)
- Linda T Li
- Department of Surgery, Division of Pediatric Surgery, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States; McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States.
| | - Lauren C Haley
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Alexandra K Boyd
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Elmer V Bernstam
- McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States; McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
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16
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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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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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Ibba S, Tancredi C, Fantesini A, Cellina M, Presta R, Montanari R, Papa S, Alì M. How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders. Eur J Radiol 2023; 165:110917. [PMID: 37327548 DOI: 10.1016/j.ejrad.2023.110917] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/16/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023]
Abstract
PURPOSE In this study we investigate how patients perceive the interaction between artificial intelligence (AI) and radiologists by designing a survey. METHOD We created a survey focused on the application of Artificial Intelligence in radiology which consisted of 20 questions distributed in three sections:Only completed questionnaires were considered for analysis. RESULTS 2119 subjects completed the survey. Among them, 1216 respondents were over 60 years old, showing interest in AI even though they were not digital natives. Although >45% of the respondents reported a high level of education, only 3% said they were AI experts. 87% of respondents favored using AI to support diagnosis but would like to be informed. Only 10% would consult another specialist if their doctor used AI support. Most respondents (76%) said they would not feel comfortable if the diagnosis was made by the AI alone, highlighting the importance of the physician's role in the emotional management of the patient. Finally, 36% of respondents were willing to discuss the topic further in a focus group. CONCLUSION Patients' perception of the use of AI in radiology was positive, although still strictly linked to the supervision of the radiologist. Respondents showed interest and willingness to learn more about AI in the medical field, confirming how patients' confidence in AI technology and its acceptance is central to its widespread use in clinical practice.
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Affiliation(s)
- Simona Ibba
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Chiara Tancredi
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Arianna Fantesini
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.
| | - Roberta Presta
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Roberto Montanari
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Marco Alì
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy; Bracco Imaging S.p.A., Via Egidio Folli, 50, 20134 Milan, Italy.
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Bahakeem BH, Alobaidi SF, Alzahrani AS, Alhasawi R, Alzahrani A, Alqahtani W, Alhashmi Alamer L, Bin Laswad BM, Al Shanbari N. The General Population's Perspectives on Implementation of Artificial Intelligence in Radiology in the Western Region of Saudi Arabia. Cureus 2023; 15:e37391. [PMID: 37182053 PMCID: PMC10171828 DOI: 10.7759/cureus.37391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2023] [Indexed: 05/16/2023] Open
Abstract
Background Artificial intelligence (AI) is a broad spectrum of computer-executed operations that mimics the human intellect. It is expected to improve healthcare practice in general and radiology in particular by enhancing image acquisition, image analysis, and processing speed. Despite the rapid development of AI systems, successful application in radiology requires analysis of social factors such as the public's perspectives toward the technology. Objectives The current study aims to investigate the general population's perspectives on AI implementation in radiology in the Western region of Saudi Arabia. Methods A cross-sectional study was conducted from November 2022 and July 2023 utilizing a self-administrative online survey distributed via social media platforms. A convenience sampling technique was used to recruit the study participants. After obtaining Institutional Review Board approval, data were collected from citizens and residents of the western region of Saudi Arabia aged 18 years or older. Results A total of 1,024 participants were included in the present study, with the mean age of respondents being 29.6 ± 11.3. Of them, 49.9% (511) were men, and 50.1% (513) were women. The comprehensive mean score of the first four domains among our participants was 3.93 out of 5.00. Higher mean scores suggest being more negative regarding AI in radiology, except for the fifth domain. Respondents had less trust in AI utilization in radiology, as evidenced by their overall distrust and accountability domain mean score of 3.52 out of 5. The majority of respondents agreed that it is essential to understand every step of the diagnostic process, and the mean score for the procedural knowledge domain was 4.34 out of 5. The mean score for the personal interaction domain was 4.31 out of 5, indicating that the participants agreed on the value of direct communication between the patient and the radiologist for discussing test results and asking questions. Our data show that people think AI is more effective than human doctors in making accurate diagnoses and decreasing patient wait times, with an overall mean score of the efficiency domain of 3.56 out of 5. Finally, the fifth domain, "being informed," had a mean score of 3.91 out of 5. Conclusion The application of AI in radiologic assessment and interpretation is generally viewed negatively. Even though people think AI is more efficient and accurate at diagnosing than humans, they still think that computers will never be able to match a specialist doctor's years of training.
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Affiliation(s)
- Basem H Bahakeem
- Department of Medical Imaging, College of Medicine, Umm Al-Qura University, Makkah, SAU
| | - Sultan F Alobaidi
- Department of Medicine and Surgery, College of Medicine, Umm Al-Qura University, Makkah, SAU
| | - Amjad S Alzahrani
- Department of Medicine and Surgery, College of Medicine, Umm Al-Qura University, Makkah, SAU
| | - Roudin Alhasawi
- Department of Medicine and Surgery, College of Medicine, Umm Al-Qura University, Makkah, SAU
| | - Abdulkarem Alzahrani
- Department of Medicine and Surgery, College of Medicine, Umm Al-Qura University, Makkah, SAU
| | - Wed Alqahtani
- Department of Medicine and Surgery, College of Medicine, Umm Al-Qura University, Makkah, SAU
| | - Lujain Alhashmi Alamer
- Department of Medicine and Surgery, College of Medicine, Umm Al-Qura University, Makkah, SAU
| | - Bassam M Bin Laswad
- Department of Medicine and Surgery, College of Medicine, Umm Al-Qura University, Makkah, SAU
| | - Nasser Al Shanbari
- Department of Medicine and Surgery, College of Medicine, Umm Al-Qura University, Makkah, SAU
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Al-Medfa MK, Al-Ansari AM, Darwish AH, Qreeballa TA, Jahrami H. Physicians’ attitudes and knowledge toward artificial intelligence in medicine: Benefits and drawbacks. Heliyon 2023; 9:e14744. [PMID: 37035387 PMCID: PMC10073828 DOI: 10.1016/j.heliyon.2023.e14744] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 03/28/2023] Open
Abstract
The use of artificial intelligence (AI) in the medical field is increasing and is expected to shape future clinical practice and job security. Therefore, this study aimed to assess the opinions and attitudes of practicing physicians in Bahrain regarding the benefits and drawbacks of AI for their future daily practice. A cross-sectional survey of practicing physicians with a minimum of five years' experience across the main secondary and tertiary care hospitals in Bahrain was conducted. An online questionnaire was used to collect data on demographics, knowledge of AI, attitudes towards the use of AI in 10 tasks of daily clinical practice, and opinions on the benefits and drawbacks of AI. A total of 114 physicians participated in the survey. Among them, 43 (37.7%) were registered psychiatrists, 15 (13.2%) were pathologists, 17 (14.9%) were radiologists, and 39 (34.2%) were surgical specialists. The participants' attitudes were overall positive towards AI. Pathologists were particularly in favor of using AI to "Formulate personalized medication and/or treatment plans for patients" and to "Interview patients in a range of settings to obtain medical history." Most participants agreed that AI would reduce the time needed to establish a diagnosis and negatively affect employment rates. There were no correlations between the responses and the participants' age, gender, years of experience, or AI knowledge. This study demonstrates that the attitudes towards the use of AI in medicine among practicing physicians in Bahrain are similar to those of physicians in developed countries in that they are positive and welcoming of AI implementation in practice. However, the potential effects of AI on job security are a major concern.
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Affiliation(s)
- Mohammed Khalid Al-Medfa
- Department of Internal Medicine, College of Medicine and Medical Sciences, Arabian Gulf University, Bahrain
| | - Ahmed M.S. Al-Ansari
- Department of Psychiatry, College of Medicine and Medical Sciences, Arabian Gulf University, Bahrain
- Corresponding author.
| | | | | | - Haitham Jahrami
- Department of Psychiatry, College of Medicine and Medical Sciences, Arabian Gulf University, Bahrain
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Temple S, Rowbottom C, Simpson J. Patient views on the implementation of artificial intelligence in radiotherapy. Radiography (Lond) 2023; 29 Suppl 1:S112-S116. [PMID: 36964044 DOI: 10.1016/j.radi.2023.03.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 03/26/2023]
Abstract
PURPOSE/OBJECTIVE To date there has been limited research looking at patient views on the implementation of artificial intelligence (AI) in radiotherapy. The aim of this study is to adapt and utilise a validated patient questionnaire to develop an understanding of current patient views on the use of AI in radiotherapy. MATERIALS/METHODS An existing questionnaire, developed to assess understanding of patients' views on the implementation of AI in radiology, was adapted to the field of radiotherapy. The questionnaire was distributed to cancer patients receiving radiotherapy treatment between November 2021 and March 2022. Completed questionnaires were analysed to assess patient levels of positivity or negativity towards AI. Results were grouped into five factors, representing underlying patient perspectives, and correlation of factors with demographic variables was assessed. RESULTS In total, 95 patients participated. Overall, there was a moderately negative patient view towards the use of AI in radiotherapy. Certain factors drew a more negative response than others, for example patients desire significant personal interaction with healthcare professionals during the course of their treatment. No significant correlation was found between the demographics of age and gender and the strength of views towards the use of AI in radiotherapy. CONCLUSION This study has found that there are clear patient concerns around the use of AI in radiotherapy. As the use of AI in this field increases in future years, it will therefore be extremely important to educate and involve patients in the future direction of this technology.
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Affiliation(s)
- S Temple
- The Clatterbridge Cancer Centre NHS Foundation Trust, 65 Pembroke Place, Liverpool L7 8YA, UK.
| | - C Rowbottom
- The Clatterbridge Cancer Centre NHS Foundation Trust, 65 Pembroke Place, Liverpool L7 8YA, UK
| | - J Simpson
- The Clatterbridge Cancer Centre NHS Foundation Trust, 65 Pembroke Place, Liverpool L7 8YA, UK
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22
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Jeyakumar T, Younus S, Zhang M, Clare M, Charow R, Karsan I, Dhalla A, Al-Mouaswas D, Scandiffio J, Aling J, Salhia M, Lalani N, Overholt S, Wiljer D. Preparing for an Artificial Intelligence-Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings. JMIR AI 2023; 2:e40973. [PMID: 38875561 PMCID: PMC11041489 DOI: 10.2196/40973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/29/2022] [Accepted: 12/29/2022] [Indexed: 06/16/2024]
Abstract
BACKGROUND As new technologies emerge, there is a significant shift in the way care is delivered on a global scale. Artificial intelligence (AI) technologies have been rapidly and inexorably used to optimize patient outcomes, reduce health system costs, improve workflow efficiency, and enhance population health. Despite the widespread adoption of AI technologies, the literature on patient engagement and their perspectives on how AI will affect clinical care is scarce. Minimal patient engagement can limit the optimization of these novel technologies and contribute to suboptimal use in care settings. OBJECTIVE We aimed to explore patients' views on what skills they believe health care professionals should have in preparation for this AI-enabled future and how we can better engage patients when adopting and deploying AI technologies in health care settings. METHODS Semistructured interviews were conducted from August 2020 to December 2021 with 12 individuals who were a patient in any Canadian health care setting. Interviews were conducted until thematic saturation occurred. A thematic analysis approach outlined by Braun and Clarke was used to inductively analyze the data and identify overarching themes. RESULTS Among the 12 patients interviewed, 8 (67%) were from urban settings and 4 (33%) were from rural settings. A majority of the participants were very comfortable with technology (n=6, 50%) and somewhat familiar with AI (n=7, 58%). In total, 3 themes emerged: cultivating patients' trust, fostering patient engagement, and establishing data governance and validation of AI technologies. CONCLUSIONS With the rapid surge of AI solutions, there is a critical need to understand patient values in advancing the quality of care and contributing to an equitable health system. Our study demonstrated that health care professionals play a synergetic role in the future of AI and digital technologies. Patient engagement is vital in addressing underlying health inequities and fostering an optimal care experience. Future research is warranted to understand and capture the diverse perspectives of patients with various racial, ethnic, and socioeconomic backgrounds.
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Affiliation(s)
| | | | | | - Megan Clare
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Rebecca Charow
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Inaara Karsan
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | | | - Dalia Al-Mouaswas
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Justin Aling
- Patient Partner Program, University Health Network, Toronto, ON, Canada
| | - Mohammad Salhia
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Scott Overholt
- Patient Partner Program, University Health Network, Toronto, ON, Canada
| | - David Wiljer
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Office of Education, Centre for Addiction and Mental Health, Toronto, ON, Canada
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Hemphill S, Jackson K, Bradley S, Bhartia B. The implementation of artificial intelligence in radiology: a narrative review of patient perspectives. Future Healthc J 2023; 10:63-68. [PMID: 37786489 PMCID: PMC10538685 DOI: 10.7861/fhj.2022-0097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Aim To synthesise research on the view of the public and patients of the use of artificial intelligence (AI) in radiology investigations. Methods A literature review of narrative synthesis of qualitative and quantitative studies that reported views of the public and patients toward the use of AI in radiology. Results Only seven studies related to patient and public views were retrieved, suggesting that this is an underexplored area of research. Two broad themes, of confidence in the capabilities of AI, and the accountability and transparency of AI, were identified. Conclusions Both optimism and concerns were expressed by participants. Transparency in the implementation of AI, scientific validation, clear regulation and accountability were expected. Combined human and AI interpretation of imaging was strongly favoured over AI acting autonomously. The review highlights the limited engagement of the public in the adoption of AI in a radiology setting. Successful implementation of AI in this field will require demonstrating not only adequate accuracy of the technology, but also its acceptance by patients.
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Lawrie L, Turner S, Cotton SC, Wood J, Morgan HM. A qualitative process evaluation within a clinical trial that used healthcare technologies for children with asthma-insights and implications. PLoS One 2023; 18:e0280086. [PMID: 36603013 PMCID: PMC9815588 DOI: 10.1371/journal.pone.0280086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 12/20/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Healthcare technologies are becoming more commonplace, however clinical and patient perspectives regarding the use of technology in the management of childhood asthma have yet to be investigated. Within a clinical trial of asthma management in children, we conducted a qualitative process evaluation that provided insights into the experiences and perspectives of healthcare staff and families on (i) the use of smart inhalers to monitor medication adherence and (ii) the use of algorithm generated treatment recommendations. METHODS We interviewed trial staff (n = 15) and families (n = 6) who were involved in the trial to gauge perspectives around the use of smart inhalers to monitor adherence and the algorithm to guide clinical decision making. FINDINGS Staff and families indicated that there were technical issues associated with the smart inhalers. While staff suggested that the smart inhalers were good for monitoring adherence and enabling communication regarding medication use, parents and children indicated that smart inhaler use increased motivation to adhere to medication and provided the patient (child) with a sense of responsibility for the management of their asthma. Staff were open-minded about the use of the algorithm to guide treatment recommendations, but some were not familiar with its' use in clinical care. There were some concerns expressed regarding treatment step-down decisions generated by the algorithm, and some staff highlighted the importance of using clinical judgement. Families perceived the algorithm to be a useful technology, but indicated that they felt comforted by the clinicians' own judgements. CONCLUSION The use of technology and individual data within appointments was considered useful to both staff and families: closer monitoring and the educational impacts were especially highlighted. Utilising an algorithm was broadly acceptable, with caveats around clinicians using the recommendations as a guide only and wariness around extreme step-ups/downs considering contextual factors not taken into account.
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Affiliation(s)
- Louisa Lawrie
- Health Services Research Unit, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Stephen Turner
- Royal Aberdeen Children’s Hospital, University of Aberdeen, Aberdeen, United Kingdom
| | - Seonaidh C. Cotton
- Health Services Research Unit, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Jessica Wood
- Health Services Research Unit, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Heather M. Morgan
- Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
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25
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Armero W, Gray KJ, Fields KG, Cole NM, Bates DW, Kovacheva VP. A survey of pregnant patients' perspectives on the implementation of artificial intelligence in clinical care. J Am Med Inform Assoc 2022; 30:46-53. [PMID: 36250788 PMCID: PMC9748543 DOI: 10.1093/jamia/ocac200] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 07/17/2022] [Accepted: 10/04/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery. MATERIALS AND METHODS We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019-June 2020. We investigated the role and interplay of patient demographic factors, healthcare literacy, understanding of AI, comfort levels with various AI scenarios, and preferences for AI use in clinical care. RESULTS Of the 349 parturients, 57.6% were between the ages of 25-34 years, 90.1% reported college or graduate education and 69.2% believed the benefits of AI use in clinical care outweighed the risks. Cluster analysis revealed 2 distinct groups: patients more comfortable with clinical AI use (Pro-AI) and those who preferred physician presence (AI-Cautious). Pro-AI patients had a higher degree of education, were more knowledgeable about AI use in their daily lives and saw AI use as a significant advancement in medicine. AI-Cautious patients reported a lack of human qualities and low trust in the technology as detriments to AI use. DISCUSSION Patient trust and the preservation of the human physician-patient relationship are critical in moving forward with AI implementation in healthcare. Pregnant individuals are cautiously optimistic about AI use in their care. CONCLUSION Our findings provide insights into the status of AI use in perinatal care and provide a platform for driving patient-centered innovations.
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Affiliation(s)
- William Armero
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Kathryn J Gray
- Division of Maternal-Fetal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kara G Fields
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Naida M Cole
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, Illinois, USA
| | - David W Bates
- Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Department of Health Care Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Vesela P Kovacheva
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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26
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Nichol BAB, Hurlbert AC, Read JCA. Predicting attitudes towards screening for neurodegenerative diseases using OCT and artificial intelligence: Findings from a literature review. J Public Health Res 2022; 11:22799036221127627. [PMID: 36310821 PMCID: PMC9597051 DOI: 10.1177/22799036221127627] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/02/2022] [Indexed: 11/25/2022] Open
Abstract
Recent developments in artificial intelligence (AI) and machine learning raise the possibility of screening and early diagnosis for neurodegenerative diseases, using 3D scans of the retina. The eventual value of such screening will depend not only on scientific metrics such as specificity and sensitivity but, critically, also on public attitudes and uptake. Differential screening rates for various screening programmes in England indicate that multiple factors influence uptake. In this narrative literature review, some of these potential factors are explored in relation to predicting uptake of an early screening tool for neurodegenerative diseases using AI. These include: awareness of the disease, perceived risk, social influence, the use of AI, previous screening experience, socioeconomic status, health literacy, uncontrollable mortality risk, and demographic factors. The review finds the strongest and most consistent predictors to be ethnicity, social influence, the use of AI, and previous screening experience. Furthermore, it is likely that factors also interact to predict the uptake of such a tool. However, further experimental work is needed both to validate these predictions and explore interactions between the significant predictors.
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Affiliation(s)
- Beth AB Nichol
- Department of Social Work, Education,
and Community Wellbeing, Northumbria University, Newcastle upon Tyne, UK,Beth AB Nichol, Department of Social Work,
Education, and Community Wellbeing, Northumbria University, Coach Lane,
Newcastle upon Tyne NE7 7XA, UK.
| | - Anya C Hurlbert
- Biosciences Institute, Newcastle
University, Newcastle upon Tyne, UK
| | - Jenny CA Read
- Biosciences Institute, Newcastle
University, Newcastle upon Tyne, UK
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27
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van der Zander QEW, van der Ende-van Loon MCM, Janssen JMM, Winkens B, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence in (gastrointestinal) healthcare: patients' and physicians' perspectives. Sci Rep 2022; 12:16779. [PMID: 36202957 PMCID: PMC9537305 DOI: 10.1038/s41598-022-20958-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/21/2022] [Indexed: 12/01/2022] Open
Abstract
Artificial intelligence (AI) is entering into daily life and has the potential to play a significant role in healthcare. Aim was to investigate the perspectives (knowledge, experience, and opinion) on AI in healthcare among patients with gastrointestinal (GI) disorders, gastroenterologists, and GI-fellows. In this prospective questionnaire study 377 GI-patients, 35 gastroenterologists, and 45 GI-fellows participated. Of GI-patients, 62.5% reported to be familiar with AI and 25.0% of GI-physicians had work-related experience with AI. GI-patients preferred their physicians to use AI (mean 3.9) and GI-physicians were willing to use AI (mean 4.4, on 5-point Likert-scale). More GI-physicians believed in an increase in quality of care (81.3%) than GI-patients (64.9%, χ2(2) = 8.2, p = 0.017). GI-fellows expected AI implementation within 6.0 years, gastroenterologists within 4.2 years (t(76) = − 2.6, p = 0.011), and GI-patients within 6.1 years (t(193) = − 2.0, p = 0.047). GI-patients and GI-physicians agreed on the most important advantages of AI in healthcare: improving quality of care, time saving, and faster diagnostics and shorter waiting times. The most important disadvantage for GI-patients was the potential loss of personal contact, for GI-physicians this was insufficiently developed IT infrastructures. GI-patients and GI-physicians hold positive perspectives towards AI in healthcare. Patients were significantly more reserved compared to GI-fellows and GI-fellows were more reserved compared to gastroenterologists.
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Affiliation(s)
- Quirine E W van der Zander
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands. .,GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
| | | | - Janneke M M Janssen
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Maastricht University, Maastricht, The Netherlands.,CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ad A M Masclee
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.,Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
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28
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Patel B, Makaryus AN. Reply to Giansanti, D. Comment on "Patel, B.; Makaryus, A.N. Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare 2022, 10, 154". Healthcare (Basel) 2022; 10:735. [PMID: 35455912 PMCID: PMC9030845 DOI: 10.3390/healthcare10040735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 02/05/2023] Open
Abstract
Thank you for your interest and comment [...].
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Affiliation(s)
- Bhakti Patel
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, NY 11549, USA;
| | - Amgad N. Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, NY 11549, USA;
- Department of Cardiology, Nassau University Medical Center, East Meadow, NY 11554, USA
- Department of Cardiology, Northwell Health, Manhasset, NY 11030, USA
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29
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Giansanti D. Comment on Patel, B.; Makaryus, A.N. Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare 2022, 10, 154. Healthcare (Basel) 2022; 10:727. [PMID: 35455904 PMCID: PMC9032641 DOI: 10.3390/healthcare10040727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/01/2022] [Accepted: 04/07/2022] [Indexed: 02/05/2023] Open
Abstract
Regarding Dr. Makaryus's interesting review study [...].
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Affiliation(s)
- Daniele Giansanti
- Centro Nazionale Tecnologie Innovative in Sanità Pubblica, Istituto Superiore di Sanità, 00161 Rome, Italy
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30
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Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2022.107296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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31
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Shah P, Mishra D, Shanmugam M, Vighnesh MJ, Jayaraj H. Acceptability of artificial intelligence-based retina screening in general population. Indian J Ophthalmol 2022; 70:1140-1144. [PMID: 35326001 PMCID: PMC9240537 DOI: 10.4103/ijo.ijo_1840_21] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Purpose: A deep learning system (DLS) using artificial intelligence (AI) is emerging as a very promising technology in the future of healthcare diagnostics. While the concept of telehealth is emerging in every field of medicine, AI assistance in diagnosis can become a great tool for successful screening in telemedicine and teleophthalmology. The aim of our study was to assess the acceptability of AI-based retina screening. Methods: This was a prospective non-randomized study performed in the outpatient department of a tertiary eye care hospital. Patients older than 18 years who came for a regular eye check-up or a routine retina screening were recruited in the study. Fundus images of the posterior pole were captured on fundus on a phone camera (REMIDIO™, India) with a built-in AI software (Netra.AI) that can identify normal versus abnormal retina. The patients were then given an 8-point questionnaire to assess their acceptance and willingness toward AI-based screening. We recruited 104 participants. Results: We found that 90.4% were willing for an AI-based fundus screening; 96.2% were satisfied with AI-based screening. Patients with diabetes (P = 0.03) and the male population (P = 0.029) were more satisfied with the AI-based screening. The majority (i.e., 97.1%) felt that AI-based screening gave them a better understanding of their eye condition and 37.5% felt that AI-based retina screening prior to a doctor’s visit can help in routine screening Conclusion: Considering the current COVID-19 pandemic situation across the globe, this study highlights the importance of AI-based telescreening and positive patient approach toward this technology.
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Affiliation(s)
- Payal Shah
- Department of Vitreoretinal Services, Sankara Eye Hospital, Bengaluru, Karnataka, India
| | - Divyansh Mishra
- Department of Vitreoretinal Services, Sankara Eye Hospital, Bengaluru, Karnataka, India
| | - Mahesh Shanmugam
- Department of Vitreoretinal Services, Sankara Eye Hospital, Bengaluru, Karnataka, India
| | - M J Vighnesh
- Sankara College of Optometry, Bengaluru, Karnataka, India
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Giansanti D, Di Basilio F. The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus. Healthcare (Basel) 2022; 10:509. [PMID: 35326987 PMCID: PMC8949694 DOI: 10.3390/healthcare10030509] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/03/2022] [Accepted: 03/08/2022] [Indexed: 12/27/2022] Open
Abstract
Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and acceptance and consensus in the field of Artificial Intelligence. The challenges and acceptance and consensus are two strategic aspects in the development and integration of technologies in the health domain. The study conducted two narrative reviews by means of two parallel points of view to take stock both on the ongoing challenges and on initiatives conducted to face the acceptance and consensus in this area. The methodology of the review was based on: (I) search of PubMed and Scopus and (II) an eligibility assessment, using parameters with 5 levels of score. The results have: (a) highlighted and categorized the important challenges in place. (b) Illustrated the different types of studies conducted through original questionnaires. The study suggests for future research based on questionnaires a better calibration and inclusion of the challenges in place together with validation and administration paths at an international level.
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Khanijahani A, Iezadi S, Dudley S, Goettler M, Kroetsch P, Wise J. Organizational, professional, and patient characteristics associated with artificial intelligence adoption in healthcare: A systematic review. HEALTH POLICY AND TECHNOLOGY 2022. [DOI: 10.1016/j.hlpt.2022.100602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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34
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Di Basilio F, Esposisto G, Monoscalco L, Giansanti D. The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders. Healthcare (Basel) 2022; 10:153. [PMID: 35052316 PMCID: PMC8775988 DOI: 10.3390/healthcare10010153] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/19/2021] [Accepted: 01/10/2022] [Indexed: 02/04/2023] Open
Abstract
Background. The study deals with the introduction of the artificial intelligence in digital radiology. There is a growing interest in this area of scientific research in acceptance and consensus studies involving both insiders and the public, based on surveys focused mainly on single professionals. Purpose. The goal of the study is to perform a contemporary investigation on the acceptance and the consensus of the three key professional figures approaching in this field of application: (1) Medical specialists in image diagnostics: the medical specialists (MS)s; (2) experts in physical imaging processes: the medical physicists (MP)s; (3) AI designers: specialists of applied sciences (SAS)s. Methods. Participants (MSs = 92: 48 males/44 females, averaged age 37.9; MPs = 91: 43 males/48 females, averaged age 36.1; SAS = 90: 47 males/43 females, averaged age 37.3) were properly recruited based on specific training. An electronic survey was designed and submitted to the participants with a wide range questions starting from the training and background up to the different applications of the AI and the environment of application. Results. The results show that generally, the three professionals show (a) a high degree of encouraging agreement on the introduction of AI both in imaging and in non-imaging applications using both standalone applications and/or mHealth/eHealth, and (b) a different consent on AI use depending on the training background. Conclusions. The study highlights the usefulness of focusing on both the three key professionals and the usefulness of the investigation schemes facing a wide range of issues. The study also suggests the importance of different methods of administration to improve the adhesion and the need to continue these investigations both with federated and specific initiatives.
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Affiliation(s)
- Francesco Di Basilio
- Facoltà di Medicina e Psicologia, Sapienza University, Piazzale Aldo Moro, 00185 Rome, Italy; (F.D.B.); (G.E.)
| | - Gianluca Esposisto
- Facoltà di Medicina e Psicologia, Sapienza University, Piazzale Aldo Moro, 00185 Rome, Italy; (F.D.B.); (G.E.)
| | - Lisa Monoscalco
- Faculty of Engineering, Tor Vergata University, 00133 Rome, Italy;
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Monoscalco L, Simeoni R, Maccioni G, Giansanti D. Information Security in Medical Robotics: A Survey on the Level of Training, Awareness and Use of the Physiotherapist. Healthcare (Basel) 2022; 10:159. [PMID: 35052322 PMCID: PMC8775601 DOI: 10.3390/healthcare10010159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/03/2022] [Accepted: 01/06/2022] [Indexed: 01/27/2023] Open
Abstract
Cybersecurity is becoming an increasingly important aspect to investigate for the adoption and use of care robots, in term of both patients' safety, and the availability, integrity and privacy of their data. This study focuses on opinions about cybersecurity relevance and related skills for physiotherapists involved in rehabilitation and assistance thanks to the aid of robotics. The goal was to investigate the awareness among insiders about some facets of cybersecurity concerning human-robot interactions. We designed an electronic questionnaire and submitted it to a relevant sample of physiotherapists. The questionnaire allowed us to collect data related to: (i) use of robots and its relationship with cybersecurity in the context of physiotherapy; (ii) training in cybersecurity and robotics for the insiders; (iii) insiders' self-assessment on cybersecurity and robotics in some usage scenarios, and (iv) their experiences of cyber-attacks in this area and proposals for improvement. Besides contributing some specific statistics, the study highlights the importance of both acculturation processes in this field and monitoring initiatives based on surveys. The study exposes direct suggestions for continuation of these types of investigations in the context of scientific societies operating in the rehabilitation and assistance robotics. The study also shows the need to stimulate similar initiatives in other sectors of medical robotics (robotic surgery, care and socially assistive robots, rehabilitation systems, training for health and care workers) involving insiders.
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Affiliation(s)
- Lisa Monoscalco
- Faculty of Engineering, Tor Vergata University, Via Cracovia, 00133 Rome, Italy;
| | - Rossella Simeoni
- Facoltà di Medicina e Chirurgia, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy;
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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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Scott IA, Carter SM, Coiera E. Exploring stakeholder attitudes towards AI in clinical practice. BMJ Health Care Inform 2021; 28:bmjhci-2021-100450. [PMID: 34887331 PMCID: PMC8663096 DOI: 10.1136/bmjhci-2021-100450] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/14/2021] [Indexed: 12/31/2022] Open
Abstract
Objectives Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out to ascertain evidence of the attitudes of clinicians, consumers, managers, researchers, regulators and industry towards AI applications in healthcare. Methods We undertook an exploratory analysis of articles whose titles or abstracts contained the terms ‘artificial intelligence’ or ‘AI’ and ‘medical’ or ‘healthcare’ and ‘attitudes’, ‘perceptions’, ‘opinions’, ‘views’, ‘expectations’. Using a snowballing strategy, we searched PubMed and Google Scholar for articles published 1 January 2010 through 31 May 2021. We selected articles relating to non-robotic clinician-facing AI applications used to support healthcare-related tasks or decision-making. Results Across 27 studies, attitudes towards AI applications in healthcare, in general, were positive, more so for those with direct experience of AI, but provided certain safeguards were met. AI applications which automated data interpretation and synthesis were regarded more favourably by clinicians and consumers than those that directly influenced clinical decisions or potentially impacted clinician–patient relationships. Privacy breaches and personal liability for AI-related error worried clinicians, while loss of clinician oversight and inability to fully share in decision-making worried consumers. Both clinicians and consumers wanted AI-generated advice to be trustworthy, while industry groups emphasised AI benefits and wanted more data, funding and regulatory certainty. Discussion Certain expectations of AI applications were common to many stakeholder groups from which a set of dependencies can be defined. Conclusion Stakeholders differ in some but not all of their attitudes towards AI. Those developing and implementing applications should consider policies and processes that bridge attitudinal disconnects between different stakeholders.
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Affiliation(s)
- Ian A Scott
- Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia .,School of Clinical Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Stacy M Carter
- Australian Centre for Health Engagement Evidence and Values, School of Health and Society, University of Wollongong, Wollongong, New South Wales, Australia
| | - Enrico Coiera
- Centre for Clinical Informatics, Macquarie University, Sydney, New South Wales, Australia
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