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Alanzi TM, Alanzi N, Arif WM, Alkhunaifer A, Al Ghaseb L, Albadrani YH, Hasoosah N, Abdullah RA, Al Shullah B. Patient preferences for teleradiology services and remote image interpretation: An empirical study. Nutr Health 2024:2601060241264649. [PMID: 39043374 DOI: 10.1177/02601060241264649] [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: 07/25/2024]
Abstract
STUDY PURPOSE This study aims to examine patient preferences for teleradiology services and remote image interpretation. In this context, this study aims to address the following research questions: (i) How do patients perceive teleradiology services, focusing specifically on comfort, quality of care and satisfaction, communication and information, and accessibility and ease of use; (ii) How do patient demographics (age, gender, urban vs. rural residence) influence the perceptions on teleradiology services? METHODS A cross-sectional survey design is adopted in this study. The survey comprises five sections targeting demographic information, comfort, and preferences regarding remote image interpretation, perceived quality of care and satisfaction, communication and information clarity, and accessibility and technology aspects using five-point Likert scale ratings. A total of 406 patients (209 males and 197 females; 170 urban residents; 174 semi-urban residents, and 62 rural residents) using teleradiology services participated in the study. RESULTS Participants reported high satisfaction with remote image interpretation (3.78 ± 1.19), quality of care (3.31 ± 1.19), understanding (3.84 ± 1.43), and user-friendliness (3.67 ± 1.29). Key issues were technical problems (3.81 ± 1.35), feedback difficulties (3.19 ± 1.58), privacy concerns (2.43 ± 1.46), and low awareness (2.37 ± 1.12). Urban participants scored significantly better in comfort, preferences, and communication than those from semi-urban and rural areas. CONCLUSION Teleradiology design and implementation should be optimized to align with patient preferences and enhance overall satisfaction.
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Affiliation(s)
- Turki M Alanzi
- Public Health College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Nouf Alanzi
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Jouf University, Jouf, Saudi Arabia
| | - Wejdan M Arif
- Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Alhanoof Alkhunaifer
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Lamia Al Ghaseb
- Department of Family Medicine, General Directorate of Health Affairs, Aseer Region, Abha, Saudi Arabia
| | - Yara Hamad Albadrani
- College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | | | - Ruya Adel Abdullah
- College of Medicine, Ibn Sina National College for Medical Studies, Jeddah, Saudi Arabia
| | - Batool Al Shullah
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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Cerda IH, Zhang E, Dominguez M, Ahmed M, Lang M, Ashina S, Schatman ME, Yong RJ, Fonseca ACG. Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management. Curr Pain Headache Rep 2024:10.1007/s11916-024-01279-7. [PMID: 38836996 DOI: 10.1007/s11916-024-01279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE OF REVIEW This review provides an overview of the current and future role of artificial intelligence (AI) and virtual reality (VR) in addressing the complexities inherent to the diagnosis, classification, and management of headache disorders. RECENT FINDINGS Through machine learning and natural language processing approaches, AI offers unprecedented opportunities to identify patterns within complex and voluminous datasets, including brain imaging data. This technology has demonstrated promise in optimizing diagnostic approaches to headache disorders and automating their classification, an attribute particularly beneficial for non-specialist providers. Furthermore, AI can enhance headache disorder management by enabling the forecasting of acute events of interest, such as migraine headaches or medication overuse, and by guiding treatment selection based on insights from predictive modeling. Additionally, AI may facilitate the streamlining of treatment efficacy monitoring and enable the automation of real-time treatment parameter adjustments. VR technology, on the other hand, offers controllable and immersive experiences, thus providing a unique avenue for the investigation of the sensory-perceptual symptomatology associated with certain headache disorders. Moreover, recent studies suggest that VR, combined with biofeedback, may serve as a viable adjunct to conventional treatment. Addressing challenges to the widespread adoption of AI and VR in headache medicine, including reimbursement policies and data privacy concerns, mandates collaborative efforts from stakeholders to enable the equitable, safe, and effective utilization of these technologies in advancing headache disorder care. This review highlights the potential of AI and VR to support precise diagnostics, automate classification, and enhance management strategies for headache disorders.
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Affiliation(s)
| | - Emily Zhang
- Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Moises Dominguez
- Department of Neurology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA
| | | | - Min Lang
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Sait Ashina
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Anesthesiology, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael E Schatman
- Department of Anesthesiology, Perioperative Care, and Pain Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Department of Population Health-Division of Medical Ethics, NYU Grossman School of Medicine, New York, NY, USA
| | - R Jason Yong
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Department of Anesthesiology, Perioperative, and Pain Medicine, 75 Francis Street, Boston, MA, 02115, USA
| | - Alexandra C G Fonseca
- Harvard Medical School, Boston, MA, USA.
- Brigham and Women's Hospital, Department of Anesthesiology, Perioperative, and Pain Medicine, 75 Francis Street, Boston, MA, 02115, USA.
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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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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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Liu TYA, Huang J, Channa R, Wolf R, Dong Y, Liang M, Wang J, Abramoff M. Autonomous Artificial Intelligence Increases Access and Health Equity in Underserved Populations with Diabetes. RESEARCH SQUARE 2024:rs.3.rs-3979992. [PMID: 38559222 PMCID: PMC10980149 DOI: 10.21203/rs.3.rs-3979992/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Diabetic eye disease (DED) is a leading cause of blindness in the world. Early detection and treatment of DED have been shown to be both sight-saving and cost-effective. As such, annual testing for DED is recommended for adults with diabetes and is a Healthcare Effectiveness Data and Information Set (HEDIS) measure. However, adherence to this guideline has historically been low, and access to this sight-saving intervention has particularly been limited for specific populations, such as Black or African American patients. In 2018, the US Food and Drug Agency (FDA) De Novo cleared autonomous artificial intelligence (AI) for diagnosing DED in a primary care setting. In 2020, Johns Hopkins Medicine (JHM), an integrated healthcare system with over 30 primary care sites, began deploying autonomous AI for DED testing in some of its primary care clinics. In this retrospective study, we aimed to determine whether autonomous AI implementation was associated with increased adherence to annual DED testing, and whether this was different for specific populations. JHM primary care sites were categorized as "non-AI" sites (sites with no autonomous AI deployment over the study period and where patients are referred to eyecare for DED testing) or "AI-switched" sites (sites that did not have autonomous AI testing in 2019 but did by 2021). We conducted a difference-in-difference analysis using a logistic regression model to compare change in adherence rates from 2019 to 2021 between non-AI and AI-switched sites. Our study included all adult patients with diabetes managed within our health system (17,674 patients for the 2019 cohort and 17,590 patients for the 2021 cohort) and has three major findings. First, after controlling for a wide range of potential confounders, our regression analysis demonstrated that the odds ratio of adherence at AI-switched sites was 36% higher than that of non-AI sites, suggesting that there was a higher increase in DED testing between 2019 and 2021 at AI-switched sites than at non-AI sites. Second, our data suggested autonomous AI improved access for historically disadvantaged populations. The adherence rate for Black/African Americans increased by 11.9% within AI-switched sites whereas it decreased by 1.2% within non-AI sites over the same time frame. Third, the data suggest that autonomous AI improved health equity by closing care gaps. For example, in 2019, a large adherence rate gap existed between Asian Americans and Black/African Americans (61.1% vs. 45.5%). This 15.6% gap shrank to 3.5% by 2021. In summary, our real-world deployment results in a large integrated healthcare system suggest that autonomous AI improves adherence to a HEDIS measure, patient access, and health equity for patients with diabetes - particularly in historically disadvantaged patient groups. While our findings are encouraging, they will need to be replicated and validated in a prospective manner across more diverse settings.
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Affiliation(s)
| | | | | | - Risa Wolf
- Johns Hopkins University School of Medicine
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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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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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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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Maslej MM, Kloiber S, Ghassemi M, Yu J, Hill SL. Out with AI, in with the psychiatrist: a preference for human-derived clinical decision support in depression care. Transl Psychiatry 2023; 13:210. [PMID: 37328465 DOI: 10.1038/s41398-023-02509-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/27/2023] [Accepted: 06/02/2023] [Indexed: 06/18/2023] Open
Abstract
Advancements in artificial intelligence (AI) are enabling the development of clinical support tools (CSTs) in psychiatry to facilitate the review of patient data and inform clinical care. To promote their successful integration and prevent over-reliance, it is important to understand how psychiatrists will respond to information provided by AI-based CSTs, particularly if it is incorrect. We conducted an experiment to examine psychiatrists' perceptions of AI-based CSTs for treating major depressive disorder (MDD) and to determine whether perceptions interacted with the quality of CST information. Eighty-three psychiatrists read clinical notes about a hypothetical patient with MDD and reviewed two CSTs embedded within a single dashboard: the note's summary and a treatment recommendation. Psychiatrists were randomised to believe the source of CSTs was either AI or another psychiatrist, and across four notes, CSTs provided either correct or incorrect information. Psychiatrists rated the CSTs on various attributes. Ratings for note summaries were less favourable when psychiatrists believed the notes were generated with AI as compared to another psychiatrist, regardless of whether the notes provided correct or incorrect information. A smaller preference for psychiatrist-generated information emerged in ratings of attributes that reflected the summary's accuracy or its inclusion of important information from the full clinical note. Ratings for treatment recommendations were also less favourable when their perceived source was AI, but only when recommendations were correct. There was little evidence that clinical expertise or familiarity with AI impacted results. These findings suggest that psychiatrists prefer human-derived CSTs. This preference was less pronounced for ratings that may have prompted a deeper review of CST information (i.e. a comparison with the full clinical note to evaluate the summary's accuracy or completeness, assessing an incorrect treatment recommendation), suggesting a role of heuristics. Future work should explore other contributing factors and downstream implications for integrating AI into psychiatric care.
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Affiliation(s)
- Marta M Maslej
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Stefan Kloiber
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Marzyeh Ghassemi
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Joanna Yu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Sean L Hill
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
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Patient Perspectives on Artificial Intelligence in Healthcare Decision Making: A Multi-Center Comparative Study. Indian J Orthop 2023; 57:653-665. [PMID: 37122674 PMCID: PMC9979110 DOI: 10.1007/s43465-023-00845-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/10/2023] [Indexed: 03/06/2023]
Abstract
Objective Investigate the patient opinion on the use of Artificial Intelligence (AI) in Orthopaedics. Methods 397 orthopaedic patients from a large urban academic center and a rural health system completed a 37-component survey querying patient demographics and perspectives on clinical scenarios involving AI. An average comfort score was calculated from thirteen Likert-scale questions (1, not comfortable; 10, very comfortable). Secondary outcomes requested a binary opinion on whether it is acceptable for patient healthcare data to be used to create AI (yes/no) and the impact of AI on: orthopaedic care (positive/negative); healthcare cost (increase/decrease); and their decision to refuse healthcare if cost increased (yes/no). Bivariate and multivariable analyses were employed to identify characteristics that impacted patient perspectives. Results The average comfort score across the population was 6.4, with significant bivariate differences between age (p = 0.0086), gender (p = 0.0001), education (p = 0.0029), experience with AI/ML (p < 0.0001), survey format (p < 0.0001), and four binary outcomes (p < 0.05). When controlling for age and education, multivariable regression identified significant relationships between comfort score and experience with AI/ML (p = 0.0018) and each of the four binary outcomes (p < 0.05). In the final multivariable model gender, survey format, perceived impact of AI on orthopaedic care, and the decision to refuse care if it were to increase cost remained significantly associated with the average AI comfort score (p < 0.05). Additionally, patients were not comfortable undergoing surgery entirely by a robot with distant physician supervision compared to close supervision. Conclusion The orthopaedic patient appears comfortable with AI joining the care team.
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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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Mlodzinski E, Wardi G, Viglione C, Nemati S, Crotty Alexander L, Malhotra A. Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence-Based Tools in Critical Care: Web-Based Survey Study. JMIR Perioper Med 2023; 6:e41056. [PMID: 36705960 PMCID: PMC10013679 DOI: 10.2196/41056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/17/2022] [Accepted: 12/23/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Although there is considerable interest in machine learning (ML) and artificial intelligence (AI) in critical care, the implementation of effective algorithms into practice has been limited. OBJECTIVE We sought to understand physician perspectives of a novel intubation prediction tool. Further, we sought to understand health care provider and nonprovider perspectives on the use of ML in health care. We aim to use the data gathered to elucidate implementation barriers and determinants of this intubation prediction tool, as well as ML/AI-based algorithms in critical care and health care in general. METHODS We developed 2 anonymous surveys in Qualtrics, 1 single-center survey distributed to 99 critical care physicians via email, and 1 social media survey distributed via Facebook and Twitter with branching logic to tailor questions for providers and nonproviders. The surveys included a mixture of categorical, Likert scale, and free-text items. Likert scale means with SD were reported from 1 to 5. We used student t tests to examine the differences between groups. In addition, Likert scale responses were converted into 3 categories, and percentage values were reported in order to demonstrate the distribution of responses. Qualitative free-text responses were reviewed by a member of the study team to determine validity, and content analysis was performed to determine common themes in responses. RESULTS Out of 99 critical care physicians, 47 (48%) completed the single-center survey. Perceived knowledge of ML was low with a mean Likert score of 2.4 out of 5 (SD 0.96), with 7.5% of respondents rating their knowledge as a 4 or 5. The willingness to use the ML-based algorithm was 3.32 out of 5 (SD 0.95), with 75% of respondents answering 3 out of 5. The social media survey had 770 total responses with 605 (79%) providers and 165 (21%) nonproviders. We found no difference in providers' perceived knowledge based on level of experience in either survey. We found that nonproviders had significantly less perceived knowledge of ML (mean 3.04 out of 5, SD 1.53 vs mean 3.43, SD 0.941; P<.001) and comfort with ML (mean 3.28 out of 5, SD 1.02 vs mean 3.53, SD 0.935; P=.004) than providers. Free-text responses revealed multiple shared concerns, including accuracy/reliability, data bias, patient safety, and privacy/security risks. CONCLUSIONS These data suggest that providers and nonproviders have positive perceptions of ML-based tools, and that a tool to predict the need for intubation would be of interest to critical care providers. There were many shared concerns about ML/AI in health care elucidated by the surveys. These results provide a baseline evaluation of implementation barriers and determinants of ML/AI-based tools that will be important in their optimal implementation and adoption in the critical care setting and health care in general.
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Affiliation(s)
- Eric Mlodzinski
- Division of Pulmonary, Critical Care, Sleep and Physiology, University of California, San Diego, CA, United States
| | - Gabriel Wardi
- Division of Pulmonary, Critical Care, Sleep and Physiology, University of California, San Diego, CA, United States
- Department of Emergency Medicine, University of California, San Diego, CA, United States
| | - Clare Viglione
- Dissemination and Implementation Science Center, Altman Clinical & Translational Research Institute, University of California, San Diego, CA, United States
| | - Shamim Nemati
- Department of Biomedical Informatics, University of California, San Diego, CA, United States
| | - Laura Crotty Alexander
- Division of Pulmonary, Critical Care, Sleep and Physiology, University of California, San Diego, CA, United States
- Section of Pulmonary and Critical Care, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States
| | - Atul Malhotra
- Division of Pulmonary, Critical Care, Sleep and Physiology, University of California, San Diego, CA, United States
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York TJ, Raj S, Ashdown T, Jones G. Clinician and computer: a study on doctors' perceptions of artificial intelligence in skeletal radiography. BMC MEDICAL EDUCATION 2023; 23:16. [PMID: 36627640 PMCID: PMC9830124 DOI: 10.1186/s12909-022-03976-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Traumatic musculoskeletal injuries are a common presentation to emergency care, the first-line investigation often being plain radiography. The interpretation of this imaging frequently falls to less experienced clinicians despite well-established challenges in reporting. This study presents novel data of clinicians' confidence in interpreting trauma radiographs, their perception of AI in healthcare, and their support for the development of systems applied to skeletal radiography. METHODS A novel questionnaire was distributed through a network of collaborators to clinicians across the Southeast of England. Over a three-month period, responses were compiled into a database before undergoing statistical review. RESULTS The responses of 297 participants were included. The mean self-assessed knowledge of AI in healthcare was 3.68 out of ten, with significantly higher knowledge reported by the most senior doctors (Specialty Trainee/Specialty Registrar or above = 4.88). 13.8% of participants reported an awareness of AI in their clinical practice. Overall, participants indicated substantial favourability towards AI in healthcare (7.87) and in AI applied to skeletal radiography (7.75). There was a preference for a hypothetical system indicating positive findings rather than ruling as negative (7.26 vs 6.20). CONCLUSIONS This study identifies clear support, amongst a cross section of student and qualified doctors, for both the general use of AI technology in healthcare and in its application to skeletal radiography for trauma. The development of systems to address this demand appear well founded and popular. The engagement of a small but reticent minority should be sought, along with improving the wider education of doctors on AI.
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Affiliation(s)
- Thomas James York
- Alexander Fleming Building, Imperial College London, South Kensington Campus, London, UK.
| | | | | | - Gareth Jones
- Imperial College Healthcare NHS Trust, London, UK
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Singapore radiographers' perceptions and expectations of artificial intelligence - A qualitative study. J Med Imaging Radiat Sci 2022; 53:554-563. [PMID: 36115823 DOI: 10.1016/j.jmir.2022.08.005] [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: 05/23/2022] [Revised: 08/03/2022] [Accepted: 08/11/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION With the emergence of artificial intelligence (AI) in medical imaging, radiographers are likely to be at the forefront of this technological advancement. Studies have therefore been conducted recently to understand radiographers' opinions on AI adoption. This study extends that work by using a qualitative approach to further explore radiographers' knowledge, perceptions, and expectations of AI. METHOD Six online focus groups were conducted with 22 radiographers from the three public healthcare clusters in Singapore. They were purposively sampled, and participants were recruited from a broad demographic background with varying years of working experience and designations. The focus group sessions were transcribed verbatim and thematic analysis was performed on their responses. RESULTS Participants demonstrated limited knowledge of AI. Their perceptions of AI were mixed, recognising its benefits in increasing efficiency and improving patient care, but also aware of its limitations in accuracy and bias. On how patients may perceive AI, participants felt that patients would accept AI if they felt it improves their care but may reject it once they lose trust in it. Expectations wise, participants envisioned several applications in pre-, peri‑, and post-procedural workflows including order vetting, patient positioning, language translation, and artefact removal. On radiographers' role and career opportunities, some participants see an opportunity for radiographers to specialise in AI, becoming involved in algorithm development and its clinical implementation. DISCUSSION Our findings suggest that widespread implementation of AI would require limited knowledge amongst radiographers and current AI limitations to be addressed. While radiographers are positively anticipating the integration of AI into their practices, they should also become actively involved in the development of AI tools such that those they envisioned. This would help align optimal use of AI tools and radiographer role changes. Patients' acceptance and reactions to AI also warrant further research.
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Kosan E, Krois J, Wingenfeld K, Deuter CE, Gaudin R, Schwendicke F. Patients' Perspectives on Artificial Intelligence in Dentistry: A Controlled Study. J Clin Med 2022; 11:jcm11082143. [PMID: 35456236 PMCID: PMC9032784 DOI: 10.3390/jcm11082143] [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: 03/14/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 01/31/2023] Open
Abstract
Background: As artificial intelligence (AI) becomes increasingly important in modern dentistry, we aimed to assess patients’ perspectives on AI in dentistry specifically for radiographic caries detection and the impact of AI-based diagnosis on patients’ trust. Methods: Validated questionnaires with Likert-scale batteries (1: “strongly disagree” to 5: “strongly agree”) were used to query participants’ experiences with dental radiographs and their knowledge/attitudes towards AI as well as to assess how AI-based communication of a diagnosis impacted their trust, belief, and understanding. Analyses of variance and ordinal logistic regression (OLR) were used (p < 0.05). Results: Patients were convinced that “AI is useful” (mean Likert ± standard deviation 4.2 ± 0.8) and did not fear AI in general (2.2 ± 1.0) nor in dentistry (1.6 ± 0.8). Age, education, and employment status were significantly associated with patients’ attitudes towards AI for dental diagnostics. When shown a radiograph with a caries lesion highlighted by an arrow, patients recognized the lesion significantly less often than when using AI-generated coloured overlays highlighting the lesion (p < 0.0005). AI-based communication did not significantly affect patients’ trust in dentists’ diagnosis (p = 0.44; OLR). Conclusions: Patients showed a positive attitude towards AI in dentistry. AI-supported diagnostics may assist communicating radiographic findings by increasing patients’ ability to recognize caries lesions on dental radiographs.
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Affiliation(s)
- Esra Kosan
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral Diagnostics and Digital Health and Health Services Research, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (J.K.); (F.S.)
- Correspondence: ; Tel.: +49-030-45066-2248
| | - Joachim Krois
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral Diagnostics and Digital Health and Health Services Research, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (J.K.); (F.S.)
| | - Katja Wingenfeld
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Hindenburgdamm 30, 12200 Berlin, Germany; (K.W.); (C.E.D.)
| | - Christian Eric Deuter
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Hindenburgdamm 30, 12200 Berlin, Germany; (K.W.); (C.E.D.)
| | - Robert Gaudin
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12200 Berlin, Germany;
| | - Falk Schwendicke
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral Diagnostics and Digital Health and Health Services Research, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (J.K.); (F.S.)
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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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Musbahi O, Syed L, Le Feuvre P, Cobb J, Jones G. Public patient views of artificial intelligence in healthcare: A nominal group technique study. Digit Health 2021; 7:20552076211063682. [PMID: 34950499 PMCID: PMC8689636 DOI: 10.1177/20552076211063682] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Objectives The beliefs of laypeople and medical professionals often diverge with regards to disease, and technology has had a positive impact on how research is conducted. Surprisingly, given the expanding worldwide funding and research into Artificial Intelligence (AI) applications in healthcare, there is a paucity of research exploring the public patient perspective on this technology. Our study sets out to address this knowledge gap, by applying the Nominal Group Technique (NGT) to explore patient public views on AI. Methods A Nominal Group Technique (NGT) was used involving four study groups with seven participants in each group. This started with a silent generation of ideas regarding the benefits and concerns of AI in Healthcare. Then a group discussion and round-robin process were conducted until no new ideas were generated. Participants ranked their top five benefits and top five concerns regarding the use of AI in healthcare. A final group consensus was reached. Results Twenty-Eight participants were recruited with the mean age of 47 years. The top five benefits were: Faster health services, Greater accuracy in management, AI systems available 24/7, reducing workforce burden, and equality in healthcare decision making. The top five concerns were: Data cybersecurity, bias and quality of AI data, less human interaction, algorithm errors and responsibility, and limitation in technology. Conclusion This is the first formal qualitative study exploring patient public views on the use of AI in healthcare, and highlights that there is a clear understanding of the potential benefits delivered by this technology. Greater patient public group involvement, and a strong regulatory framework is recommended.
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Affiliation(s)
- Omar Musbahi
- MSK Lab, Imperial College London, Charing Cross Campus, Hammersmith, London, UK
| | - Labib Syed
- MSK Lab, Imperial College London, Charing Cross Campus, Hammersmith, London, UK
| | - Peter Le Feuvre
- MSK Lab, Imperial College London, Charing Cross Campus, Hammersmith, London, UK
| | - Justin Cobb
- MSK Lab, Imperial College London, Charing Cross Campus, Hammersmith, London, UK
| | - Gareth Jones
- MSK Lab, Imperial College London, Charing Cross Campus, Hammersmith, London, UK
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Ploug T, Sundby A, Moeslund TB, Holm S. Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey. J Med Internet Res 2021; 23:e26611. [PMID: 34898454 PMCID: PMC8713089 DOI: 10.2196/26611] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 05/31/2021] [Accepted: 11/11/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public's interests in such features of AI. OBJECTIVE This study elicited the public's preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI. METHODS We conducted a choice-based conjoint survey of public preferences for attributes of AI decision making in health care in a representative sample of the adult Danish population. Initial focus group interviews yielded 6 attributes playing a role in the respondents' views on the use of AI decision support in health care: (1) type of AI decision, (2) level of explanation, (3) performance/accuracy, (4) responsibility for the final decision, (5) possibility of discrimination, and (6) severity of the disease to which the AI is applied. In total, 100 unique choice sets were developed using fractional factorial design. In a 12-task survey, respondents were asked about their preference for AI system use in hospitals in relation to 3 different scenarios. RESULTS Of the 1678 potential respondents, 1027 (61.2%) participated. The respondents consider the physician having the final responsibility for treatment decisions the most important attribute, with 46.8% of the total weight of attributes, followed by explainability of the decision (27.3%) and whether the system has been tested for discrimination (14.8%). Other factors, such as gender, age, level of education, whether respondents live rurally or in towns, respondents' trust in health and technology, and respondents' fears and hopes regarding AI, do not play a significant role in the majority of cases. CONCLUSIONS The 3 factors that are most important to the public are, in descending order of importance, (1) that physicians are ultimately responsible for diagnostics and treatment planning, (2) that the AI decision support is explainable, and (3) that the AI system has been tested for discrimination. Public policy on AI system use in health care should give priority to such AI system use and ensure that patients are provided with information.
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Affiliation(s)
- Thomas Ploug
- Department of Communication and Psychology, Aalborg University, Copenhagen, Denmark
| | - Anna Sundby
- Department of Communication and Psychology, Aalborg University, Copenhagen, Denmark
| | - Thomas B Moeslund
- Visual Analysis and Perception Lab, Aalborg University, Aalborg, Denmark
| | - Søren Holm
- Centre for Social Ethics and Policy, University of Manchester, Manchester, United Kingdom
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Langer M, Landers RN. The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. COMPUTERS IN HUMAN BEHAVIOR 2021. [DOI: 10.1016/j.chb.2021.106878] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Weisberg EM, Chu LC, Fishman EK. Using Artificial Intelligence to Interpret CT Scans: Getting Closer to Standard of Care. J Am Coll Radiol 2021; 18:1569-1571. [PMID: 34147504 DOI: 10.1016/j.jacr.2021.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/28/2021] [Accepted: 05/24/2021] [Indexed: 11/30/2022]
Affiliation(s)
| | | | - Elliot K Fishman
- Director, Diagnostic Imaging and Body Computed Tomography, Johns Hopkins Medicine, Baltimore, Maryland; Johns Hopkins Kimmel Cancer Center, Johns Hopkins Medicine, Baltimore, Maryland
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