Investigating the Barriers to Physician Adoption of an Artificial Intelligence- Based Decision Support System in Emergency Care: An Interpretative Qualitative Study
Open accessAbstract
The development of artificial intelligence (AI) systems to support diagnostic decision-making is rapidly expanding in health care. However, important challenges remain in executing algorithmic systems at the frontlines of clinical practice. Hence, most often, these systems have not been trained with local data nor do they fit with context-specific patterns of care. This research examines the implementation of an AI-based decision support system (DSS) in the emergency department of a large Academic Health Center (AHC) in Canada, focusing specifically on the question of end-user adoption. Based in an interpretative perspective, the study analyzes the perceptions of healthcare managers, AI developers, physicians and nurses on the DSS, so as to make sense of the main barriers to its adoption by emergency physicians. The study points to the importance of considering interconnections between technical, human and organizational factors to better grasp the unique challenges raised by AI systems in health care. It further emphasizes the need to investigate actors' perceptions of AI in order to develop strategies to adequately test and adapt AI systems, and ensure that they meet the needs of health professionals and patients. This research is particularly relevant at a time when considerable investments are being made to develop and deploy AI-based systems in health care. Empirically probing the conditions under which AI-based systems can effectively be integrated into processes and workflow is essential for maximizing the benefits these investments can bring to the organization and delivery of care.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In some studies, patients reported the need to assess the usefulness of the AI before using it and had concerns around the quality and accuracy of the output, thereby questioning the value of AI as a whole.
“In some studies, patients reported the need to assess the usefulness of the AI before using it and had concerns around the quality and accuracy of the output, thereby questioning the value of AI as a whole [39,42,52-55].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [54]
Lack of accuracy, doubts about unsafe results, privacy breaches, patients' perceptions and acceptance of automation, and uncertainty about developers' reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust.
“Lack of accuracy; doubts about unsafe results; privacy breaches; patients’ perceptions and acceptance of automation; and uncertainty about developers’ reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust [39,42,47,50,53,54,69,70].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [54]
Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · cited 1× · see citing works
“AI systems have already proven more effective than dermatologists at diagnosing skin cancer [5], and experts believe they will outperform medical specialists in surgery by 2050 [6].”
BackgroundHigh-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · cited 1× · see citing works
“concerns about data privacy, algorithm opacity and patient safety; lack of ethical and legal frameworks to provide safeguards against inappropriate use) [11-14].”
BackgroundStrategies for Theorizing from Process Data
Langley, A. (1999) · The Academy of Management Review · doi:10.5465/amr.1999.2553248 · Cited by 7,008 · cited 1× · see citing works
“For data analysis, a description of the implementation process was first completed using a narrative strategy [16] that highlighted key timelines and activities.”
MethodArtificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · cited 1× · see citing works
“As a result, health care is often presented as one of the most propitious domains in the upcoming "AI revolution" [7][8].”
BackgroundDeo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466 · see citing works
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246 · see citing works
The Inevitable Application of Big Data to Health Care
Murdoch, T. B., & Detsky, A. S. (2013) · JAMA · doi:10.1001/jama.2013.393 · Cited by 1,676 · see citing works
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
Tang, A., Tam, R. C., Cadrin-Chênevert, A., Guest, W. C., Chong, J., Barfett, J., Chepelev, L. L., Cairns, R., Mitchell, J. R., Cicero, M. D., et al. (2018) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2018.02.002 · Cited by 524 · cited 1× · see citing works
“concerns about data privacy, algorithm opacity and patient safety; lack of ethical and legal frameworks to provide safeguards against inappropriate use) [11-14].”
BackgroundThe “inconvenient truth” about AI in healthcare
Panch, T., Mattie, H., & Celi, L. A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0155-4 · Cited by 473 · cited 2× · see citing works
“Moreover, the research emphasizes the need to consider the unique challenges raised by AI integration into clinical processes and workflow [10].”
BackgroundArtificial intelligence powers digital medicine
Fogel, A. L., & Kvedar, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0012-2 · Cited by 461 · cited 2× · see citing works
“defined as the imitation of human cognition by a machine [1], is expected to enable better surveillance, detection and diagnosis of illnesses, as well as uncover novel treatments to spur precision medicine [2-4].”
BackgroundThe role of artificial intelligence in precision medicine
Mesko, B. (2017) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2017.1380516 · Cited by 343 · cited 1× · see citing works
“defined as the imitation of human cognition by a machine [1], is expected to enable better surveillance, detection and diagnosis of illnesses, as well as uncover novel treatments to spur precision medicine [2-4].”
BackgroundEthical dimensions of using artificial intelligence in health care
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.121 · Cited by 328 · cited 1× · see citing works
“concerns about data privacy, algorithm opacity and patient safety; lack of ethical and legal frameworks to provide safeguards against inappropriate use) [11-14].”
BackgroundThe legal and ethical concerns that arise from using complex predictive analytics in health care
Cohen, I. G., Amarasingham, R., Shah, A., Xie, B., & Lo, B. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0048 · Cited by 300 · cited 1× · see citing works
“concerns about data privacy, algorithm opacity and patient safety; lack of ethical and legal frameworks to provide safeguards against inappropriate use) [11-14].”
BackgroundWhen Will AI Exceed Human Performance? Evidence from AI Experts
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2017) · arXiv · doi:10.48550/arxiv.1705.08807 · Cited by 285 · cited 1× · see citing works
“AI systems have already proven more effective than dermatologists at diagnosing skin cancer [5], and experts believe they will outperform medical specialists in surgery by 2050 [6].”
BackgroundAn evaluation of machine-learning methods for predicting pneumonia mortality
Cooper, G. F., Aliferis, C., Ambrosino, R., Aronis, J. M., Buchanan, B. G., Caruana, R. A., Fine, M. J., Glymour, C., Gordon, G. J., Hanusa, B. H., et al. (1997) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(96)00367-3 · Cited by 211 · see citing works
Flagged first, then by citations
1 of the 16 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., Tyskbo, D., & Svedberg, P. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08215-8 · Cited by 434 · see citing works
“’s study [45] serves as a notable exception.”
BackgroundLambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stephan, A. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00852-5 · Cited by 404 · see citing works
A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare
Ahmed, M. I., Spooner, B., Isherwood, J., Lane, M., Orrock, E., & Dennison, A. (2023) · Cureus · doi:10.7759/cureus.46454 · Cited by 364 · see citing works
“These models are yet to be integrated into clinical workflows in a fashion that improves clinical care or outcomes [44] and there remains a divide between those using and those implementing the AI models providing a significant challenge for those responsible for the model when it is used…”
BackgroundBarriers to and facilitators of artificial intelligence adoption in health care: scoping review
Hassan, M., Kushniruk, A., & Borycki, E. (2024) · JMIR Human Factors · doi:10.2196/48633 · Cited by 324 · see citing works
“Lack of accuracy; doubts about unsafe results; privacy breaches; patients’ perceptions and acceptance of automation; and uncertainty about developers’ reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust [39,42,47,50,53,54,69,70].”
BackgroundChen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.990604 · Cited by 258 · see citing works
Artificial Intelligence Applications in Health Care Practice: Scoping Review
Sharma, M., Savage, C., Nair, M., Larsson, I., Svedberg, P., & Nygren, J. M. (2022) · Journal of Medical Internet Research · doi:10.2196/40238 · Cited by 198 · see citing works
Nair, M., Svedberg, P., Larsson, I., & Nygren, J. M. (2024) · PLOS ONE · doi:10.1371/journal.pone.0305949 · Cited by 179 · see citing works
Hogg, H. D. J., Al-Zubaidy, M., Technology Enhanced Macular Services Study Reference Group, Talks, J., Denniston, A. K., Kelly, C. J., Malawana, J., Papoutsi, C., Teare, M. D., Keane, P. A., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/39742 · Cited by 163 · see citing works
Meskó, B. (2023) · Journal of Medical Internet Research · doi:10.2196/48392 · Cited by 156 · see citing works
“No matter how many studies, clinical trials, regulatory approvals, and product announcements keep on becoming available about AI’s role in medicine and health care, adoption rates have been lagging behind [13,14].”
Chomutare, T., Tejedor, M., Svenning, T. O., Marco-Ruiz, L., Tayefi, M., Lind, K., Godtliebsen, F., Moen, A., Ismail, L., Makhlysheva, A., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192316359 · Cited by 156 · see citing works
Abell, B., Naicker, S., Rodwell, D., Donovan, T., Tariq, A., Baysari, M., Blythe, R., Parsons, R., & McPhail, S. M. (2023) · Implementation Science · doi:10.1186/s13012-023-01287-y · Cited by 131 · see citing works
“For example, in one study [65], CDSS use was hindered by having limited available service options in the community for onward patient care after a clinical decision was recommended.”
BackgroundKhanijahani, A., Iezadi, S., Dudley, S., Goettler, M., Kroetsch, P., & Wise, J. (2022) · Health Policy and Technology · doi:10.1016/j.hlpt.2022.100602 · Cited by 106 · see citing works
Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., Vu, T. M. T., Nguyen, S. H., Tran, B. X., Latkin, C. A., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.755644 · Cited by 87 · see citing works
Enhancing trust in AI through industry self-governance.
Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab065 · Cited by 83 · see citing works
“…Data transparency & reporting 32,37,40,42–46 • Model provenance records 40 • Equitable/diverse workforce • Organizational implementation 38,46,47,56,57 • Organizational governance 13,47,58,59 • Promote “human in the loop” practices 60,61 PHASE 4: Maintenance • Lack of algorithmic accountability…”
BackgroundPedro, A. R., Dias, M. B., Laranjo, L., Cunha, A. S., & Cordeiro, J. V. (2023) · PLOS ONE · doi:10.1371/journal.pone.0290613 · Cited by 74 · see citing works
Zając, H. D., Li, D., Dai, X., Carlsen, J. F., Kensing, F., & Andersen, T. O. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3582430 · Cited by 76 · see citing works
“End-users: Physicians [24, 45, 64, 86], Nurses [106] Other stakeholders: Physicians, Other Medical Professionals, Other, IT specialists [106]”
BackgroundMethodResultInfluentialBotha, N. N., Segbedzi, C. E., Dumahasi, V. K., Maneen, S., Kodom, R. V., Tsedze, I. S., Akoto, L. A., Atsu, F. S., Lasim, O. U., & Ansah, E. W. (2024) · Archives of Public Health · doi:10.1186/s13690-024-01414-1 · Cited by 75 · see citing works
“Similar concerns were found in a study that determined the impact of AI on public health practices [72], and another study that explored the views of patients about various AI applications in healthcare [87].”
ResultJesso, S. T., Kelliher, A., Sanghavi, H. K., Martin, T., & Parker, S. H. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.830345 · Cited by 59 · see citing works
Liao, X., Yao, C., Jin, F., Zhang, J., & Liu, L. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-084398 · Cited by 50 · see citing works
Choi, A., Choi, S. Y., Chung, K. S., Chung, H. S., Song, T., Choi, B., & Kim, J. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-35617-3 · Cited by 55 · see citing works
“However, previous ML-based predictive models have not been adequately tailored for context-specific patterns of care 37 .”
MethodBarriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea
Brennan, H. L., & Kirby, S. D. (2022) · Journal of Otolaryngology - Head & Neck Surgery · doi:10.1186/s40463-022-00566-w · Cited by 39 · see citing works
“” [53] Many of the ANNs that have been developed to detect OSA have required data from patient questionnaires reporting on their snoring status [38].”
BackgroundLi, L. T., Haley, L. C., Boyd, A. K., & Bernstam, E. V. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104531 · Cited by 45 · see citing works
Impact of artificial intelligence on pathologists’ decisions: an experiment
Meyer, J. M., Khademi, A., Têtu, B., Han, W., Nippak, P. M. D., & Remisch, D. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac103 · Cited by 36 · see citing works
Attitudes towards artificial intelligence in emergency medicine
Stewart, J. E., Freeman, S., Eroglu, E., Dumitrascu, N., Lü, J., Goudie, A., Sprivulis, P., Akhlaghi, H., Tran, V., Sanfilippo, F. M., et al. (2023) · Emergency Medicine Australasia · doi:10.1111/1742-6723.14345 · Cited by 36 · see citing works
Samhammer, D., Roller, R., Hummel, P., Osmanodja, B., Burchardt, A., Mayrdorfer, M., Duettmann, W., & Dabrock, P. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.1016366 · Cited by 33 · see citing works
“Thus it is important to take into account the given processes and workflows in which clinical decision-making takes place (15).”
BackgroundEvans, R. P., Bryant, L. D., Russell, G., & Absolom, K. L. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105342 · Cited by 38 · see citing works
Nair, M., Nygren, J., Nilsén, P., Gama, F., Neher, M., Larsson, I., & Svedberg, P. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1550459 · Cited by 38 · see citing works
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Gupta, V., Erdal, B. S., Ramirez, C., Floca, R. O., Genereaux, B. W., Bryson, S., Bridge, C. P., Kleesiek, J., Nensa, F., Braren, R., et al. (2024) · JMIR AI · doi:10.2196/55833 · Cited by 35 · see citing works
Nair, A., Ong, W., Lee, A., Leow, N. W., Makmur, A., Ting, Y. H., Lee, Y. J., Ong, S. J., Tan, J. J. H., Kumar, N., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15091146 · Cited by 35 · see citing works
“Additionally, defining clear roles, responsibilities, and liabilities for clinicians involved in AI-integrated workflows is crucial for smooth and effective implementation [143].”
Ekundayo, T., Khan, Z., & Nuzhat, S. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/8713718 · Cited by 36 · see citing works
Ed‐Driouch, C., Mars, F., Gourraud, P., & Dumas, C. (2022) · Sensors · doi:10.3390/s22218313 · Cited by 23 · see citing works
“Researchers have identified two main issues for the adoption of ML in healthcare systems [6]: technical concerns such as data quality and the ‘black box’ problem, and human concerns regarding the ethics, legality, and acceptability of ML.”
BackgroundScandiffio, J., Zhang, M., Karsan, I., Charow, R., Anderson, M. A., Salhia, M., & Wiljer, D. (2024) · Digital Health · doi:10.1177/20552076241238075 · Cited by 24 · see citing works
Usher, S. E., & Denis, J. (2022) · Patient Education and Counseling · doi:10.1016/j.pec.2022.04.001 · Cited by 19 · see citing works
Self-citationArtificial Intelligence: Opportunities and Challenges for Public Administration
David, G. (2024) · Canadian Public Administration · doi:10.1111/capa.12580 · Cited by 20 · see citing works
What is needed to mainstream artificial intelligence in health care?
Scott, I. A., Abdel‐Hafez, A., Barras, M. A., & Canaris, S. (2021) · Australian Health Review · doi:10.1071/ah21034 · Cited by 15 · see citing works
"It depends": Configuring AI to Improve Clinical Usefulness Across Contexts
Zając, H. D., Ribeiro, J., Ingala, S., Gentile, S., Wanjohi, R., Gitau, S. N., Carlsen, J. F., Nielsen, M. B., & Andersen, T. O. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660707 · Cited by 14 · see citing works
“Poor performance also leads to increased workload [57, 64, 82], additional time spent on discerning false positive predictions [67, 68], or breakages to work routines [31].”
BackgroundZhang, I. Y., Hart, G. R., Qin, B., & Deng, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-29167-x · Cited by 12 · see citing works
Zając, H. D., Andersen, T. O., Kwasa, E., Wanjohi, R., Onyinkwa, M. K., Mwaniki, E. K., Gitau, S. N., Yaseen, S. S., Carlsen, J. F., Fraccaro, M., et al. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3715115 · Cited by 12 · see citing works
“For example, in the Global North, Petitgand et al. [97] investigated the implementation of an AI-based Decision Support System (DSS) in an emergency department in a large hospital in Canada.”
Healthcare AI: A Revised Quebec Framework for Nursing Education
Lattuca, M., Maratta, D., Beffert, U., Chevrier, A. S., & Winer, L. R. (2023) · Quality Advancement in Nursing Education - Avancées en formation infirmière · doi:10.17483/2368-6669.1408 · Cited by 9 · see citing works
Navigating the barriers and facilitators to implementation of AI in healthcare
Khattak, M., Bowness, J. S., Yonis, R., Kierkegaard, P., McGregor, A. H., & Perry, D. C. (2025) · The Bone & Joint Journal · doi:10.1302/0301-620x.107b7.bjj-2024-1570.r1 · Cited by 8 · see citing works
Artificial Intelligence in Healthcare
Aggarwal, N., Matheny, M. E., Shachar, C., Wang, S. X. Y., & Israni, S. T. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.50 · Cited by 6 · see citing works
Digitalization and Artificial Intelligence as Motivators for Healthcare Professionals
Karaferis, D. C., Dimitra, B., & Pollalis, Y. (2025) · Japan Journal of Research · doi:10.33425/2690-8077.1170 · Cited by 9 · see citing works
Kotzé, A., Lawton, T., Howell, S. J., O'Driscoll, R., Odling-Smee, M. K., Shangguan, L., Johnson, O. A., & Wong, D. (2025) · Anaesthesia · doi:10.1111/anae.16777 · Cited by 6 · see citing works
“There is a lack of integration between models and clinical systems [14, 15].”
Lacan, L., Betrouni, N., Chaton, L., et al. (2024) · Clinical Neurophysiology · doi:10.1016/j.clinph.2024.07.015 · Cited by 4 · see citing works
Artificial Intelligence in Cardiac Electrophysiology: Enhancing Mapping and Ablation Precision
Tran, H. H.-V., Thu, A., Twayana, A. R., Fuertes, A., Gonzalez, M., Harrison, J. L., Mehta, K. A., James, M., Basta, M., Frishman, W. H., et al. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000001032 · Cited by 5 · see citing works
Chen, M., Zhang, B., Cai, Z., Seery, S., Mendez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4128867 · Cited by 2 · see citing works
Vigil, L., Zapata, T., Grau, A., Bonet, M., Montaña, M., & Piñar, M. (2024) · Open Respiratory Archives · doi:10.1016/j.opresp.2025.100402 · Cited by 2 · see citing works
Nurses’ Experiences Using AI in Clinical Practice: Systematic Review
Scott, A. J. S., Zhao, Q., Pan, J., Brown, B. C., & Dowding, D. (2026) · JMIR Nursing · doi:10.2196/91238 · Cited by 2 · see citing works
“A total of 11 studies addressed perceived ease of use [24-26, 28, 30, The nurses commented that their computer-printed charts were readable and understandable.”
InfluentialJust What the Doctor Ordered – Towards Design Principles for NLP-Based Systems in Healthcare
Braun, M., Aslan, A., Diesterhöft, T. O., Greve, M., Brendel, B., & Kolbe, L. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06516-3_14 · Cited by 1 · see citing works
L’intelligence hybride pour prédire l’évolution des maladies chroniques
Ed‐Driouch, C., Mars, F., Gourraud, P., & Dumas, C. (2022) · doi:10.1145/3502178.3529109 · Cited by 1 · see citing works
“…de l’activité de la SEP. Il est possible d’ajouter d’autres marqueurs s’ils sont présents dans la base de données, tels que : la cognition, l’humeur, la qualité de vie, etc. L’approche proposée vise à intégrer le médecin dans le processus de prédiction, plus précisément dans la phase de…”
Supervised Learning Algorithms
Nagaraj, A. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815179453123010005
Park, K., Kim, K., Kim, D., Kim, K., Seo, W.-Y., Shin, J.-M., Kim, J., & Kim, S. (2024) · doi:10.36227/techrxiv.171779115.51311145/v1
Lee, S. Y., & Tan, E. X. (2025) · International Conference on Information Technology in Asia (CITA) · doi:10.1109/cita66455.2025.11198744
“…operational research); (2) two moderate-bias studies offering supplementary insights (e.g., [19]’s case study; [7]’s conference paper); and (3) seven high-bias studies excluded from synthesis but referenced for context (e.g., [3]’s robot-focused review; [12]’s physician-centered qualitative work).”
Nurses’ perceptions of artificial intelligence use in clinical practice: a qualitative study
Karnjuš, I., & Žvanut, B. (2026) · Pielegniarstwo XXI wieku / Nursing in the 21st Century · doi:10.12923/pielxxiw-2026-0002 · Cited by 1 · see citing works
Okada, N., Nakanishi, H., Sakamoto, Y., Hirayama, S., Liu, C., Mitarai, S., Mitarai, S., Yamamoto, G., & Okamoto, K. (2026) · JMIR Medical Informatics · doi:10.2196/preprints.96996
Asryan, N. V. (2026) · Регион и мир / Region and the World · doi:10.58587/18292437-2026.2-219
Sociotechnical Challenges of Machine Learning in Healthcare and Social Welfare
Reinmund, T., Kunze, L., & Jirotka, M. D. (2026) · ACM Interactive Health Conference · doi:10.1145/3786579.3804917 · Cited by 2 · see citing works
“As a result, tool usage is reduced despite technical availability.”
Wang, Y., Hu, W., Zhang, X., Liao, H., Wagner, S. K., Wang, Y., Li, L., Chen, R., Zeng, X., Fan, A., et al. (2026) · Ophthalmology Science · doi:10.1016/j.xops.2026.101338
“191,192 Furthermore, ensuring intuitive and user-friendly user experience and user interface design is essential, as poorly optimized interfaces developed without end-user input can substantially impede adoption.”
Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services
Zhang, Z., Agbugba, V. F., Bai, E., Billings, S., Luo, X., & Adelgais, K. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00086
“However, most CDSS research has been conducted in comparatively stable, hospital-based settings (e.g., inpatient wards, radiology, primary care) [8], [9], with few studies looking into how AI-enabled support should be designed for time-critical EMS care [10], [11], [6], [12].”
Balazadeh, K., Nygren, J., Gama, F., da Silva, M. A. R., Ploug, T., & Svedberg, P. (2026) · Research Square · doi:10.21203/rs.3.rs-11085511/v1
- Authors
- Cécile, P., Aude, M., Jean-Louis, D., & Catherine, R.
- Venue
- Studies in Health Technology and Informatics
- Publisher
- IOS Press
- Published
- 2020
- Volume
- 270
- Pages
- 1001-1005
- ISSN
- 0926-9630 (ISSN-L)
- OpenAlex
- W3036620451
- Cited by
- 62
- References
- 16
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 14 · 2023: 12 · 2022: 16 · 2021: 3
- FWCI
- 4.70
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Studies in health technology and informatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Keywords
- artificial intelligence, clinical decision support systems, emergency department, physician adoption, implementation barriers, emergency physicians, health managers, nurses, AI developers, perceptions of AI, clinical workflow integration, academic medical centers
- MeSH terms
- Artificial Intelligence, Canada, Delivery of Health Care, Emergency Medical Services, Humans, Qualitative Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
