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Wenderott K, Krups J, Zaruchas F, Weigl M. Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis. NPJ Digit Med 2024; 7:265. [PMID: 39349815 PMCID: PMC11442995 DOI: 10.1038/s41746-024-01248-9] [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] [Received: 04/03/2024] [Accepted: 08/31/2024] [Indexed: 10/04/2024] Open
Abstract
In healthcare, integration of artificial intelligence (AI) holds strong promise for facilitating clinicians' work, especially in clinical imaging. We aimed to assess the impact of AI implementation for medical imaging on efficiency in real-world clinical workflows and conducted a systematic review searching six medical databases. Two reviewers double-screened all records. Eligible records were evaluated for methodological quality. The outcomes of interest were workflow adaptation due to AI implementation, changes in time for tasks, and clinician workload. After screening 13,756 records, we identified 48 original studies to be incuded in the review. Thirty-three studies measured time for tasks, with 67% reporting reductions. Yet, three separate meta-analyses of 12 studies did not show significant effects after AI implementation. We identified five different workflows adapting to AI use. Most commonly, AI served as a secondary reader for detection tasks. Alternatively, AI was used as the primary reader for identifying positive cases, resulting in reorganizing worklists or issuing alerts. Only three studies scrutinized workload calculations based on the time saved through AI use. This systematic review and meta-analysis represents an assessment of the efficiency improvements offered by AI applications in real-world clinical imaging, predominantly revealing enhancements across the studies. However, considerable heterogeneity in available studies renders robust inferences regarding overall effectiveness in imaging tasks. Further work is needed on standardized reporting, evaluation of system integration, and real-world data collection to better understand the technological advances of AI in real-world healthcare workflows. Systematic review registration: Prospero ID CRD42022303439, International Registered Report Identifier (IRRID): RR2-10.2196/40485.
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Affiliation(s)
| | - Jim Krups
- Institute for Patient Safety, University Hospital Bonn, Bonn, Germany
| | - Fiona Zaruchas
- Institute for Patient Safety, University Hospital Bonn, Bonn, Germany
| | - Matthias Weigl
- Institute for Patient Safety, University Hospital Bonn, Bonn, Germany
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Ning J, Spielvogel CP, Haberl D, Trachtova K, Stoiber S, Rasul S, Bystry V, Wasinger G, Baltzer P, Gurnhofer E, Timelthaler G, Schlederer M, Papp L, Schachner H, Helbich T, Hartenbach M, Grubmüller B, Shariat SF, Hacker M, Haug A, Kenner L. A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study. Theranostics 2024; 14:4570-4581. [PMID: 39239512 PMCID: PMC11373617 DOI: 10.7150/thno.96921] [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: 04/03/2024] [Accepted: 07/10/2024] [Indexed: 09/07/2024] Open
Abstract
Purpose: This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to compare its performance with biopsy-proven GG (bxGG) assessment. Materials and Methods: A total of 146 patients with PCa recruited in a pilot study of a prospective clinical trial (NCT02659527) were retrospectively included in the side study, all of whom underwent 68Ga-PSMA-11 integrated positron emission tomography (PET) / magnetic resonance (MR) before radical prostatectomy (RP) between May 2014 and April 2020. To establish a multiomics ML model, we quantified PET radiomics features, pathway-level genomics features from whole exome sequencing, and pathomics features derived from immunohistochemical staining of 11 biomarkers. Based on the multiomics dataset, five ML models were established and validated using 100-fold Monte Carlo cross-validation. Results: Among five ML models, the random forest (RF) model performed best in terms of the area under the curve (AUC). Compared to bxGG assessment alone, the RF model was superior in terms of AUC (0.87 vs 0.75), specificity (0.72 vs 0.61), positive predictive value (0.79 vs 0.75), and accuracy (0.78 vs 0.77) and showed slightly decreased sensitivity (0.83 vs 0.89) and negative predictive value (0.80 vs 0.81). Among the feature categories, bxGG was identified as the most important feature, followed by pathomics, clinical, radiomics and genomics features. The three important individual features were bxGG, PSA staining and one intensity-related radiomics feature. Conclusion: The findings demonstrate a superior assessment of the developed multiomics-based ML model in whole-mount GG compared to the current clinical baseline of bxGG. This enables personalized patient management by identifying high-risk PCa patients for RP.
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Affiliation(s)
- Jing Ning
- Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria
- Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Clemens P Spielvogel
- Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - David Haberl
- Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Karolina Trachtova
- Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria
- Central European Institute of Technology, Masaryk University, Brno 62500, Czech Republic
| | - Stefan Stoiber
- Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria
- Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria
| | - Sazan Rasul
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Vojtech Bystry
- Central European Institute of Technology, Masaryk University, Brno 62500, Czech Republic
| | - Gabriel Wasinger
- Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria
| | - Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Elisabeth Gurnhofer
- Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria
| | - Gerald Timelthaler
- Center for Cancer Research, Medical University of Vienna, 1090 Vienna, Austria
| | - Michaela Schlederer
- Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Vienna, Austria
| | - Helga Schachner
- Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria
| | - Thomas Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Markus Hartenbach
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Bernhard Grubmüller
- Department of Urology, Medical University of Vienna, Vienna, Austria
- Working Group of Diagnostic Imaging in Urology, Austrian Society of Urology, Vienna, Austria
| | - Shahrokh F Shariat
- Department of Urology, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria
- Department of Urology, University of Texas Southwestern, Dallas, Texas
- Division of Medical Oncology, Department of Urology, Weill Medical College of Cornell University, New York, New York
- Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Alexander Haug
- Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Lukas Kenner
- Christian Doppler Laboratory for Applied Metabolomics, 1090 Vienna, Austria
- Clinical Institute of Pathology, Department for Experimental and Laboratory Animal Pathology, Medical University of Vienna, Vienna, Austria
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
- Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria
- Center for Biomarker Research in Medicine (CBmed), Graz, Styria, Austria
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Alami H, Lehoux P, Papoutsi C, Shaw SE, Fleet R, Fortin JP. Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre. BMC Health Serv Res 2024; 24:701. [PMID: 38831298 PMCID: PMC11149257 DOI: 10.1186/s12913-024-11112-x] [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: 02/03/2023] [Accepted: 05/14/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) technologies are expected to "revolutionise" healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital. METHODS Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. RESULTS Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients' digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors' priorities and the needs and expectations of healthcare organisations and systems. CONCLUSION Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.
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Affiliation(s)
- Hassane Alami
- Department of Health Management, Evaluation and Policy, School of Public Health, University of Montreal, P.O. Box 6128, Branch Centre-Ville, Montreal, QC, H3C 3J7, Canada.
- Center for Public Health Research of the University of Montreal, Montreal, QC, Canada.
- Institute for Data Valorization (IVADO), Montreal, QC, Canada.
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - Pascale Lehoux
- Department of Health Management, Evaluation and Policy, School of Public Health, University of Montreal, P.O. Box 6128, Branch Centre-Ville, Montreal, QC, H3C 3J7, Canada
- Center for Public Health Research of the University of Montreal, Montreal, QC, Canada
| | - Chrysanthi Papoutsi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sara E Shaw
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Fleet
- Faculty of Medicine, Laval University, Quebec, QC, Canada
- VITAM Research Centre on Sustainable Health, Faculty of Medicine, Laval University, Quebec, QC, Canada
| | - Jean-Paul Fortin
- Faculty of Medicine, Laval University, Quebec, QC, Canada
- VITAM Research Centre on Sustainable Health, Faculty of Medicine, Laval University, Quebec, QC, Canada
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Naderalvojoud B, Curtin CM, Yanover C, El-Hay T, Choi B, Park RW, Tabuenca JG, Reeve MP, Falconer T, Humphreys K, Asch SM, Hernandez-Boussard T. Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network. J Am Med Inform Assoc 2024; 31:1051-1061. [PMID: 38412331 PMCID: PMC11031239 DOI: 10.1093/jamia/ocae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/26/2024] [Accepted: 02/01/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. METHODS Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. RESULTS Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). CONCLUSIONS Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.
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Affiliation(s)
| | - Catherine M Curtin
- Department of Surgery, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, United States
| | - Chen Yanover
- KI Research Institute, Kfar Malal, 4592000, Israel
| | - Tal El-Hay
- KI Research Institute, Kfar Malal, 4592000, Israel
| | - Byungjin Choi
- Department of Biomedical Informatics, Ajou University Graduate School of Medicine, Suwon, 16499, Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University Graduate School of Medicine, Suwon, 16499, Korea
| | - Javier Gracia Tabuenca
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland
| | - Mary Pat Reeve
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States
| | - Keith Humphreys
- Department of Psychiatry and the Behavioral Sciences, Stanford University, Stanford, CA 94305, United States
- Center for Innovation to Implementation, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, United States
| | - Steven M Asch
- Department of Medicine, Stanford University, Stanford, CA 94305, United States
- Center for Innovation to Implementation, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, United States
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Shulha M, Hovdebo J, D'Souza V, Thibault F, Harmouche R. Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach. JMIR Form Res 2024; 8:e50475. [PMID: 38625728 PMCID: PMC11061789 DOI: 10.2196/50475] [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: 07/04/2023] [Revised: 01/26/2024] [Accepted: 02/19/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools. OBJECTIVE This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface. METHODS We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool. RESULTS Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity. CONCLUSIONS The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools.
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Affiliation(s)
- Michael Shulha
- Lady Davis Institute for Medical Research, Jewish General Hospital, Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Centre-Ouest-de-l'Île-de-Montréal, Montreal, QC, Canada
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Jordan Hovdebo
- National Research Council of Canada, Winnipeg, MB, Canada
| | - Vinita D'Souza
- Lady Davis Institute for Medical Research, Jewish General Hospital, Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Centre-Ouest-de-l'Île-de-Montréal, Montreal, QC, Canada
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | | | - Rola Harmouche
- National Research Council of Canada, Boucherville, QC, Canada
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Weik L, Fehring L, Mortsiefer A, Meister S. Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study. J Med Internet Res 2024; 26:e52085. [PMID: 38252468 PMCID: PMC10845021 DOI: 10.2196/52085] [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: 08/22/2023] [Revised: 11/18/2023] [Accepted: 12/16/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Various studies propose the significance of digital maturity in ensuring effective patient care and enabling improved health outcomes, a successful digital transformation, and optimized service delivery. Although previous research has centered around inpatient health care settings, research on digital maturity in general practices is still in its infancy. OBJECTIVE As general practitioners (GPs) are the first point of contact for most patients, we aimed to shed light on the pivotal role of GPs' inherent characteristics, especially their personality, in the digital maturity of general practices. METHODS In the first step, we applied a sequential mixed methods approach involving a literature review and expert interviews with GPs to construct the digital maturity scale used in this study. Next, we designed a web-based survey to assess digital maturity on a 5-point Likert-type scale and analyze the relationship with relevant inherent characteristics using ANOVAs and regression analysis. RESULTS Our web-based survey with 219 GPs revealed that digital maturity was overall moderate (mean 3.31, SD 0.64) and substantially associated with several characteristics inherent to the GP. We found differences in overall digital maturity based on GPs' gender, the expected future use of digital health solutions, the perceived digital affinity of medical assistants, GPs' level of digital affinity, and GPs' level of extraversion and neuroticism. In a regression model, a higher expected future use, a higher perceived digital affinity of medical assistants, a higher digital affinity of GPs, and lower neuroticism were substantial predictors of overall digital maturity. CONCLUSIONS Our study highlights the impact of GPs' inherent characteristics, especially their personality, on the digital maturity of general practices. By identifying these inherent influencing factors, our findings support targeted approaches to drive digital maturity in general practice settings.
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Affiliation(s)
- Lisa Weik
- Health Care Informatics, Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany
| | - Leonard Fehring
- Helios University Hospital Wuppertal, Department of Gastroenterology, Witten/Herdecke University, Wuppertal, Germany
- Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany
| | - Achim Mortsiefer
- General Practice II and Patient-Centredness in Primary Care, Institute of General Practice and Primary Care, Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany
| | - Sven Meister
- Health Care Informatics, Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany
- Department Healthcare, Fraunhofer Institute for Software and Systems Engineering ISST, Dortmund, Germany
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Gillner S. We're implementing AI now, so why not ask us what to do? - How AI providers perceive and navigate the spread of diagnostic AI in complex healthcare systems. Soc Sci Med 2024; 340:116442. [PMID: 38029666 DOI: 10.1016/j.socscimed.2023.116442] [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: 08/09/2023] [Revised: 10/14/2023] [Accepted: 11/17/2023] [Indexed: 12/01/2023]
Abstract
Despite high expectations of artificial intelligence (AI) in medical diagnostics, predictions of its extensive and rapid adoption have so far not been matched by reality. AI providers seeking to promote and perpetuate the use of this technology are faced with the complex reality of embedding AI-enabled diagnostics across variable implementation contexts. In this study, we draw upon a complexity science approach and qualitative methodology to understand how AI providers perceive and navigate the spread of AI in complex healthcare systems. Using semi-structured, one-to-one interviews, we collected qualitative data from 14 providers of AI-enabled diagnostics. We triangulated the data by complementing the interviews with multiple sources, including a focus group of physicians with experience using these technologies. The notion of embedding allowed us to connect local implementation efforts with systemic diffusion. Our study reveals that AI providers self-organise to increase their adaptability when navigating the variable conditions and unpredictability of complex healthcare contexts. In addition to the tensions perceived by AI providers within the sociocultural, technological, and institutional subsystems of healthcare, we illustrate the practices emerging among them to mitigate these tensions: stealth science, agility, and digital ambidexterity. Our study contributes to the growing body of literature on the spread of AI in healthcare by capturing the view of technology providers and adding a new theoretical perspective through the lens of complexity science.
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Affiliation(s)
- Sandra Gillner
- KPM Center for Public Management, University of Bern, Freiburgstr. 3, 3010, Bern, Switzerland; Swiss Institute for Translational and Entrepreneurial Medicine (sitem-insel), Freiburgstr. 3, 3010, Bern, Switzerland.
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Budiarto A, Tsang KCH, Wilson AM, Sheikh A, Shah SA. Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review. JMIR AI 2023; 2:e46717. [PMID: 38875586 PMCID: PMC11041490 DOI: 10.2196/46717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 09/28/2023] [Accepted: 10/09/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks. OBJECTIVE This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks. METHODS We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models' performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation. RESULTS Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting-based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated. CONCLUSIONS Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.
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Affiliation(s)
- Arif Budiarto
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Kevin C H Tsang
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M Wilson
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
- Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich, United Kingdom
| | - Aziz Sheikh
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Syed Ahmar Shah
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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Hogg HDJ, Al-Zubaidy M, Keane PA, Hughes G, Beyer FR, Maniatopoulos G. Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research. FRONTIERS IN HEALTH SERVICES 2023; 3:1161822. [PMID: 37492632 PMCID: PMC10364639 DOI: 10.3389/frhs.2023.1161822] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023]
Abstract
Introduction Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. Methods Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. Results Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). Conclusion TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science's translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the "AI chasm" continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice.
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Affiliation(s)
- H. D. J. Hogg
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- The Royal Victoria Infirmary, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - M. Al-Zubaidy
- The Royal Victoria Infirmary, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - P. A. Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - G. Hughes
- Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, United Kingdom
- University ofLeicester School of Business, University of Leicester, Leicester, United Kingdom
| | - F. R. Beyer
- Evidence Synthesis Group, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - G. Maniatopoulos
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- University ofLeicester School of Business, University of Leicester, Leicester, United Kingdom
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Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med 2023; 6:111. [PMID: 37301946 DOI: 10.1038/s41746-023-00852-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting. Forty-two articles met the inclusion criteria for this review. Pertinent elements to the study such as the type of AI, factors influencing acceptance, and the participants' profession were extracted from the included studies, and the studies were appraised for their quality. The data extraction and results were presented according to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The included studies revealed a variety of facilitating and hindering factors for AI acceptance in the hospital setting. Clinical decision support systems (CDSS) were the AI form included in most studies (n = 21). Heterogeneous results with regard to the perceptions of the effects of AI on error occurrence, alert sensitivity and timely resources were reported. In contrast, fear of a loss of (professional) autonomy and difficulties in integrating AI into clinical workflows were unanimously reported to be hindering factors. On the other hand, training for the use of AI facilitated acceptance. Heterogeneous results may be explained by differences in the application and functioning of the different AI systems as well as inter-professional and interdisciplinary disparities. To conclude, in order to facilitate acceptance of AI among healthcare professionals it is advisable to integrate end-users in the early stages of AI development as well as to offer needs-adjusted training for the use of AI in healthcare and providing adequate infrastructure.
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Affiliation(s)
- Sophie Isabelle Lambert
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.
- Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Murielle Madi
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Saša Sopka
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
- Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Andrea Lenes
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Hendrik Stange
- Fraunhofer Society for the Advancement of Applied Research. Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, 53757, Sankt Augustin, Bonn, Germany
| | - Claus-Peter Buszello
- Fraunhofer Society for the Advancement of Applied Research. Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, 53757, Sankt Augustin, Bonn, Germany
| | - Astrid Stephan
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Fliedner University of Applied Sciences, Geschwister-Aufricht-Straße, 940489, Düsseldorf, Germany
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Haight TJ, Eshaghi A. Deep Learning Algorithms for Brain Imaging: From Black Box to Clinical Toolbox? Neurology 2023; 100:549-550. [PMID: 36639238 DOI: 10.1212/wnl.0000000000206808] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 11/29/2022] [Indexed: 01/15/2023] Open
Affiliation(s)
- Thaddeus J Haight
- From the Center for Neuroscience and Regenerative Medicine (T.J.H.), Uniformed Services University of the Health Sciences, Bethesda; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD; Department of Neuroinflammation (A.E.), Queen Square Multiple Sclerosis Centre, Queen Square Institute of Neurology, University College London; Centre for Medical Image Computing (A.E.), Department of Computer Science, University College London, United Kingdom.
| | - Arman Eshaghi
- From the Center for Neuroscience and Regenerative Medicine (T.J.H.), Uniformed Services University of the Health Sciences, Bethesda; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD; Department of Neuroinflammation (A.E.), Queen Square Multiple Sclerosis Centre, Queen Square Institute of Neurology, University College London; Centre for Medical Image Computing (A.E.), Department of Computer Science, University College London, United Kingdom
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12
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Uren V, Edwards JS. Technology readiness and the organizational journey towards AI adoption: An empirical study. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2023. [DOI: 10.1016/j.ijinfomgt.2022.102588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, Ren R, Qiao Y, Xue P, Jiang Y. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Front Med (Lausanne) 2022; 9:990604. [PMID: 36117979 PMCID: PMC9472134 DOI: 10.3389/fmed.2022.990604] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Artificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance. Materials and methods We conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world. Results Sixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes. Conclusion Most physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.
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Affiliation(s)
- Mingyang Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ziting Cai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | | | - Nasra M. Ali
- The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Ran Ren
- Global Health Research Center, Dalian Medical University, Dalian, China
| | - Youlin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Youlin Qiao,
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Peng Xue,
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Yu Jiang,
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