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Hughes JA, Wu Y, Jones L, Douglas C, Brown N, Hazelwood S, Lyrstedt AL, Jarugula R, Chu K, Nguyen A. Analyzing pain patterns in the emergency department: Leveraging clinical text deep learning models for real-world insights. Int J Med Inform 2024; 190:105544. [PMID: 39003790 DOI: 10.1016/j.ijmedinf.2024.105544] [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: 03/22/2024] [Revised: 06/09/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVE To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text deep learning algorithm. MATERIALS AND METHODS A fine-tuned, domain-specific, transformer-based clinical text deep learning model was used to interpret free-text nursing assessments in the electronic medical records of 235,789 adult presentations to the ED over a three-year period. The model classified presentations according to whether the patient had pain on arrival at the ED. Interrupted time series analysis was used to determine the incidence of pain in patients on arrival over time. We described the changes in the population characteristics and incidence of patients with pain on arrival occurring with the start of the Covid-19 pandemic. RESULTS 55.16% (95%CI 54.95%-55.36%) of all patients presenting to this ED had pain on arrival. There were differences in demographics and arrival and departure patterns between patients with and without pain. The Covid-19 pandemic initially precipitated a decrease followed by a sharp, sustained rise in pain on arrival, with concurrent changes to the population arriving in pain and their treatment. DISCUSSION Applying a clinical text deep learning model has successfully identified the incidence of pain on arrival. It represents an automated, reproducible mechanism to identify pain from routinely collected medical records. The description of this population and their treatment forms the basis of intervention to improve care for patients with pain. The combination of the clinical text deep learning models and interrupted time series analysis has reported on the effects of the Covid-19 pandemic on pain care in the ED, outlining a methodology to assess the impact of significant events or interventions on pain care in the ED. CONCLUSION Applying a novel deep learning approach to identifying pain guides methodological approaches to evaluating pain care interventions in the ED, giving previously unavailable population-level insights.
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Affiliation(s)
- James A Hughes
- School of Nursing, Queensland University of Technology, Brisbane, Australia; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia.
| | - Yutong Wu
- Australian e-Health Research Centre, CSIRO, Brisbane, Australia
| | - Lee Jones
- QIMR-Berghoffer Research Institute, Brisbane, Australia
| | - Clint Douglas
- School of Nursing, Queensland University of Technology, Brisbane, Australia; Metro North Health, Queensland, Australia
| | - Nathan Brown
- Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia; Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Sarah Hazelwood
- Emergency Department, The Prince Charles Hospital, Queensland, Australia
| | - Anna-Lisa Lyrstedt
- School of Nursing, Queensland University of Technology, Brisbane, Australia; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Rajeev Jarugula
- Emergency Department, The Prince Charles Hospital, Queensland, Australia
| | - Kevin Chu
- Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia; Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Anthony Nguyen
- Australian e-Health Research Centre, CSIRO, Brisbane, Australia
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Gottsäter A, Ekelund U, Melander O, Björkelund A, Ohlsson B. Cohort study of prediction of venous thromboembolism in emergency department patients with extremity symptoms. Intern Emerg Med 2024:10.1007/s11739-024-03696-3. [PMID: 38954105 DOI: 10.1007/s11739-024-03696-3] [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: 03/12/2024] [Accepted: 06/25/2024] [Indexed: 07/04/2024]
Abstract
Despite diagnostic algorithms, identification of venous thromboembolism (VTE) in emergency departments (ED) remains a challenge. We evaluated symptoms, background, and laboratory data in 27,647 ED patients presenting with pain, swelling, or other symptoms from the extremities, and identified predictors of VTE diagnosis within one year. Predictors of a clinical decision to perform phlebography, ultrasound, or computer tomography (CT) angiography of pelvic, lower, or upper extremity veins, CT of pulmonary arteries, or pulmonary scintigraphy at the ED or within 30 days, and the results of such investigations were also evaluated. A total of 3195 patients (11.6%) were diagnosed with VTE within one year. In adjusted analysis of patients in whom all laboratory data were available, a d-dimer value ≥ 0.5 mg/l (odds ratio [OR]: 2.602; 95% confidence interval [CI] 1.894-3.575; p < 0.001) at the ED and a previous diagnosis of VTE (OR: 6.037; CI 4.465-8.162; p < 0.001) independently predicted VTE within one year. Of diagnosed patients, 2355 (73.7%) had undergone imaging within 30 days after the ED visit and 1730 (54.1%) were diagnosed at this examination. Lower age (OR: 0.984; CI 0.972-0.997; p = 0.014), higher blood hemoglobin (OR: 1.023; CI 1.010-1.037; p < 0.001), C-reactive protein (OR: 2.229; CI 1.433-3.468; p < 0.001), d-dimer (OR: 8.729; CI 5.614-13.574; p < 0.001), and previous VTE (OR: 7.796; CI 5.193-11.705; p < 0.001) predicted VTE on imaging within 30 days, whereas female sex (OR 0.602 [95% CI 0.392-0.924]; p = 0.020) and a previous diagnosis of ischemic heart disease (OR 0.254 [95% CI 0.113-0.571]; p = 0.001) were negative predictors of VTE. In conclusion, analysis of 27,647 ED patients with extremity symptoms confirmed the importance of well-established risk factors for VTE. Many patients developing VTE within one year had initial negative imaging, highlighting the importance of continued symptom vigilance.
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Affiliation(s)
- Anders Gottsäter
- Department of Clinical Sciences in Malmö, University of Lund, S-20502, Malmö, Sweden.
- Department of Emergency and Internal Medicine, Skåne University Hospital, S-20502, Malmö, Sweden.
| | - Ulf Ekelund
- Department of Clinical Sciences in Lund, University of Lund, S-22100, Lund, Sweden
- Department of Emergency and Internal Medicine, Skåne University Hospital, S-22242, Lund, Sweden
| | - Olle Melander
- Department of Clinical Sciences in Malmö, University of Lund, S-20502, Malmö, Sweden
- Department of Emergency and Internal Medicine, Skåne University Hospital, S-20502, Malmö, Sweden
| | - Anders Björkelund
- Centre for Environmental and Climate Research, University of Lund, S-22100, Lund, Sweden
| | - Bodil Ohlsson
- Department of Clinical Sciences in Malmö, University of Lund, S-20502, Malmö, Sweden
- Department of Emergency and Internal Medicine, Skåne University Hospital, S-20502, Malmö, Sweden
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Yao J, Alabousi A, Mironov O. Evaluation of a BERT Natural Language Processing Model for Automating CT and MRI Triage and Protocol Selection. Can Assoc Radiol J 2024:8465371241255895. [PMID: 38832645 DOI: 10.1177/08465371241255895] [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: 06/05/2024] Open
Abstract
Purpose: To evaluate the accuracy of a Bidirectional Encoder Representations for Transformers (BERT) Natural Language Processing (NLP) model for automating triage and protocol selection of cross-sectional image requisitions. Methods: A retrospective study was completed using 222 392 CT and MRI studies from a single Canadian university hospital database (January 2018-September 2022). Three hundred unique protocols (116 CT and 184 MRI) were included. A BERT model was trained, validated, and tested using an 80%-10%-10% stratified split. Naive Bayes (NB) and Support Vector Machine (SVM) machine learning models were used as comparators. Models were assessed using F1 score, precision, recall, and area under the receiver operating characteristic curve (AUROC). The BERT model was also assessed for multi-class protocol suggestion and subgroups based on referral location, modality, and imaging section. Results: BERT was superior to SVM for protocol selection (F1 score: BERT-0.901 vs SVM-0.881). However, was not significantly different from SVM for triage prediction (F1 score: BERT-0.844 vs SVM-0.845). Both models outperformed NB for protocol and triage. BERT had superior performance on minority classes compared to SVM and NB. For multiclass prediction, BERT accuracy was up to 0.991 for top-5 protocol suggestion, and 0.981 for top-2 triage suggestion. Emergency department patients had the highest F1 scores for both protocol (0.957) and triage (0.986), compared to inpatients and outpatients. Conclusion: The BERT NLP model demonstrated strong performance in automating the triage and protocol selection of radiology studies, showing potential to enhance radiologist workflows. These findings suggest the feasibility of using advanced NLP models to streamline radiology operations.
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Affiliation(s)
- Jason Yao
- Department of Radiology, McMaster University, Hamilton, ON, Canada
| | - Abdullah Alabousi
- Department of Radiology, McMaster University, Hamilton, ON, Canada
- St Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Oleg Mironov
- Department of Radiology, McMaster University, Hamilton, ON, Canada
- St Joseph's Healthcare Hamilton, Hamilton, ON, Canada
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Alrawashdeh A, Alqahtani S, Alkhatib ZI, Kheirallah K, Melhem NY, Alwidyan M, Al-Dekah AM, Alshammari T, Nehme Z. Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review. Prehosp Disaster Med 2024:1-11. [PMID: 38757150 DOI: 10.1017/s1049023x24000414] [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: 05/18/2024]
Abstract
OBJECTIVE The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). METHODS Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. RESULTS This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. CONCLUSION Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.
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Affiliation(s)
- Ahmad Alrawashdeh
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Saeed Alqahtani
- Department of Emergency Medical Services, Prince Sultan Military College for Health Sciences, Dhahran, Saudi Arabia
| | - Zaid I Alkhatib
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Khalid Kheirallah
- Department of Public Health and Family Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Nebras Y Melhem
- Department of Anatomy, Physiology and Biochemistry, Faculty of Medicine, The Hashemite University, Zarqa, Jordan
| | - Mahmoud Alwidyan
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | | | - Talal Alshammari
- Department of Emergency Medical Care, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ziad Nehme
- Ambulance Victoria, Doncaster, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Savchenko E, Bunimovich-Mendrazitsky S. Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer. Front Med (Lausanne) 2024; 11:1388685. [PMID: 38808135 PMCID: PMC11130437 DOI: 10.3389/fmed.2024.1388685] [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: 02/20/2024] [Accepted: 04/26/2024] [Indexed: 05/30/2024] Open
Abstract
In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.
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Preiksaitis C, Ashenburg N, Bunney G, Chu A, Kabeer R, Riley F, Ribeira R, Rose C. The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR Med Inform 2024; 12:e53787. [PMID: 38728687 PMCID: PMC11127144 DOI: 10.2196/53787] [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: 10/19/2023] [Revised: 12/20/2023] [Accepted: 04/05/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. OBJECTIVE Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. METHODS Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. RESULTS A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. CONCLUSIONS LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.
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Affiliation(s)
- Carl Preiksaitis
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Nicholas Ashenburg
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Gabrielle Bunney
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Andrew Chu
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Rana Kabeer
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Fran Riley
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Ryan Ribeira
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Christian Rose
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
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Yao J, Chu LC, Patlas M. Applications of Artificial Intelligence in Acute Abdominal Imaging. Can Assoc Radiol J 2024:8465371241250197. [PMID: 38715249 DOI: 10.1177/08465371241250197] [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: 06/12/2024] Open
Abstract
Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.
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Affiliation(s)
- Jason Yao
- Department of Radiology, McMaster University, Hamilton, ON, Canada
| | - Linda C Chu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Patlas
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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Ahmadzadeh B, Patey C, Hurley O, Knight J, Norman P, Farrell A, Czarnuch S, Asghari S. Applications of Artificial Intelligence in Emergency Departments to Improve Wait Times: Protocol for an Integrative Living Review. JMIR Res Protoc 2024; 13:e52612. [PMID: 38607662 PMCID: PMC11053385 DOI: 10.2196/52612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/14/2024] [Accepted: 03/01/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Long wait times in the emergency department (ED) are a major issue for health care systems all over the world. The application of artificial intelligence (AI) is a novel strategy to reduce ED wait times when compared to the interventions included in previous research endeavors. To date, comprehensive systematic reviews that include studies involving AI applications in the context of EDs have covered a wide range of AI implementation issues. However, the lack of an iterative update strategy limits the use of these reviews. Since the subject of AI development is cutting edge and is continuously changing, reviews in this area must be frequently updated to remain relevant. OBJECTIVE This study aims to provide a summary of the evidence that is currently available regarding how AI can affect ED wait times; discuss the applications of AI in improving wait times; and periodically assess the depth, breadth, and quality of the evidence supporting the application of AI in reducing ED wait times. METHODS We plan to conduct a living systematic review (LSR). Our strategy involves conducting continuous monitoring of evidence, with biannual search updates and annual review updates. Upon completing the initial round of the review, we will refine the search strategy and establish clear schedules for updating the LSR. An interpretive synthesis using Whittemore and Knafl's framework will be performed to compile and summarize the findings. The review will be carried out using an integrated knowledge translation strategy, and knowledge users will be involved at all stages of the review to guarantee applicability, usability, and clarity of purpose. RESULTS The literature search was completed by September 22, 2023, and identified 17,569 articles. The title and abstract screening were completed by December 9, 2023. In total, 70 papers were eligible. The full-text screening is in progress. CONCLUSIONS The review will summarize AI applications that improve ED wait time. The LSR enables researchers to maintain high methodological rigor while enhancing the timeliness, applicability, and value of the review. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/52612.
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Affiliation(s)
- Bahareh Ahmadzadeh
- Centre for Rural Health Studies, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Christopher Patey
- Eastern Health, Carbonear Institute for Rural Reach and Innovation by the Sea, Carbonear General Hospital, Carbonear, NL, Canada
- Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Oliver Hurley
- Centre for Rural Health Studies, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - John Knight
- Data and Information Services, Digital Health, NL Health Services, St. John's, NL, Canada
- Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Paul Norman
- Eastern Health, Carbonear Institute for Rural Reach and Innovation by the Sea, Carbonear General Hospital, Carbonear, NL, Canada
| | - Alison Farrell
- Health Sciences Library, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Stephen Czarnuch
- Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada
- Discipline of Emergency Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Shabnam Asghari
- Centre for Rural Health Studies, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
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Cohen I, Sorin V, Lekach R, Raskin D, Segev M, Klang E, Eshed I, Barash Y. Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee. Eur J Radiol 2024; 175:111460. [PMID: 38608501 DOI: 10.1016/j.ejrad.2024.111460] [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: 03/04/2024] [Revised: 03/29/2024] [Accepted: 04/08/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies. OBJECTIVE To develop and evaluate an AI algorithm for detecting effusion of any kind in knee X-rays and selected CT images and distinguishing between simple effusion and lipo-hemarthrosis indicative of intra-articular fractures. METHODS This retrospective study analyzed post traumatic knee imaging from January 2016 to February 2023, categorizing images into lipo-hemarthrosis, simple effusion, or normal. It utilized the FishNet-150 algorithm for image classification, with class activation maps highlighting decision-influential regions. The AI's diagnostic accuracy was validated against a gold standard, based on the evaluations made by a radiologist with at least four years of experience. RESULTS Analysis included CT images from 515 patients and X-rays from 637 post traumatic patients, identifying lipo-hemarthrosis, simple effusion, and normal findings. The AI showed an AUC of 0.81 for detecting any effusion, 0.78 for simple effusion, and 0.83 for lipo-hemarthrosis in X-rays; and 0.89, 0.89, and 0.91, respectively, in CTs. CONCLUSION The AI algorithm effectively detects knee effusion and differentiates between simple effusion and lipo-hemarthrosis in post-traumatic patients for both X-rays and selected CT images further studies are needed to validate these results.
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Affiliation(s)
- Israel Cohen
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Vera Sorin
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Ruth Lekach
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Department of Nuclear Medicine, Sourasky Medical Center, Tel-Aviv, Israel.
| | - Daniel Raskin
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Maria Segev
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Eyal Klang
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Iris Eshed
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Yiftach Barash
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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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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Hsueh J, Fritz C, Thomas CE, Reimer AP, Reisner AT, Schoenfeld D, Haimovich A, Thomas SH. Applications of Artificial Intelligence in Helicopter Emergency Medical Services: A Scoping Review. Air Med J 2024; 43:90-95. [PMID: 38490791 DOI: 10.1016/j.amj.2023.11.012] [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: 09/24/2023] [Revised: 11/15/2023] [Accepted: 11/18/2023] [Indexed: 03/17/2024]
Abstract
OBJECTIVE Recent systematic reviews of acute care medicine applications of artificial intelligence (AI) have focused on hospital and general prehospital uses. The purpose of this scoping review was to identify and describe the literature on AI use with a focus on applications in helicopter emergency medical services (HEMS). METHODS A literature search was performed with specific inclusion and exclusion criteria. Articles were grouped by characteristics such as publication year and general subject matter with categoric and temporal trend analyses. RESULTS We identified 21 records focused on the use of AI in HEMS. These applications included both clinical and triage uses and nonclinical uses. The earliest study appeared in 2006, but over one third of the identified studies have been published in 2021 or later. The passage of time has seen an increased likelihood of HEMS AI studies focusing on nonclinical issues; for each year, the likelihood of a nonclinical focus had an odds ratio of 1.3. CONCLUSION This scoping review provides overview and hypothesis-generating information regarding AI applications specific to HEMS. HEMS AI may be ultimately deployed in nonclinical arenas as much as or more than for clinical decision support. Future studies will inform future decisions as to how AI may improve HEMS systems design, asset deployment, and clinical care.
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Affiliation(s)
- Jennifer Hsueh
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
| | - Christie Fritz
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | | | - Andrew P Reimer
- Case Western Reserve University Frances Payne Bolton School of Nursing, Cleveland, OH; Cleveland Clinic Critical Care Transport, Cleveland, OH
| | - Andrew T Reisner
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - David Schoenfeld
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Adrian Haimovich
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Stephen H Thomas
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Blizard Institute, Barts and The London School of Medicine, London, United Kingdom
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12
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Iserson KV. Informed consent for artificial intelligence in emergency medicine: A practical guide. Am J Emerg Med 2024; 76:225-230. [PMID: 38128163 DOI: 10.1016/j.ajem.2023.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/08/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency medicine (EM), there is growing urgency to explore the ethical and practical considerations surrounding its adoption. AI holds the potential to revolutionize how emergency physicians (EPs) make clinical decisions, but AI's complexity often surpasses EPs' capacity to provide patients with informed consent regarding its use. This article underscores the crucial need to address the ethical pitfalls of AI in EM. Patient autonomy necessitates that EPs engage in conversations with patients about whether to use AI in their evaluation and treatment. As clinical AI integration expands, this discussion should become an integral part of the informed consent process, aligning with ethical and legal requirements. The rapid availability of AI programs, fueled by vast electronic health record (EHR) datasets, has led to increased pressure on hospitals and clinicians to embrace clinical AI without comprehensive system evaluation. However, the evolving landscape of AI technology outpaces our ability to anticipate its impact on medical practice and patient care. The central question arises: Are EPs equipped with the necessary knowledge to offer well-informed consent regarding clinical AI? Collaborative efforts between EPs, bioethicists, AI researchers, and healthcare administrators are essential for the development and implementation of optimal AI practices in EM. To facilitate informed consent about AI, EPs should understand at least seven key areas: (1) how AI systems operate; (2) whether AI systems are understandable and trustworthy; (3) the limitations of and errors AI systems make; (4) how disagreements between the EP and AI are resolved; (5) whether the patient's personally identifiable information (PII) and the AI computer systems will be secure; (6) if the AI system functions reliably (has been validated); and (7) if the AI program exhibits bias. This article addresses each of these critical issues, aiming to empower EPs with the knowledge required to navigate the intersection of AI and informed consent in EM.
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Affiliation(s)
- Kenneth V Iserson
- Professor Emeritus, Department of Emergency Medicine, The University of Arizona, Tucson, AZ, 4930 N. Calle Faja, Tucson, AZ, United States of America.
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13
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Koerber D, Khan S, Shamsheri T, Kirubarajan A, Mehta S. Accuracy of Heart Rate Measurement with Wrist-Worn Wearable Devices in Various Skin Tones: a Systematic Review. J Racial Ethn Health Disparities 2023; 10:2676-2684. [PMID: 36376641 PMCID: PMC9662769 DOI: 10.1007/s40615-022-01446-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/21/2022] [Accepted: 11/01/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Wearable consumer technology allows for the collection of a growing amount of personal health data. Through the analysis of reflected LED light on the skin, heart rate measurement and arrhythmia detection can be performed. Given that melanin alters skin light absorption, this study seeks to summarize the accuracy of cardiac data from wrist-worn wearable devices for participants of varying skin tones. METHODS We conducted a systematic review, searching Embase, MEDLINE, CINAHL, and Cochrane for original studies that stratified heart rate and rhythm data for consumer wearable technology according to participant race and/or skin tone. RESULTS A total of 10 studies involving 469 participants met inclusion criteria. The frequency-weighted Fitzpatrick score for skin tone was reported in six studies (n = 293), with a mean participant score of 3.5 (range 1-6). Overall, four of the ten studies reported a significant reduction in accuracy of heart rate measurement with wearable devices in darker-skinned individuals, compared to participants with lighter skin tones. Four studies noted no effect of user skin tone on accuracy. The remaining two studies showed mixed results. CONCLUSIONS Preliminary evidence is inconclusive, but some studies suggest that wearable devices may be less accurate for detecting heart rate in participants with darker skin tones. Higher quality evidence is necessary, with larger sample sizes and more objective stratification of participants by skin tone, in order to characterize potential racial bias in consumer devices.
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Affiliation(s)
- Daniel Koerber
- Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Shawn Khan
- Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Tahmina Shamsheri
- Department of Interdisciplinary Studies, McMaster University, Hamilton, Canada
| | - Abirami Kirubarajan
- Faculty of Medicine, University of Toronto, Toronto, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada
| | - Sangeeta Mehta
- Faculty of Medicine, University of Toronto, Toronto, Canada.
- Department of Medicine, Interdepartmental Division of Critical Care Medicine, Sinai Health System, University of Toronto, Toronto, Canada.
- Mount Sinai Hospital, 600 University Ave, Suite 18-216, Toronto, ON, Canada.
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Toy J, Bosson N, Schlesinger S, Gausche-Hill M, Stratton S. Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review. Resusc Plus 2023; 16:100491. [PMID: 37965243 PMCID: PMC10641545 DOI: 10.1016/j.resplu.2023.100491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/23/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023] Open
Abstract
Background Artificial intelligence (AI) has demonstrated significant potential in supporting emergency medical services personnel during out-of-hospital cardiac arrest (OHCA) care; however, the extent of research evaluating this topic is unknown. This scoping review examines the breadth of literature on the application of AI in early OHCA care. Methods We conducted a search of PubMed®, Embase, and Web of Science in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Articles focused on non-traumatic OHCA and published prior to January 18th, 2023 were included. Studies were excluded if they did not use an AI intervention (including machine learning, deep learning, or natural language processing), or did not utilize data from the prehospital phase of care. Results Of 173 unique articles identified, 54 (31%) were included after screening. Of these studies, 15 (28%) were from the year 2022 and with an increasing trend annually starting in 2019. The majority were carried out by multinational collaborations (20/54, 38%) with additional studies from the United States (10/54, 19%), Korea (5/54, 10%), and Spain (3/54, 6%). Studies were classified into three major categories including ECG waveform classification and outcome prediction (24/54, 44%), early dispatch-level detection and outcome prediction (7/54, 13%), return of spontaneous circulation and survival outcome prediction (15/54, 20%), and other (9/54, 16%). All but one study had a retrospective design. Conclusions A small but growing body of literature exists describing the use of AI to augment early OHCA care.
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Affiliation(s)
- Jake Toy
- University of California Los Angeles, Fielding School of Public Health, 650 Charles E Young Drive South, Los Angeles, CA 90095, USA
- Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA
- Los Angeles County EMS Agency, 10100 Pioneer Blvd, Santa Fe Springs, CA 90670, USA
- David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Nichole Bosson
- Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA
- Los Angeles County EMS Agency, 10100 Pioneer Blvd, Santa Fe Springs, CA 90670, USA
- David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Shira Schlesinger
- Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA
- David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Marianne Gausche-Hill
- Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA
- Los Angeles County EMS Agency, 10100 Pioneer Blvd, Santa Fe Springs, CA 90670, USA
- David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Samuel Stratton
- University of California Los Angeles, Fielding School of Public Health, 650 Charles E Young Drive South, Los Angeles, CA 90095, USA
- Orange County California Emergency Medical Services Agency, 405 W. 5th Street, Santa Ana, CA 92705, USA
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Leonard F, O’Sullivan D, Gilligan J, O’Shea N, Barrett MJ. Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. PLoS One 2023; 18:e0294231. [PMID: 37972029 PMCID: PMC10653406 DOI: 10.1371/journal.pone.0294231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
INTRODUCTION Machine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. This paper presents a protocol for a scoping review, with the objective of summarising the existing research on machine learning clinical decision support system tools in the emergency department, focusing on models that can be used for paediatric patients, where a knowledge gap exists. MATERIALS AND METHODS The methodology used will follow the scoping study framework of Arksey and O'Malley, along with other guidelines. Machine learning clinical decision support system tools for any outcome and population (paediatric/adult/mixed) for use in the emergency department will be included. Articles such as grey literature, letters, pre-prints, editorials, scoping/literature/narrative reviews, non-English full text papers, protocols, surveys, abstract or full text not available and models based on synthesised data will be excluded. Articles from the last five years will be included. Four databases will be searched: Medline (EBSCO), CINAHL (EBSCO), EMBASE and Cochrane Central. Independent reviewers will perform the screening in two sequential stages (stage 1: clinician expertise and stage 2: computer science expertise), disagreements will be resolved by discussion. Data relevant to the research question will be collected. Quantitative analysis will be performed to generate the results. DISCUSSION The study results will summarise the existing research on machine learning clinical decision support tools in the emergency department, focusing on models that can be used for paediatric patients. This holds the promise to identify opportunities to both incorporate models in clinical practice and to develop future models by utilising reviewers from diverse backgrounds and relevant expertise.
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Affiliation(s)
- Fiona Leonard
- School of Computer Science, Technological University Dublin, Dublin, Ireland
- Digital Health Department, Children’s Health Ireland, Crumlin, Dublin, Ireland
| | - Dympna O’Sullivan
- School of Computer Science, Technological University Dublin, Dublin, Ireland
| | - John Gilligan
- School of Computer Science, Technological University Dublin, Dublin, Ireland
| | - Nicola O’Shea
- Library and Information Service, Children’s Health Ireland at Crumlin, Dublin, Ireland
| | - Michael J. Barrett
- Department of Paediatric Emergency Medicine, Children’s Health Ireland at Crumlin, Dublin, Ireland
- Women’s and Children’s Health, School of Medicine, University College Dublin, Dublin, Ireland
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16
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Ali G, Anwar M, Nauman M, Faheem M, Rashid J. Lyme rashes disease classification using deep feature fusion technique. Skin Res Technol 2023; 29:e13519. [PMID: 38009027 PMCID: PMC10628356 DOI: 10.1111/srt.13519] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 10/24/2023] [Indexed: 11/28/2023]
Abstract
Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists' probe and investigate Lyme skin rashes effectively. This paper proposes a new in-depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state-of-the-art models.
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Affiliation(s)
- Ghulam Ali
- Department of Computer ScienceUniversity of OkaraOkaraPakistan
| | - Muhammad Anwar
- Department of Information SciencesDivision of Science and TechnologyUniversity of EducationLahorePakistan
| | - Muhammad Nauman
- Department of Computer ScienceUniversity of OkaraOkaraPakistan
| | - Muhammad Faheem
- School of Technology and InnovationsUniversity of VaasaVaasaFinland
| | - Javed Rashid
- Department of IT ServicesUniversity of OkaraOkaraPakistan
- MLC LabOkaraPakistan
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Chee ML, Chee ML, Huang H, Mazzochi K, Taylor K, Wang H, Feng M, Ho AFW, Siddiqui FJ, Ong MEH, Liu N. Artificial intelligence and machine learning in prehospital emergency care: A scoping review. iScience 2023; 26:107407. [PMID: 37609632 PMCID: PMC10440716 DOI: 10.1016/j.isci.2023.107407] [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] [Indexed: 08/24/2023] Open
Abstract
Our scoping review provides a comprehensive analysis of the landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field by highlighting the most studied AI applications and identifying the most common methodological approaches across 106 included studies. The findings indicate a promising future for AI in PEC, with many unique use cases, such as prognostication, demand prediction, resource optimization, and the Internet of Things continuous monitoring systems. Comparisons with other approaches showed AI outperforming clinicians and non-AI algorithms in most cases. However, most studies were internally validated and retrospective, highlighting the need for rigorous prospective validation of AI applications before implementation in clinical settings. We identified knowledge and methodological gaps using an evidence map, offering a roadmap for future investigators. We also discussed the significance of explainable AI for establishing trust in AI systems among clinicians and facilitating real-world validation of AI models.
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Affiliation(s)
- Marcel Lucas Chee
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Mark Leonard Chee
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Haotian Huang
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Katelyn Mazzochi
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Kieran Taylor
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Han Wang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Andrew Fu Wah Ho
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Fahad Javaid Siddiqui
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Marcus Eng Hock Ong
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Nan Liu
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
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18
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Chen YHJ, Lin CS, Lin C, Tsai DJ, Fang WH, Lee CC, Wang CH, Chen SJ. An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration. J Med Syst 2023; 47:81. [PMID: 37523102 DOI: 10.1007/s10916-023-01980-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/21/2023] [Indexed: 08/01/2023]
Abstract
Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.
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Affiliation(s)
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Chin Lin
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Dung-Jang Tsai
- Center for Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Wen-Hui Fang
- Center for Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chih-Hung Wang
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Sy-Jou Chen
- Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490, Taiwan.
- Graduate Institute of Injury Prevention and Control, College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan.
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19
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Eastwood KW, May R, Andreou P, Abidi S, Abidi SSR, Loubani OM. Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians. BMC Health Serv Res 2023; 23:798. [PMID: 37491228 PMCID: PMC10369807 DOI: 10.1186/s12913-023-09740-w] [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: 11/02/2022] [Accepted: 06/22/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools. METHODS A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians' current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI. RESULTS The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, 'automated charting/report generation', 'clinical prediction rules' and 'monitoring vitals with early-warning detection' were the top items. When ranking by physician work-activities, 'AI-tools for documentation', 'AI-tools for computer use' and 'AI-tools for triaging patients' were the top items. For secondary outcomes, EPs indicated AI was 'likely' (43.1%) or 'extremely likely' (43.7%) to be able to complete the task of 'documentation' and indicated either 'a-great-deal' (32.8%) or 'quite-a-bit' (39.7%) of potential for AI in EM. Further, EPs were either 'strongly' (48.5%) or 'somewhat' (39.8%) interested in AI for EM. CONCLUSIONS Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.
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Affiliation(s)
- Kyle W Eastwood
- Department of Emergency Medicine, Dalhousie University, 1796 Summer Street, Halifax Infirmary, 4Th Floor Emergency Department Administration Office, Halifax, NS, B3H 2Y9, Canada.
| | - Ronald May
- Department of Emergency Medicine, Dalhousie University, 1796 Summer Street, Halifax Infirmary, 4Th Floor Emergency Department Administration Office, Halifax, NS, B3H 2Y9, Canada
| | - Pantelis Andreou
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Canada
| | - Samina Abidi
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Canada
| | - Syed Sibte Raza Abidi
- NICHE Research Group, Faculty of Computer Science, Dalhousie University, Halifax, Canada
| | - Osama M Loubani
- Department of Emergency Medicine, Dalhousie University, 1796 Summer Street, Halifax Infirmary, 4Th Floor Emergency Department Administration Office, Halifax, NS, B3H 2Y9, Canada
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20
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Varma JR, Fernando S, Ting BY, Aamir S, Sivaprakasam R. The Global Use of Artificial Intelligence in the Undergraduate Medical Curriculum: A Systematic Review. Cureus 2023; 15:e39701. [PMID: 37398823 PMCID: PMC10309075 DOI: 10.7759/cureus.39701] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 07/04/2023] Open
Abstract
Artificial intelligence (AI) is a rapidly advancing technology that has the potential to revolutionize medical education. AI can provide personalized learning experiences, assist with student assessment, and aid in the integration of pre-clinical and clinical curricula. Despite the potential benefits, there is a paucity of literature investigating the use of AI in undergraduate medical education. This study aims to evaluate the role of AI in undergraduate medical curricula worldwide and compare AI to current teaching and assessment methods. This systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. Texts unavailable in English were excluded alongside those not focused on medical students alone or with little mention of AI. The key search terms were "undergraduate medical education," "medical students," "medical education," and "artificial intelligence." The methodological rigor of each study was assessed using the Medical Education Research Study Quality Instrument (MERSQI). A total of 36 articles were screened from 700 initial articles, of which 11 were deemed eligible. These were categorized into the following three domains: teaching (n = 6), assessing (n = 3), and trend spotting (n = 2). AI was shown to be highly accurate in studies that directly tested its ability. The mean overall MERSQI score for all selected papers was 10.5 (standard deviation = 2.3; range = 6 to 15.5) falling below the expected score of 10.7 due to notable weaknesses in study design, sampling methods, and study outcomes. AI performance was synergized with human involvement suggesting that AI would be best employed as a supplement to undergraduate medical curricula. Studies directly comparing AI to current teaching methods demonstrated favorable performance. While shown to have a promising role, there remains a limited number of studies in the field, and further research is needed to refine and establish clear foundations to assist in its development.
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Affiliation(s)
- Jonny R Varma
- Undergraduate Medical Education, Barts and The London School of Medicine and Dentistry, London, GBR
| | - Sherwin Fernando
- Undergraduate Medical Education, Barts and The London School of Medicine and Dentistry, London, GBR
| | - Brian Y Ting
- Undergraduate Medical Education, Barts and The London School of Medicine and Dentistry, London, GBR
| | - Shahrukh Aamir
- Undergraduate Medical Education, Barts and The London School of Medicine and Dentistry, London, GBR
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21
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Viderman D, Abdildin YG, Batkuldinova K, Badenes R, Bilotta F. Artificial Intelligence in Resuscitation: A Scoping Review. J Clin Med 2023; 12:jcm12062254. [PMID: 36983255 PMCID: PMC10054374 DOI: 10.3390/jcm12062254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction: Cardiac arrest is a significant cause of premature mortality and severe disability. Despite the death rate steadily decreasing over the previous decade, only 22% of survivors achieve good clinical status and only 25% of patients survive until their discharge from the hospital. The objective of this scoping review was to review relevant AI modalities and the main potential applications of AI in resuscitation. Methods: We conducted the literature search for related studies in PubMed, EMBASE, and Google Scholar. We included peer-reviewed publications and articles in the press, pooling and characterizing the data by their model types, goals, and benefits. Results: After identifying 268 original studies, we chose 59 original studies (reporting 1,817,419 patients) to include in the qualitative synthesis. AI-based methods appear to be superior to traditional methods in achieving high-level performance. Conclusion: AI might be useful in predicting cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers. AI-powered technologies could be valuable assistants to continuously track patient conditions. Healthcare professionals should assist in the research and development of AI-powered technologies as well as their implementation into clinical practice.
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, Nazarbayev University School of Medicine (NUSOM), Kerei, Zhanibek khandar Str. 5/1, Astana 010000, Kazakhstan;
| | - Yerkin G. Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana 010000, Kazakhstan
| | - Kamila Batkuldinova
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana 010000, Kazakhstan
| | - Rafael Badenes
- Department of Anaesthesiology and Intensive Care, Hospital Clìnico Universitario de Valencia, University of Valencia, 46001 Valencia, Spain
- Correspondence:
| | - Federico Bilotta
- Department of Anesthesia and Intensive Care, University La Sapienza, 00185 Rome, Italy
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22
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Alakare J, Kemp K, Strandberg T, Castrén M, Tolonen J, Harjola VP. Red cell distribution width and mortality in older patients with frailty in the emergency department. BMC Emerg Med 2023; 23:24. [PMID: 36894893 PMCID: PMC9998144 DOI: 10.1186/s12873-023-00801-1] [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: 10/10/2022] [Accepted: 03/01/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND The red cell distribution width (RDW) reflects the degree of heterogeneity of red blood cells. Elevated RDW is associated both with frailty and with increased mortality in hospital-admitted patients. In this study we evaluate whether high RDW values are associated with mortality in older emergency department (ED) patients with frailty, and if the association is independent of the degree of frailty. METHODS We included ED patients with the following criteria: ≥ 75 years of age, Clinical Frailty Scale (CFS) score of 4 to 8, and RDW % measured within 48 h of ED admission. Patients were allocated to six classes by their RDW value: ≤ 13%, 14%, 15%, 16%, 17%, and ≥ 18%. The outcome was death within 30 days of ED admission. Crude and adjusted odds ratios (OR) with 95% confidence intervals (CI) for a one-class increase in RDW for 30-day mortality were calculated via binary logistic regression analysis. Age, gender and CFS score were considered as potential confounders. RESULTS A total of 1407 patients (61.2% female), were included. The median age was 85 with an inter-quartile range (IQR) of 80-89, median CFS score 6 (IQR: 5-7), and median RDW 14 (IQR: 13-16). Of the included patients, 71.9% were admitted to hospital wards. A total of 85 patients (6.0%) died during the 30-day follow-up. Mortality rate was associated with RDW increase (p for trend < .001). Crude OR for a one-class increase in RDW for 30-day mortality was 1.32 (95% CI: 1.17-1.50, p < .001). When adjusted for age, gender and CFS-score, OR of mortality for one-class RDW increase was still 1.32 (95% CI: 1.16-1.50, p < .001). CONCLUSION Higher RDW values had a significant association with increased 30-day mortality risk in frail older adults in the ED, and this risk was independent of degree of frailty. RDW is a readily available biomarker for most ED patients. It might be beneficial to include it in risk stratification of older frail ED patients to identify those who could benefit from further diagnostic assessment, targeted interventions, and care planning.
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Affiliation(s)
- Janne Alakare
- Department of Emergency Medicine and Services, Helsinki University Hospital and University of Helsinki, Helsinki, Finland. .,Department of Geriatric Acute Care, Espoo Hospital, 2550 02070, City of Espoo, PL, Finland.
| | - Kirsi Kemp
- Department of Emergency Medicine and Services, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Timo Strandberg
- University of Helsinki, Clinicum, and Helsinki University Hospital, Helsinki, Finland.,University of Oulu, Center for Life Course Health Research, Oulu, Finland
| | - Maaret Castrén
- Department of Emergency Medicine and Services, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Jukka Tolonen
- Department of Internal Medicine, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Veli-Pekka Harjola
- Department of Emergency Medicine and Services, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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23
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Hatachi T, Hashizume T, Taniguchi M, Inata Y, Aoki Y, Kawamura A, Takeuchi M. Machine Learning-Based Prediction of Hospital Admission Among Children in an Emergency Care Center. Pediatr Emerg Care 2023; 39:80-86. [PMID: 36719388 DOI: 10.1097/pec.0000000000002648] [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] [Indexed: 02/03/2023]
Abstract
OBJECTIVES Machine learning-based prediction of hospital admissions may have the potential to optimize patient disposition and improve clinical outcomes by minimizing both undertriage and overtriage in crowded emergency care. We developed and validated the predictive abilities of machine learning-based predictions of hospital admissions in a pediatric emergency care center. METHODS A prognostic study was performed using retrospectively collected data of children younger than 16 years who visited a single pediatric emergency care center in Osaka, Japan, between August 1, 2016, and October 15, 2019. Generally, the center treated walk-in children and did not treat trauma injuries. The main outcome was hospital admission as determined by the physician. The 83 potential predictors available at presentation were selected from the following categories: demographic characteristics, triage level, physiological parameters, and symptoms. To identify predictive abilities for hospital admission, maximize the area under the precision-recall curve, and address imbalanced outcome classes, we developed the following models for the preperiod training cohort (67% of the samples) and also used them in the 1-year postperiod validation cohort (33% of the samples): (1) logistic regression, (2) support vector machine, (3) random forest, and (4) extreme gradient boosting. RESULTS Among 88,283 children who were enrolled, the median age was 3.9 years, with 47,931 (54.3%) boys and 1985 (2.2%) requiring hospital admission. Among the models, extreme gradient boosting achieved the highest predictive abilities (eg, area under the precision-recall curve, 0.26; 95% confidence interval, 0.25-0.27; area under the receiver operating characteristic curve, 0.86; 95% confidence interval, 0.84-0.88; sensitivity, 0.77; and specificity, 0.82). With an optimal threshold, the positive and negative likelihood ratios were 4.22, and 0.28, respectively. CONCLUSIONS Machine learning-based prediction of hospital admissions may support physicians' decision-making for hospital admissions. However, further improvements are required before implementing these models in real clinical settings.
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Affiliation(s)
- Takeshi Hatachi
- From the Department of Intensive Care Medicine, Osaka Women's and Children's Hospital
| | - Takao Hashizume
- Department of Pediatrics, SAKAI Children's Emergency Medical Center, Osaka
| | - Masashi Taniguchi
- From the Department of Intensive Care Medicine, Osaka Women's and Children's Hospital
| | - Yu Inata
- From the Department of Intensive Care Medicine, Osaka Women's and Children's Hospital
| | | | - Atsushi Kawamura
- From the Department of Intensive Care Medicine, Osaka Women's and Children's Hospital
| | - Muneyuki Takeuchi
- From the Department of Intensive Care Medicine, Osaka Women's and Children's Hospital
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24
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Kishore K, Braitberg G, Holmes NE, Bellomo R. Early prediction of hospital admission of emergency department patients. Emerg Med Australas 2023. [PMID: 36634916 DOI: 10.1111/1742-6723.14169] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/09/2022] [Accepted: 12/19/2022] [Indexed: 01/14/2023]
Abstract
OBJECTIVE The early prediction of hospital admission is important to ED patient management. Using available electronic data, we aimed to develop a predictive model for hospital admission. METHODS We analysed all presentations to the ED of a tertiary referral centre over 7 years. To our knowledge, our data set of nearly 600 000 presentations is the largest reported. Using demographic, clinical, socioeconomic, triage, vital signs, pathology data and keywords in electronic notes, we trained a machine learning (ML) model with presentations from 2015 to 2020 and evaluated it on a held-out data set from 2021 to mid-2022. We assessed electronic medical records (EMRs) data at patient arrival (baseline), 30, 60, 120 and 240 min after ED presentation. RESULTS The training data set included 424 354 data points and the validation data set 53 403. We developed and trained a binary classifier to predict inpatient admission. On a held-out test data set of 121 258 data points, we predicted admission with 86% accuracy within 30 min of ED presentation with 94% discrimination. All models for different time points from ED presentation produced an area under the receiver operating characteristic curve (AUC) ≥0.93 for admission overall, with sensitivity/specificity/F1-scores of 0.83/0.90/0.84 for any inpatient admission at 30 min after presentation and 0.81/0.92/0.84 at baseline. The models retained lower but still high AUC levels when separated for short stay units or inpatient admissions. CONCLUSION We combined available electronic data and ML technology to achieve excellent predictive performance for subsequent hospital admission. Such prediction may assist with patient flow.
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Affiliation(s)
- Kartik Kishore
- Data Analytics Research and Evaluation Centre, Austin Hospital, Melbourne, Victoria, Australia
| | - George Braitberg
- Department of Emergency Medicine, Austin Hospital, Melbourne, Victoria, Australia.,Department of Critical Care, The University of Melbourne, Melbourne, Victoria, Australia
| | - Natasha E Holmes
- Data Analytics Research and Evaluation Centre, Austin Hospital, Melbourne, Victoria, Australia.,Department of Critical Care, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rinaldo Bellomo
- Data Analytics Research and Evaluation Centre, Austin Hospital, Melbourne, Victoria, Australia.,Department of Critical Care, The University of Melbourne, Melbourne, Victoria, Australia
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25
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Gan L, Yin X, Huang J, Jia B. Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1695-1715. [PMID: 36899504 DOI: 10.3934/mbe.2023077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute nonfatal event and is characterized by high morbidity, disability, and mortality. Transcranial Doppler (TCD) ultrasonography is a non-invasive method for the diagnosis of cerebrovascular disease that uses the Doppler effect to detect the hemodynamic and physiological parameters of the major intracranial basilar arteries. It can provide important hemodynamic information that cannot be measured by other diagnostic imaging techniques for cerebrovascular disease. And the result parameters of TCD ultrasonography such as blood flow velocity and beat index can reflect the type of cerebrovascular disease and serve as a basis to assist physicians in the treatment of cerebrovascular diseases. Artificial intelligence (AI) is a branch of computer science which is used in a wide range of applications in agriculture, communications, medicine, finance, and other fields. In recent years, there are much research devoted to the application of AI to TCD. The review and summary of related technologies is an important work to promote the development of this field, which can provide an intuitive technical summary for future researchers. In this paper, we first review the development, principles, and applications of TCD ultrasonography and other related knowledge, and briefly introduce the development of AI in the field of medicine and emergency medicine. Finally, we summarize in detail the applications and advantages of AI technology in TCD ultrasonography including the establishment of an examination system combining brain computer interface (BCI) and TCD ultrasonography, the classification and noise cancellation of TCD ultrasonography signals using AI algorithms, and the use of intelligent robots to assist physicians in TCD ultrasonography and discuss the prospects for the development of AI in TCD ultrasonography.
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Affiliation(s)
- Lingli Gan
- Department of Neurology, Chongqing General Hospital, Chongqing 401147, China
| | - Xiaoling Yin
- Department of Neurosurgery, Chongqing General Hospital, Chongqing 401147, China
| | - Jiating Huang
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Bin Jia
- Department of Neurosurgery, Chongqing General Hospital, Chongqing 401147, China
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26
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Jordan M, Hauser J, Cota S, Li H, Wolf L. The Impact of Cultural Embeddedness on the Implementation of an Artificial Intelligence Program at Triage: A Qualitative Study. J Transcult Nurs 2023; 34:32-39. [PMID: 36214065 DOI: 10.1177/10436596221129226] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
INTRODUCTION Triage requires rapid determination of acuity and resources. Current modalities allow for individual judgment, with varied application of algorithmic rules. Although artificial intelligence can improve triage accuracy, gaps remain in understanding implementation facilitators and barriers, especially those related to the cultural understandings by nurses of emergency department presentations. The purpose of this study was to explore the cultural and technological elements of the implementation of an artificial intelligence clinical decision support aid (i.e., KATE) in an emergency nursing triage process in an urban community hospital on the West Coast of the United States. METHOD An exploratory qualitative study using semi-structured small group and individual interviews and constant comparison analysis strategies. The sample comprised 13 emergency department triage nurses at one site. Campinha-Bacote's theory of cultural competence framed the study. RESULTS Responses yielded the overall theme of We know these people and we know these things. Supporting categories included the problem of aire; just another checkbox; gut trumps data; higher acuity with no resources; and technology as a safety net. Participants reported reliance on clinical experience and cultural knowledge to assign acuity. DISCUSSION The implementation of an artificial intelligence program was initially received skeptically due to the acontextual nature of AI, but grew to be perceived as a safety net for triage decision making among emergency nurses.
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Affiliation(s)
| | | | | | - Hong Li
- Azusa Pacific University, CA, USA
| | - Lisa Wolf
- Emergency Nurses Association, Schaumburg, IL, USA
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27
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Ngo B, Nguyen D, vanSonnenberg E. The Cases for and against Artificial Intelligence in the Medical School Curriculum. Radiol Artif Intell 2022; 4:e220074. [PMID: 36204540 PMCID: PMC9530767 DOI: 10.1148/ryai.220074] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/26/2022] [Accepted: 08/02/2022] [Indexed: 06/02/2023]
Abstract
Although artificial intelligence (AI) has immense potential to shape the future of medicine, its place in undergraduate medical education currently is unclear. Numerous arguments exist both for and against including AI in the medical school curriculum. AI likely will affect all medical specialties, perhaps radiology more so than any other. The purpose of this article is to present a balanced perspective on whether AI should be included officially in the medical school curriculum. After presenting the balanced point-counterpoint arguments, the authors provide a compromise. Keywords: Artificial Intelligence, Medical Education, Medical School Curriculum, Medical Students, Radiology, Use of AI in Education © RSNA, 2022.
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28
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Barthélemy EJ, Affana CK, Asfaw ZK, Dams-O'Connor K, Rahman J, Jones S, Ullman J, Margetis K, Hickman ZL, Dangayach NS, Giwa AO. Racial and Socioeconomic Disparities in Neurotrauma: Research Priorities in the New York Metropolitan Area through a Global Neurosurgery Paradigm. World Neurosurg 2022; 165:51-57. [PMID: 35700861 DOI: 10.1016/j.wneu.2022.06.031] [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: 03/19/2022] [Accepted: 06/06/2022] [Indexed: 11/29/2022]
Abstract
The New York Neurotrauma Consortium (NYNC) is a nascent multidisciplinary research and advocacy organization based in the New York Metropolitan Area (NYMA). It aims to advance health equity and optimize outcomes for traumatic brain and spine injury patients. Given the extensive racial, ethnic, and socioeconomic diversity of the NYMA, global health frameworks aimed at eliminating disparities in neurotrauma may provide a relevant and useful model for the informing research agendas of consortia like the NYNC. In this review, we present a comparative analysis of key health disparities in traumatic brain injury (TBI) that persist in the NYMA as well as in low- and middle-income countries (LMIC). Examples include: (a) inequitable access to quality care due to fragmentation of healthcare systems, (b) barriers to effective prehospital care for TBI, and (c) socioeconomic challenges faced by patients and their families during the subacute and chronic post-injury phases of TBI care. This review presents strategies to address each area of health disparity based on previous studies conducted in both LMIC and high-income country (HIC) settings. Increased awareness of healthcare disparities, education of healthcare professionals, effective policy advocacy for systemic changes, and fostering racial diversity of the trauma care workforce can guide the development of trauma care systems in the NYMA that are free of racial and related healthcare disparities.
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Affiliation(s)
- Ernest J Barthélemy
- New York Neurotrauma Consortium, Inc., New York, New York; Department of Neurosurgery, University of California San Francisco, San Francisco, California, USA; Center for Health Equity in Surgery and Anesthesia, University of California San Francisco, San Francisco, California, USA.
| | | | - Zerubabbel K Asfaw
- Department of Neurosurgery, Icahn School of Medicine at the Mount Sinai Hospital, New York, New York
| | - Kristen Dams-O'Connor
- New York Neurotrauma Consortium, Inc., New York, New York; Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jueria Rahman
- New York Neurotrauma Consortium, Inc., New York, New York; Department of Neurosurgery, Icahn School of Medicine at the Mount Sinai Hospital, New York, New York; Department of Neurosurgery, NYC Health + Hospitals/Elmhurst, Queens, New York
| | - Salazar Jones
- New York Neurotrauma Consortium, Inc., New York, New York; Department of Neurosurgery, Icahn School of Medicine at the Mount Sinai Hospital, New York, New York; Department of Neurosurgery, NYC Health + Hospitals/Elmhurst, Queens, New York
| | - Jamie Ullman
- New York Neurotrauma Consortium, Inc., New York, New York; Institute for Neurology and Neurosurgery at North Shore University Hospital
| | - Konstantinos Margetis
- New York Neurotrauma Consortium, Inc., New York, New York; Department of Neurosurgery, Icahn School of Medicine at the Mount Sinai Hospital, New York, New York
| | - Zachary L Hickman
- New York Neurotrauma Consortium, Inc., New York, New York; Department of Neurosurgery, Icahn School of Medicine at the Mount Sinai Hospital, New York, New York; Department of Neurosurgery, NYC Health + Hospitals/Elmhurst, Queens, New York
| | - Neha S Dangayach
- New York Neurotrauma Consortium, Inc., New York, New York; Department of Neurosurgery, Icahn School of Medicine at the Mount Sinai Hospital, New York, New York; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Al O Giwa
- New York Neurotrauma Consortium, Inc., New York, New York; Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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Sujan M, Thimbleby H, Habli I, Cleve A, Maaløe L, Rees N. Assuring safe artificial intelligence in critical ambulance service response: study protocol. Br Paramed J 2022; 7:36-42. [DOI: 10.29045/14784726.2022.06.7.1.36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Introduction: Early recognition of out-of-hospital cardiac arrest (OHCA) by ambulance service call centre operators is important so that cardiopulmonary resuscitation can be delivered immediately, but around 25% of OHCAs are not picked up by call centre operators. An artificial
intelligence (AI) system has been developed to support call centre operators in the detection of OHCA. The study aims to (1) explore ambulance service stakeholder perceptions on the safety of OHCA AI decision support in call centres, and (2) develop a clinical safety case for the OHCA AI decision-support
system.Methods and analysis: The study will be undertaken within the Welsh Ambulance Service. The study is part research and part service evaluation. The research utilises a qualitative study design based on thematic analysis of interview data. The service evaluation consists of
the development of a clinical safety case based on document analysis, analysis of the AI model and its development process and informal interviews with the technology developer.Conclusions: AI presents many opportunities for ambulance services, but safety assurance requirements
need to be understood. The ASSIST project will continue to explore and build the body of knowledge in this area.
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Affiliation(s)
- Mark Sujan
- Human Factors Everywhere Ltd. ORCID iD:, URL: https://orcid.org/0000-0001-6895-946X
| | | | | | | | | | - Nigel Rees
- Welsh Ambulance Service NHS Trust ORCID iD:, URL: https://orcid.org/0000-0001-8799-5335
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30
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Boonstra A, Laven M. Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review. BMC Health Serv Res 2022; 22:669. [PMID: 35585603 PMCID: PMC9118875 DOI: 10.1186/s12913-022-08070-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/11/2022] [Indexed: 11/30/2022] Open
Abstract
Objective This systematic literature review aims to demonstrate how Artificial Intelligence (AI) is currently used in emergency departments (ED) and how it alters the work design of ED clinicians. AI is still new and unknown to many healthcare professionals in emergency care, leading to unfamiliarity with its capabilities. Method Various criteria were used to establish the suitability of the articles to answer the research question. This study was based on 34 selected peer-reviewed papers on the use of Artificial Intelligence (AI) in the Emergency Department (ED), published in the last five years. Drawing on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, all articles were scanned, read full-text, and analyzed afterward. Results The majority of the AI applications consisted of AI-based tools to aid with clinical decisions and to relieve overcrowded EDs of their burden. AI support was mostly offered during triage, the moment that sets the patient trajectory. There is ample evidence that AI-based applications could improve the clinical decision-making process. Conclusion The use of AI in EDs is still in its nascent stages. Many studies focus on the question of whether AI has clinical utility, such as decision support, improving resource allocation, reducing diagnostic errors, and promoting proactivity. Some studies suggest that AI-based tools essentially have the ability to outperform human skills. However, it is evident from the literature that current technology does not have the aims or power to do so. Nevertheless, AI-based tools can impact clinician work design in the ED by providing support with clinical decisions, which could ultimately help alleviate a portion of the increasing clinical burden. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-08070-7.
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Affiliation(s)
- Albert Boonstra
- Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands.
| | - Mente Laven
- Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands
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31
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Picard CT, Kleib M, O'Rourke HM, Norris CM, Douma MJ. Emergency nurses' triage narrative data, their uses and structure: a scoping review protocol. BMJ Open 2022; 12:e055132. [PMID: 35418428 PMCID: PMC9014040 DOI: 10.1136/bmjopen-2021-055132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
INTRODUCTION The first clinical interaction most patients have in the emergency department occurs during triage. An unstructured narrative is generated during triage and is the first source of in-hospital documentation. These narratives capture the patient's reported reason for the visit and the initial assessment and offer significantly more nuanced descriptions of the patient's complaints than fixed field data. Previous research demonstrated these data are useful for predicting important clinical outcomes. Previous reviews examined these narratives in combination or isolation with other free-text sources, but used restricted searches and are becoming outdated. Furthermore, there are no reviews focused solely on nurses' (the primary collectors of these data) narratives. METHODS AND ANALYSIS Using the Arksey and O'Malley scoping review framework and PRISMA-ScR reporting guidelines, we will perform structured searches of CINAHL, Ovid MEDLINE, ProQuest Central, Ovid Embase and Cochrane Library (via Wiley). Additionally, we will forward citation searches of all included studies. No geographical or study design exclusion criteria will be used. Studies examining disaster triage, published before 1990, and non-English language literature will be excluded. Data will be managed using online management tools; extracted data will be independently confirmed by a separate reviewer using prepiloted extraction forms. Cohen's kappa will be used to examine inter-rater agreement on pilot and final screening. Quantitative data will be expressed using measures of range and central tendency, counts, proportions and percentages, as appropriate. Qualitative data will be narrative summaries of the authors' primary findings. PATIENT AND PUBLIC INVOLVEMENT No patients involved. ETHICS AND DISSEMINATION No ethics approval is required. Findings will be submitted to peer-reviewed conferences and journals. Results will be disseminated using individual and institutional social media platforms.
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Affiliation(s)
- Christopher Thomas Picard
- Faculty of Nursing, University of Alberta, Edmonton, Alberta, Canada
- Royal Alexandra Hospital, Emergency, Alberta Health Services, Edmonton, Alberta, Canada
| | - Manal Kleib
- Faculty of Nursing, University of Alberta, Edmonton, Alberta, Canada
| | - Hannah M O'Rourke
- Faculty of Nursing, University of Alberta, Edmonton, Alberta, Canada
| | - Colleen M Norris
- Faculty of Nursing, University of Alberta, Edmonton, Alberta, Canada
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Matthew J Douma
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
- School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, Ireland
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Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions. JOURNAL OF SURGICAL EDUCATION 2022; 79:500-515. [PMID: 34756807 DOI: 10.1016/j.jsurg.2021.09.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/21/2021] [Accepted: 09/16/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To synthesize peer-reviewed evidence related to the use of artificial intelligence (AI) in surgical education DESIGN: We conducted and reported a scoping review according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis with extension for Scoping Reviews guideline and the fourth edition of the Joanna Briggs Institute Reviewer's Manual. We systematically searched eight interdisciplinary databases including MEDLINE-Ovid, ERIC, EMBASE, CINAHL, Web of Science: Core Collection, Compendex, Scopus, and IEEE Xplore. Databases were searched from inception until the date of search on April 13, 2021. SETTING/PARTICIPANTS We only examined original, peer-reviewed interventional studies that self-described as AI interventions, focused on medical education, and were relevant to surgical trainees (defined as medical or dental students, postgraduate residents, or surgical fellows) within the title and abstract (see Table 2). Animal, cadaveric, and in vivo studies were not eligible for inclusion. RESULTS After systematically searching eight databases and 4255 citations, our scoping review identified 49 studies relevant to artificial intelligence in surgical education. We found diverse interventions related to the evaluation of surgical competency, personalization of surgical education, and improvement of surgical education materials across surgical specialties. Many studies used existing surgical education materials, such as the Objective Structured Assessment of Technical Skills framework or the JHU-ISI Gesture and Skill Assessment Working Set database. Though most studies did not provide outcomes related to the implementation in medical schools (such as cost-effective analyses or trainee feedback), there are numerous promising interventions. In particular, many studies noted high accuracy in the objective characterization of surgical skill sets. These interventions could be further used to identify at-risk surgical trainees or evaluate teaching methods. CONCLUSIONS There are promising applications for AI in surgical education, particularly for the assessment of surgical competencies, though further evidence is needed regarding implementation and applicability.
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Affiliation(s)
| | - Dylan Young
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada
| | - Shawn Khan
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Noelle Crasto
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada
| | - Mara Sobel
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Ryerson University and St. Michael's Hospital, Toronto, Ontario, Canada
| | - Dafna Sussman
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Ryerson University and St. Michael's Hospital, Toronto, Ontario, Canada; Department of Obstetrics and Gynaecology, University of Toronto, Toronto, Ontario, Canada; The Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Ontario, Canada
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Ma K, Harmon SA, Klyuzhin IS, Rahmim A, Turkbey B. Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer. PET Clin 2021; 17:137-143. [PMID: 34809863 DOI: 10.1016/j.cpet.2021.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PET imaging with targeted novel tracers has been commonly used in the clinical management of prostate cancer. The use of artificial intelligence (AI) in PET imaging is a relatively new approach and in this review article, we will review the current trends and categorize the currently available research into the quantification of tumor burden within the organ, evaluation of metastatic disease, and translational/supplemental research which aims to improve other AI research efforts.
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Affiliation(s)
- Kevin Ma
- Artificial Intelligence Resource, Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
| | - Stephanie A Harmon
- Artificial Intelligence Resource, Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
| | - Ivan S Klyuzhin
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada; Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada; Department of Physics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Baris Turkbey
- Artificial Intelligence Resource, Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA.
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Mangus CW, Mahajan P. Decision Making: Healthy Heuristics and Betraying Biases. Crit Care Clin 2021; 38:37-49. [PMID: 34794630 DOI: 10.1016/j.ccc.2021.07.002] [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: 11/25/2022]
Abstract
Critical care settings are unpredictable, dynamic environments where clinicians face high decision density in suboptimal conditions (stress, time constraints, competing priorities). Experts have described two systems of human decision making: one fast and intuitive; the other slow and methodical. Heuristics, or mental shortcuts, a key feature of intuitive reasoning, are often accurate, applied instinctively, and essential for efficient diagnostic decision making. Heuristics are also prone to failures, or cognitive biases, which can lead to diagnostic errors. A variety of strategies have been proposed to mitigate biases; however, current understanding of such interventions to optimize diagnostic safety is still incomplete.
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Affiliation(s)
- Courtney W Mangus
- Departments of Emergency Medicine and Pediatrics, University of Michigan, 1540 East Hospital Drive, CW 2-737, SPC 4260, Ann Arbor, MI 48109-4260, USA.
| | - Prashant Mahajan
- Departments of Emergency Medicine and Pediatrics, University of Michigan, 1540 East Hospital Drive, CW 2-737, SPC 4260, Ann Arbor, MI 48109-4260, USA
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Jud P, Hackl G, Reisinger AC, Horvath A, Eller P, Stadlbauer V. Red urine and a red herring - diagnosing rare diseases in the light of the COVID-19 pandemic. ZEITSCHRIFT FUR GASTROENTEROLOGIE 2021; 60:1326-1331. [PMID: 34768287 PMCID: PMC9470277 DOI: 10.1055/a-1659-4481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background
The COVID-19 pandemic has occupied the time and resources of health care professionals for more than 1 year. The risk of missed diagnoses has been discussed in the medical literature, mainly for common diseases such as cancer and cardiovascular events. However, rare diseases also need appropriate attention in times of a pandemic.
Case Report
We report a 34-year-old woman with fever, pinprick sensation in her chest and thoracic spine, and dizziness after receiving the first dose of ChAdOx1 nCoV-19 vaccination. The patient’s condition worsened with abdominal pain, red urine, and hyponatremia, needing intensive care admission. Syndrome of inappropriate antidiuretic hormone secretion (SIADH) was diagnosed. Vaccine-induced thrombocytopenia and thrombosis were ruled out. Acute hepatic porphyria was finally diagnosed, and the patient recovered completely after treatment with hemin.
Conclusion
Currently, the focus of physicians is on COVID-19 and associated medical problems, such as vaccine side effects. However, it is important to be vigilant for other uncommon medical emergencies in medically exceptional situations that may shift our perception.
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Affiliation(s)
- Philipp Jud
- Department of Angiology, Medical University of Graz, Graz, Austria
| | - Gerald Hackl
- Intensive Care Unit, Medical University of Graz, Graz, Austria
| | | | - Angela Horvath
- Department of Gastroenterology and Hepatology, Medical University of Graz, Graz, Austria.,CBMed Center of Biomarker Research, Graz, Austria
| | - Philipp Eller
- Intensive Care Unit, Medical University of Graz, Graz, Austria
| | - Vanessa Stadlbauer
- Department of Gastroenterology and Hepatology, Medical University of Graz, Graz, Austria.,CBMed Center of Biomarker Research, Graz, Austria
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Lee S, Lam SH, Hernandes Rocha TA, Fleischman RJ, Staton CA, Taylor R, Limkakeng AT. Machine Learning and Precision Medicine in Emergency Medicine: The Basics. Cureus 2021; 13:e17636. [PMID: 34646684 PMCID: PMC8485701 DOI: 10.7759/cureus.17636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2021] [Indexed: 12/28/2022] Open
Abstract
As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.
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Affiliation(s)
- Sangil Lee
- Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, USA
| | - Samuel H Lam
- Emergency Medicine, Sutter Medical Center, Sacramento, USA
| | | | | | - Catherine A Staton
- Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Durham, USA
| | - Richard Taylor
- Department of Emergency Medicine, Yale University, New Haven, USA
| | - Alexander T Limkakeng
- Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Durham, USA
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