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Article ; Online: Artificial Intelligence Decision Support for Medical Triage.

Marchiori, Chiara / Dykeman, Douglas / Girardi, Ivan / Ivankay, Adam / Thandiackal, Kevin / Zusag, Mario / Giovannini, Andrea / Karpati, Daniel / Saenz, Henri

AMIA ... Annual Symposium proceedings. AMIA Symposium

2021 Volume 2020, Page(s) 793–802

Abstract: ... of AI-based decision support systems. Providing such remote guidance at the beginning of the chain ... million of teleconsultation records, we developed a triage system, now certified and in use at the largest ... via a mobile application. Reasoning on an initial set of provided symptoms, the triage application ...

Abstract	Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak.
MeSH term(s)	Algorithms ; Artificial Intelligence ; COVID-19/epidemiology ; COVID-19/prevention & control ; Decision Support Systems, Management ; Expert Systems ; Humans ; Remote Consultation ; SARS-CoV-2 ; Telemedicine ; Triage
Language	English
Publishing date	2021-01-25
Publishing country	United States
Document type	Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Artificial Intelligence Decision Support for Medical Triage

Marchiori, Chiara / Dykeman, Douglas / Girardi, Ivan / Ivankay, Adam / Thandiackal, Kevin / Zusag, Mario / Giovannini, Andrea / Karpati, Daniel / Saenz, Henri

2020

Abstract	Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak. Comment: 10 pages, 5 figures, accepted to AMIA 2020
Keywords	Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Machine Learning
Publishing date	2020-11-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Artificial Intelligence Decision Support for Medical Triage

Marchiori, Chiara / Dykeman, Douglas / Girardi, Ivan / Ivankay, Adam / Thandiackal, Kevin / Zusag, Mario / Giovannini, Andrea / Karpati, Daniel / Saenz, Henri

Abstract	Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak.
Keywords	covid19
Publisher	ArXiv
Document type	Article
Database	COVID19

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Article ; Online: Artificial Intelligence in Lung Imaging.

Choe, Jooae / Lee, Sang Min / Hwang, Hye Jeon / Yun, Jihye / Kim, Namkug / Seo, Joon Beom

Seminars in respiratory and critical care medicine

2022 Volume 43, Issue 6, Page(s) 946–960

Abstract: Recently, interest and advances in artificial intelligence (AI) including deep learning for medical ... quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is ... purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis ...

Abstract	Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.
MeSH term(s)	Humans ; Artificial Intelligence ; Radiology/methods ; Radiologists ; Diagnostic Imaging ; Lung/diagnostic imaging
Language	English
Publishing date	2022-09-29
Publishing country	United States
Document type	Review ; Journal Article
ZDB-ID	1183617-9
ISSN	1098-9048 ; 1069-3424
ISSN (online)	1098-9048
ISSN	1069-3424
DOI	10.1055/s-0042-1755571
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Zs.A 1555: Show issues

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Article ; Online: Assessing the precision of artificial intelligence in ED triage decisions: Insights from a study with ChatGPT.

Paslı, Sinan / Şahin, Abdul Samet / Beşer, Muhammet Fatih / Topçuoğlu, Hazal / Yadigaroğlu, Metin / İmamoğlu, Melih

The American journal of emergency medicine

2024 Volume 78, Page(s) 170–175

Abstract: ... complaints, vital parameters, medical history and the area to which they were directed by the triage team ... into previously trained GPT-4, according to local rules. According to this data, the triage decisions made by GPT ... endpoints of our study - the agreement between the decisions of the triage team, GPT-4 decisions ...

Abstract	Background: The rise in emergency department presentations globally poses challenges for efficient patient management. To address this, various strategies aim to expedite patient management. Artificial intelligence's (AI) consistent performance and rapid data interpretation extend its healthcare applications, especially in emergencies. The introduction of a robust AI tool like ChatGPT, based on GPT-4 developed by OpenAI, can benefit patients and healthcare professionals by improving the speed and accuracy of resource allocation. This study examines ChatGPT's capability to predict triage outcomes based on local emergency department rules. Methods: This study is a single-center prospective observational study. The study population consists of all patients who presented to the emergency department with any symptoms and agreed to participate. The study was conducted on three non-consecutive days for a total of 72 h. Patients' chief complaints, vital parameters, medical history and the area to which they were directed by the triage team in the emergency department were recorded. Concurrently, an emergency medicine physician inputted the same data into previously trained GPT-4, according to local rules. According to this data, the triage decisions made by GPT-4 were recorded. In the same process, an emergency medicine specialist determined where the patient should be directed based on the data collected, and this decision was considered the gold standard. Accuracy rates and reliability for directing patients to specific areas by the triage team and GPT-4 were evaluated using Cohen's kappa test. Furthermore, the accuracy of the patient triage process performed by the triage team and GPT-4 was assessed by receiver operating characteristic (ROC) analysis. Statistical analysis considered a value of p < 0.05 as significant. Results: The study was carried out on 758 patients. Among the participants, 416 (54.9%) were male and 342 (45.1%) were female. Evaluating the primary endpoints of our study - the agreement between the decisions of the triage team, GPT-4 decisions in emergency department triage, and the gold standard - we observed almost perfect agreement both between the triage team and the gold standard and between GPT-4 and the gold standard (Cohen's Kappa 0.893 and 0.899, respectively; p < 0.001 for each). Conclusion: Our findings suggest GPT-4 possess outstanding predictive skills in triaging patients in an emergency setting. GPT-4 can serve as an effective tool to support the triage process.
MeSH term(s)	Female ; Humans ; Male ; Artificial Intelligence ; Emergency Medicine ; Emergency Service, Hospital ; Reproducibility of Results ; Triage ; Prospective Studies
Language	English
Publishing date	2024-01-24
Publishing country	United States
Document type	Observational Study ; Journal Article
ZDB-ID	605890-5
ISSN	1532-8171 ; 0735-6757
ISSN (online)	1532-8171
ISSN	0735-6757
DOI	10.1016/j.ajem.2024.01.037
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Zs.A 2005: Show issues

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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Artificial intelligence to advance acute and intensive care medicine.

Biesheuvel, Laurens A / Dongelmans, Dave A / Elbers, Paul W G

Current opinion in critical care

2024 Volume 30, Issue 3, Page(s) 246–250

Abstract: ... with its integration into emergency medical dispatch, triage, medical consultation and ICUs.: Recent findings ... data for comprehensive decision support. In the ICU, artificial intelligence applications range ... The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate ...

Abstract	Purpose of review: This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs. Recent findings: The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate assessment. In the emergency department (ED), artificial intelligence driven triage models leverage diverse patient data for improved outcome predictions, surpassing human performance in retrospective studies. Artificial intelligence can streamline medical documentation in the ED and enhances medical imaging interpretation. The introduction of large multimodal generative models showcases the future potential to process varied biomedical data for comprehensive decision support. In the ICU, artificial intelligence applications range from early warning systems to treatment suggestions. Summary: Despite promising academic strides, widespread artificial intelligence adoption in acute and critical care is hindered by ethical, legal, technical, organizational, and validation challenges. Despite these obstacles, artificial intelligence's potential to streamline clinical workflows is evident. When these barriers are overcome, future advancements in artificial intelligence have the potential to transform the landscape of patient care for acute and intensive care medicine.
MeSH term(s)	Humans ; Artificial Intelligence/trends ; Critical Care ; Triage/methods ; Emergency Service, Hospital/organization & administration ; Intensive Care Units/organization & administration
Language	English
Publishing date	2024-03-22
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	1235629-3
ISSN	1531-7072 ; 1070-5295
ISSN (online)	1531-7072
ISSN	1070-5295
DOI	10.1097/MCC.0000000000001150
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Zs.A 4999: Show issues

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Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article: Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education.

Hamilton, Allan

Cureus

2024 Volume 16, Issue 5, Page(s) e59747

Abstract: ... students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under ... triage decisions, and improved outcomes from rapid response teams. However, the issue of bias ... deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS ...

Abstract	The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.
Language	English
Publishing date	2024-05-06
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2747273-5
ISSN	2168-8184
ISSN	2168-8184
DOI	10.7759/cureus.59747
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Artificial intelligence in emergency medicine. A systematic literature review.

Piliuk, Konstantin / Tomforde, Sven

International journal of medical informatics

2023 Volume 180, Page(s) 105274

Abstract: ... decision support. The latter covers such applications as mortality, outcome, admission prediction, condition ... specific and triage-specific. The former ones are focused on either diagnosis prediction or ... within a single disease or medical operation and often use privately collected retrospective data, making ...

Abstract	Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies. Methods: The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms. Findings and discussion: The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction. Conclusion: Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.
MeSH term(s)	Humans ; Artificial Intelligence ; Retrospective Studies ; Algorithms ; Emergency Medicine ; Machine Learning
Language	English
Publishing date	2023-10-31
Publishing country	Ireland
Document type	Systematic Review ; Journal Article ; Review
ZDB-ID	1466296-6
ISSN	1872-8243 ; 1386-5056
ISSN (online)	1872-8243
ISSN	1386-5056
DOI	10.1016/j.ijmedinf.2023.105274
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Article: Artificial Intelligence in Lung Imaging

Choe, Jooae / Lee, Sang Min / Hwang, Hye Jeon / Yun, Jihye / Kim, Namkug / Seo, Joon Beom

Seminars in Respiratory and Critical Care Medicine

(Chest Imaging)

2022 Volume 43, Issue 06, Page(s) 946–960

Series title	Chest Imaging
Abstract	Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.
Keywords	artificial intelligence ; deep learning ; chest radiograph ; computed tomography
Language	English
Publishing date	2022-09-29
Publisher	Thieme Medical Publishers, Inc.
Publishing place	Stuttgart ; New York
Document type	Article
ZDB-ID	1183617-9
ISSN	1098-9048 ; 1069-3424
ISSN (online)	1098-9048
ISSN	1069-3424
DOI	10.1055/s-0042-1755571
Database	Thieme publisher's database

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Zs.A 1555: Show issues

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Article ; Online: Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.

Abdel-Hafez, Ahmad / Jones, Melanie / Ebrahimabadi, Maziiar / Ryan, Cathi / Graham, Steve / Slee, Nicola / Whitfield, Bernard

Frontiers in digital health

2023 Volume 5, Page(s) 1192975

Abstract: ... to their clinical urgency. Medical referrals are manually triaged and prioritised into three categories ... artificial intelligence to automate the process of categorizing medical referrals based on clinical prioritization ... The clinical prioritisation criteria (CPC) are a clinical decision support tool that ensures ...

Abstract	The clinical prioritisation criteria (CPC) are a clinical decision support tool that ensures patients referred for public specialist outpatient services to Queensland Health are assessed according to their clinical urgency. Medical referrals are manually triaged and prioritised into three categories by the associated health service before appointments are booked. We have developed a method using artificial intelligence to automate the process of categorizing medical referrals based on clinical prioritization criteria (CPC) guidelines. Using machine learning techniques, we have created a tool that can assist clinicians in sorting through the substantial number of referrals they receive each year, leading to more efficient use of clinical specialists' time and improved access to healthcare for patients. Our research included analyzing 17,378 ENT referrals from two hospitals in Queensland between 2019 and 2022. Our results show a level of agreement between referral categories and generated predictions of 53.8%.
Language	English
Publishing date	2023-10-27
Publishing country	Switzerland
Document type	Journal Article
ISSN	2673-253X
ISSN (online)	2673-253X
DOI	10.3389/fdgth.2023.1192975
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