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Schroeder KM, Elkassabany N. Artificial intelligence and regional anesthesiology education curriculum development: navigating the digital noise. Reg Anesth Pain Med 2024:rapm-2024-105522. [PMID: 38876802 DOI: 10.1136/rapm-2024-105522] [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: 03/30/2024] [Accepted: 06/04/2024] [Indexed: 06/16/2024]
Abstract
Artificial intelligence (AI) has demonstrated a disruptive ability to enhance and transform clinical medicine. While the dexterous nature of anesthesiology work offers some protections from AI clinical assimilation, this technology will ultimately impact the practice and augment the ability to provide an enhanced level of safe and data-driven care. Whether predicting difficulties with airway management, providing perioperative or critical care risk assessments, clinical-decision enhancement, or image interpretation, the indications for AI technologies will continue to grow and are limited only by our collective imagination on how best to deploy this technology.An essential mission of academia is education, and challenges are frequently encountered when working to develop and implement comprehensive and effectively targeted curriculum appropriate for the diverse set of learners assigned to teaching faculty. Curriculum development in this context frequently requires substantial efforts to identify baseline knowledge, learning needs, content requirement, and education strategies. Large language models offer the promise of targeted and nimble curriculum and content development that can be individualized to a variety of learners at various stages of training. This technology has not yet been widely evaluated in the context of education deployment, but it is imperative that consideration be given to the role of AI in curriculum development and how best to deploy and monitor this technology to ensure optimal implementation.
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Affiliation(s)
| | - Nabil Elkassabany
- University of Virginia School of Medicine, Charlottesville, Virginia, USA
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Duggal A, Scheraga R, Sacha GL, Wang X, Huang S, Krishnan S, Siuba MT, Torbic H, Dugar S, Mucha S, Veith J, Mireles-Cabodevila E, Bauer SR, Kethireddy S, Vachharajani V, Dalton JE. Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit. BMJ Open 2024; 14:e079243. [PMID: 38320842 PMCID: PMC10860023 DOI: 10.1136/bmjopen-2023-079243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/22/2024] [Indexed: 02/15/2024] Open
Abstract
OBJECTIVE Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness. DESIGN We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm. SETTING AND PARTICIPANTS We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022. RESULTS 5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states. CONCLUSION We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.
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Affiliation(s)
- Abhijit Duggal
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Rachel Scheraga
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Xiaofeng Wang
- Department of Qualitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
| | - Shuaqui Huang
- Department of Qualitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
| | - Sudhir Krishnan
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Matthew T Siuba
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Heather Torbic
- Department of Pharmacy, Cleveland Clinic, Cleveland, Ohio, USA
| | - Siddharth Dugar
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Simon Mucha
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Joshua Veith
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Seth R Bauer
- Department of Pharmacy, Cleveland Clinic, Cleveland, Ohio, USA
| | | | | | - Jarrod E Dalton
- Department of Qualitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
- Cleveland Clinic, Cleveland, Ohio, USA
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Song B, Zhou M, Zhu J. Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security. Med Sci Monit 2023; 29:e938835. [PMID: 36810475 PMCID: PMC9969716 DOI: 10.12659/msm.938835] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The rapid development of artificial intelligence (AI) technology is due to the significant progress in big data, databases, algorithms, and computing power, and medical research is a vital application direction of AI. The integrated development of AI and medicine has improved medical technology, and the efficiency of medical services and equipment has enabled doctors to better serve patients. The tasks and characteristics of the anesthesia discipline also make AI necessary for its development, and AI has also been initially applied in different fields of anesthesia. Our review aims to clarify the current situation and challenges of AI application in anesthesiology to provide clinical references and guide the future development of AI in anesthesiology. This review summarizes progress in the application of AI in perioperative risk assessment and prediction, deep monitoring and regulation of anesthesia, essential anesthesia skills operation, automatic drug administration systems, and teaching and training in anesthesia. Also discussed herein are the accompanying risks and challenges of applying AI in anesthesia: patient privacy and information security, data sources, and ethical issues, lack of capital and talent, and the "black box" phenomenon.
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Affiliation(s)
- Bijia Song
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Ming Zhou
- Department of Information, Beijing University of Technology, Beijing, PR China
| | - Junchao Zhu
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China
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Prediction and Prevention of Intraoperative Hypotension with the Hypotension Prediction Index: A Narrative Review. J Clin Med 2022; 11:jcm11195551. [PMID: 36233419 PMCID: PMC9571689 DOI: 10.3390/jcm11195551] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
Intraoperative hypotension is common and has been associated with adverse events. Although association does not imply causation, predicting and preventing hypotension may improve postoperative outcomes. This review summarizes current evidence on the development and validation of an artificial intelligence predictive algorithm, the Hypotension Prediction (HPI) (formerly known as the Hypotension Probability Indicator). This machine learning model can arguably predict hypotension up to 15 min before its occurrence. Several validation studies, retrospective cohorts, as well as a few prospective randomized trials, have been published in the last years, reporting promising results. Larger trials are needed to definitively assess the usefulness of this algorithm in optimizing postoperative outcomes.
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Abstract
The application of artificial intelligence (AI) is currently changing very different areas of life. Artificial intelligence involves the emulation of human behavior with the aid of methods from mathematics and informatics. Machine learning (ML) represents a subdivision of AI. Algorithms for ML have the potential to optimize patient care, in that they can be utilized in a supportive way in personalized medicine, decision making and risk prediction. Although the majority of the applications in medicine are still limited to data analysis and research, it is certain that ML will become increasingly more important in scientific and clinical aspects in this supportive function. Therefore, it is necessary for clinicians to have at least a basic understanding of the functional principles, strengths and weaknesses of ML.
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Affiliation(s)
- J Sassenscheidt
- Klinik und Poliklinik für Anästhesiologie, Zentrum für Anästhesiologie und Intensivmedizin, Martinistr. 52, 20246, Hamburg, Deutschland
- Abteilung für Anästhesiologie, Intensivmedizin, Notfallmedizin, Schmerztherapie, Asklepios Klinik Altona, Paul-Ehrlich-Straße 1, 22763, Hamburg, Deutschland
| | - B Jungwirth
- Klinik für Anästhesiologie, Universitätsklinikum Ulm, 89070, Ulm, Deutschland
| | - J C Kubitz
- Klinik und Poliklinik für Anästhesiologie, Zentrum für Anästhesiologie und Intensivmedizin, Martinistr. 52, 20246, Hamburg, Deutschland.
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van der Ven WH, Veelo DP, Wijnberge M, van der Ster BJP, Vlaar APJ, Geerts BF. One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making. Surgery 2020; 169:1300-1303. [PMID: 33309616 DOI: 10.1016/j.surg.2020.09.041] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/04/2020] [Accepted: 09/07/2020] [Indexed: 01/24/2023]
Abstract
This review describes the steps and conclusions from the development and validation of an artificial intelligence algorithm (the Hypotension Prediction Index), one of the first machine learning predictive algorithms used in the operating room environment. The algorithm has been demonstrated to reduce intraoperative hypotension in two randomized controlled trials via real-time prediction of upcoming hypotensive events prompting anesthesiologists to act earlier, more often, and differently in managing impending hypotension. However, the algorithm entails no dynamic learning process that evolves from use in clinical patient care, meaning the algorithm is fixed, and furthermore provides no insight into the decisional process that leads to an early warning for intraoperative hypotension, which makes the algorithm a "black box." Many other artificial intelligence machine learning algorithms have these same disadvantages. Clinical validation of such algorithms is relatively new and requires more standardization, as guidelines are lacking or only now start to be drafted. Before adaptation in clinical practice, impact of artificial intelligence algorithms on clinical behavior, outcomes and economic advantages should be studied too.
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Affiliation(s)
- Ward H van der Ven
- Amsterdam UMC, University of Amsterdam, Department of Anesthesiology, Amsterdam, Netherlands
| | - Denise P Veelo
- Amsterdam UMC, University of Amsterdam, Department of Anesthesiology, Amsterdam, Netherlands
| | - Marije Wijnberge
- Amsterdam UMC, University of Amsterdam, Department of Anesthesiology, Amsterdam, Netherlands
| | - Björn J P van der Ster
- Amsterdam UMC, University of Amsterdam, Department of Anesthesiology, Amsterdam, Netherlands
| | - Alexander P J Vlaar
- Amsterdam UMC, University of Amsterdam, Department of Intensive Care, Amsterdam, Netherlands; Amsterdam UMC, University of Amsterdam, Laboratory of Experimental Intensive Care and Anesthesiology, Amsterdam, Netherlands.
| | - Bart F Geerts
- Amsterdam UMC, University of Amsterdam, Department of Anesthesiology, Amsterdam, Netherlands
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The present and future role of artificial intelligence and machine learning in anesthesiology. Int Anesthesiol Clin 2020; 58:7-16. [PMID: 32841964 DOI: 10.1097/aia.0000000000000294] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Bowness J, El-Boghdadly K, Burckett-St Laurent D. Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia. Anaesthesia 2020; 76:602-607. [PMID: 32726498 DOI: 10.1111/anae.15212] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2020] [Indexed: 12/12/2022]
Affiliation(s)
- J Bowness
- Institute of Academic Anaesthesia, University of Dundee, Dundee, UK.,Department of Anaesthesia, Ninewells Hospital, Dundee, UK
| | - K El-Boghdadly
- Department of Anaesthesia and Peri-operative Medicine, Guy's and St Thomas's NHS Foundation Trust, London, UK.,King's College London, London, UK
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Char DS, Burgart A. Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges. Anesth Analg 2020; 130:1709-1712. [PMID: 31922996 DOI: 10.1213/ane.0000000000004656] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Danton S Char
- From the Division of Pediatric Anesthesia, Department of Anesthesia, and Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California
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Abstract
Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated.The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them-perhaps bringing anesthesiology into an era of machine-assisted discovery.
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