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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [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: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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Phan TC, Pranata A, Farragher J, Bryant A, Nguyen HT, Chai R. Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain. SENSORS (BASEL, SWITZERLAND) 2024; 24:1337. [PMID: 38400495 PMCID: PMC10891548 DOI: 10.3390/s24041337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 02/08/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.
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Affiliation(s)
- Trung C. Phan
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; (T.C.P.); (A.P.); (H.T.N.)
| | - Adrian Pranata
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; (T.C.P.); (A.P.); (H.T.N.)
- School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China;
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC 3000, Australia
| | - Joshua Farragher
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China;
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC 3000, Australia
| | - Adam Bryant
- Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Melbourne, VIC 3010, Australia;
| | - Hung T. Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; (T.C.P.); (A.P.); (H.T.N.)
| | - Rifai Chai
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; (T.C.P.); (A.P.); (H.T.N.)
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McDermott MBA, Nestor B, Szolovits P. Clinical Artificial Intelligence: Design Principles and Fallacies. Clin Lab Med 2023; 43:29-46. [PMID: 36764807 DOI: 10.1016/j.cll.2022.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Clinical artificial intelligence (AI)/machine learning (ML) is anticipated to offer new abilities in clinical decision support, diagnostic reasoning, precision medicine, clinical operational support, and clinical research, but careful concern is needed to ensure these technologies work effectively in the clinic. Here, we detail the clinical ML/AI design process, identifying several key questions and detailing several common forms of issues that arise with ML tools, as motivated by real-world examples, such that clinicians and researchers can better anticipate and correct for such issues in their own use of ML/AI techniques.
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Affiliation(s)
| | - Bret Nestor
- Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada
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Bacchi S, Oakden-Rayner L, Menon DK, Moey A, Jannes J, Kleinig T, Koblar S. Prospective and external validation of stroke discharge planning machine learning models. J Clin Neurosci 2022; 96:80-84. [PMID: 34999495 DOI: 10.1016/j.jocn.2021.12.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 11/10/2021] [Accepted: 12/25/2021] [Indexed: 11/18/2022]
Abstract
Machine learning may be able to help with predicting factors that aid in discharge planning for stroke patients. This study aims to validate previously derived models, on external and prospective datasets, for the prediction of discharge modified Rankin scale (mRS), discharge destination, survival to discharge and length of stay. Data were collected from consecutive patients admitted with ischaemic or haemorrhagic stroke at the Royal Adelaide Hospital from September 2019 to January 2020, and at the Lyell McEwin Hospital from January 2017 to January 2020. The previously derived models were then applied to these datasets with three pre-defined cut-off scores (high-sensitivity, Youden's index, and high-specificity) to return indicators of performance including area under the receiver operator curve (AUC), sensitivity and specificity. The number of individuals included in the prospective and external datasets were 334 and 824 respectively. The models performed well on both the prospective and external datasets in the prediction of discharge mRS ≤ 2 (AUC 0.85 and 0.87), discharge destination to home (AUC 0.76 and 0.78) and survival to discharge (AUC 0.91 and 0.92). Accurate prediction of length of stay with only admission data remains difficult (AUC 0.62 and 0.66). This study demonstrates successful prospective and external validation of machine learning models using six variables to predict information relevant to discharge planning for stroke patients. Further research is required to demonstrate patient or system benefits following implementation of these models.
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Affiliation(s)
- Stephen Bacchi
- Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, Adelaide SA 5005, Australia; South Australian Health and Medical Research Institute, Adelaide SA 5000, Australia.
| | - Luke Oakden-Rayner
- Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, Adelaide SA 5005, Australia
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Andrew Moey
- Lyell McEwin Hospital, Adelaide SA 5112, Australia
| | - Jim Jannes
- Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, Adelaide SA 5005, Australia
| | - Timothy Kleinig
- Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, Adelaide SA 5005, Australia
| | - Simon Koblar
- Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, Adelaide SA 5005, Australia; South Australian Health and Medical Research Institute, Adelaide SA 5000, Australia
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Aldhoayan MD, Al Harbi AS, Arajhi K, Abduljawad J. Statistical and machine learning analysis of non-clinical factors impacting emergency room delays. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
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