1
|
Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel) 2024; 14:1456. [PMID: 39001346 PMCID: PMC11240935 DOI: 10.3390/diagnostics14131456] [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: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
Collapse
Affiliation(s)
- Jin Y Chang
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| |
Collapse
|
2
|
Shen TT, Liu CF, Wu MP. Implementation of a machine learning model in acute coronary syndrome and stroke risk assessment for patients with lower urinary tract symptoms. Taiwan J Obstet Gynecol 2024; 63:518-526. [PMID: 39004479 DOI: 10.1016/j.tjog.2024.01.037] [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] [Accepted: 01/03/2024] [Indexed: 07/16/2024] Open
Abstract
OBJECTIVE The global population is aging and the burden of lower urinary tract symptoms (LUTS) is expected to increase. According to the National Health Insurance Research Database, our previous studies have showed LUTS may predispose patients to cardiovascular disease. However, it is difficult to provide a personalized risk assessment in the context of "having acute coronary syndrome (ACS) and stroke." This study aimed to develop an artificial intelligence (AI)-based prediction model for patients with LUTS. MATERIAL AND METHODS We retrospectively reviewed the electronic medical records of 1799 patients with LUTS at Chi Mei Medical Center between January 1, 2001 and December, 31, 2018. Features with >10 cases and high correlations with outcomes were imported into six machine learning algorithms. The study outcomes included ACS and stroke. Model performances was evaluated using the area under the receiver operating characteristic curve (AUC). The model with the highest AUC was used to implement the clinical risk prediction application. RESULTS Age, systemic blood pressure, diastolic blood pressure, creatinine, glycated hemoglobin, hypertension, diabetes mellitus and hyperlipidemia were the most relevant features that affect the outcomes. Based on the AUC, our optimal model was built using multilayer perception (AUC = 0.803) to predict ACS and stroke events within 3 years. CONCLUSION We successfully built an AI-based prediction system that can be used as a prediction model to achieve time-saving, precise, personalized risk evaluation; it can also be used to offer warning, enhance patient adherence, early intervention and better health care outcomes.
Collapse
Affiliation(s)
- Tzu-Tsen Shen
- Division of Urogynecology, Department of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Ming-Ping Wu
- Division of Urogynecology, Department of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan; Department of Post-Baccalaureate Medicine, School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan.
| |
Collapse
|
3
|
Vaidya YP, Shumway SJ. Artificial intelligence: The future of cardiothoracic surgery. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00371-4. [PMID: 38685465 DOI: 10.1016/j.jtcvs.2024.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024]
Affiliation(s)
- Yash Pradeep Vaidya
- Department of Cardiothoracic Surgery, University of Minnesota, Minneapolis, Minn.
| | - Sara Jane Shumway
- Department of Cardiothoracic Surgery, University of Minnesota, Minneapolis, Minn
| |
Collapse
|
4
|
Bassi M, Vaz Sousa R, Zacchini B, Centofanti A, Ferrante F, Poggi C, Carillo C, Pecoraro Y, Amore D, Diso D, Anile M, De Giacomo T, Venuta F, Vannucci J. Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment. Healthcare (Basel) 2024; 12:803. [PMID: 38610225 PMCID: PMC11011722 DOI: 10.3390/healthcare12070803] [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: 01/07/2024] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.
Collapse
Affiliation(s)
- Massimiliano Bassi
- Division of Thoracic Surgery, Department of General Surgery and Surgical Specialties “Paride Stefanini”, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
| | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
5
|
Stivi T, Padawer D, Dirini N, Nachshon A, Batzofin BM, Ledot S. Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. J Clin Med 2024; 13:1505. [PMID: 38592696 PMCID: PMC10934889 DOI: 10.3390/jcm13051505] [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: 02/01/2024] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 04/10/2024] Open
Abstract
The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient's MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.
Collapse
Affiliation(s)
- Tamar Stivi
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Dan Padawer
- Department of Pulmonary Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel;
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
| | - Noor Dirini
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Akiva Nachshon
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Baruch M. Batzofin
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Stephane Ledot
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
| |
Collapse
|
6
|
Abbaker N, Minervini F, Guttadauro A, Solli P, Cioffi U, Scarci M. The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review. Front Oncol 2024; 14:1347464. [PMID: 38414748 PMCID: PMC10897973 DOI: 10.3389/fonc.2024.1347464] [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: 11/30/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024] Open
Abstract
Objectives To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases. Methods A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer. Conclusion While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI's ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.
Collapse
Affiliation(s)
- Namariq Abbaker
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom
| | - Fabrizio Minervini
- Division of Thoracic Surgery, Luzerner Kantonsspital, Lucern, Switzerland
| | - Angelo Guttadauro
- Division of Surgery, Università Milano-Bicocca and Istituti Clinici Zucchi, Monza, Italy
| | - Piergiorgio Solli
- Division of Thoracic Surgery, Policlinico S. Orsola-Malpighi, Bologna, Italy
| | - Ugo Cioffi
- Department of Surgery, University of Milan, Milan, Italy
| | - Marco Scarci
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom
| |
Collapse
|
7
|
Liu MY, Liu CF, Lin TC, Ma YS. Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control. Bioengineering (Basel) 2023; 10:1139. [PMID: 37892869 PMCID: PMC10604578 DOI: 10.3390/bioengineering10101139] [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: 08/24/2023] [Revised: 09/24/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
(1) Background: Persistent hyperglycemia in diabetes mellitus (DM) increases the risk of death and causes cardiovascular disease (CVD), resulting in significant social and economic costs. This study used a machine learning (ML) technique to build prediction models with the factors of lifestyle, medication compliance, and self-control in eating habits and then implemented a predictive system based on the best model to forecast whether blood glucose can be well-controlled within 1 year in diabetic patients attending a DM nutritional clinic. (2) Methods: Data were collected from outpatients aged 20 years or older with type 2 DM who received nutrition education in Chi Mei Medical Center. Multiple ML algorithms were used to build the predictive models. (3) Results: The predictive models achieved accuracies ranging from 0.611 to 0.690. The XGBoost model with the highest area under the curve (AUC) of 0.738 was regarded as the best and used for the predictive system implementation. SHAP analysis was performed to interpret the feature importance in the best model. The predictive system, evaluated by dietitians, received positive feedback as a beneficial tool for diabetes nutrition consultations. (4) Conclusions: The ML prediction model provides a promising approach for diabetes nutrition consultations to maintain good long-term blood glucose control, reduce diabetes-related complications, and enhance the quality of medical care.
Collapse
Affiliation(s)
- Mei-Yuan Liu
- Department of Nutrition, Chi Mei Medical Center, Tainan 710402, Taiwan;
- Department of Nutrition and Health Sciences, Chia Nan University of Pharmacy & Science, Tainan 710402, Taiwan
- Department of Food Nutrition, Chung Hwa University of Medical Technology, Tainan 710402, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan;
| | - Tzu-Chi Lin
- Nursing Department, Chi Mei Medical Center, Liouying, Tainan 73657, Taiwan
| | - Yu-Shan Ma
- Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan;
| |
Collapse
|
8
|
Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data. Diagnostics (Basel) 2023; 13:diagnostics13040648. [PMID: 36832135 PMCID: PMC9954858 DOI: 10.3390/diagnostics13040648] [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: 01/02/2023] [Revised: 01/30/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Lung cancer remains the most commonly diagnosed cancer and the leading cause of death from cancer. Recent research shows that the human eye can provide useful information about one's health status, but few studies have revealed that the eye's features are associated with the risk of cancer. The aims of this paper are to explore the association between scleral features and lung neoplasms and develop a non-invasive artificial intelligence (AI) method for detecting lung neoplasms based on scleral images. A novel instrument was specially developed to take the reflection-free scleral images. Then, various algorithms and different strategies were applied to find the most effective deep learning algorithm. Ultimately, the detection method based on scleral images and the multi-instance learning (MIL) model was developed to predict benign or malignant lung neoplasms. From March 2017 to January 2019, 3923 subjects were recruited for the experiment. Using the pathological diagnosis of bronchoscopy as the gold standard, 95 participants were enrolled to take scleral image screens, and 950 scleral images were fed to AI analysis. Our non-invasive AI method had an AUC of 0.897 ± 0.041(95% CI), a sensitivity of 0.836 ± 0.048 (95% CI), and a specificity of 0.828 ± 0.095 (95% CI) for distinguishing between benign and malignant lung nodules. This study suggested that scleral features such as blood vessels may be associated with lung cancer, and the non-invasive AI method based on scleral images can assist in lung neoplasm detection. This technique may hold promise for evaluating the risk of lung cancer in an asymptomatic population in areas with a shortage of medical resources and as a cost-effective adjunctive tool for LDCT screening at hospitals.
Collapse
|
9
|
Liu CF, Hung CM, Ko SC, Cheng KC, Chao CM, Sung MI, Hsing SC, Wang JJ, Chen CJ, Lai CC, Chen CM, Chiu CC. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne) 2022; 9:935366. [PMID: 36465940 PMCID: PMC9715756 DOI: 10.3389/fmed.2022.935366] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2023] Open
Abstract
Background For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Purpose Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. Methods AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. Results A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. Conclusion We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.
Collapse
Affiliation(s)
- Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Shian-Chin Ko
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Kuo-Chen Cheng
- Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Ming Chao
- Department of Intensive Care Medicine, Chi Mei Medical Center, Liouying, Taiwan
- Department of Dental Laboratory Technology, Min-Hwei College of Health Care Management, Liouying, Taiwan
| | - Mei-I Sung
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Shu-Chen Hsing
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Jhi-Joung Wang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
- Department of Anesthesiology, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Chih-Cheng Lai
- Division of Hospital Medicine, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chin-Ming Chen
- Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Department of Medical Education and Research, E-Da Cancer Hospital, Kaohsiung, Taiwan
- Department of General Surgery, Chi Mei Medical Center, Tainan, Taiwan
| |
Collapse
|
10
|
Zhou Y, Xu M. Analysis of the Effect of Quality Nursing on Recovery after Thoracic Surgery. Emerg Med Int 2022; 2022:6204832. [PMID: 36247703 PMCID: PMC9568356 DOI: 10.1155/2022/6204832] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/08/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022] Open
Abstract
Objective To observe the feasibility and safety of rapid rehabilitation nursing in the perioperative period of thoracoscopic treatment of lung cancer patients. Rapid rehabilitation nursing was compared with conventional perioperative nursing to explore its clinical efficacy, i.e., its advantages in improving postoperative comfort, postoperative rehabilitation efficiency, and hospitalization cost of patients undergoing thoracoscopic lung cancer resection. Methods We carried out a retrospective analysis of 337 lung cancer patients who underwent lobectomy in our thoracic surgery department from July 2019 to June 2021, of which 168 lung cancer patients whose perioperative care method was traditional rehabilitation care were classified as A and 169 lung cancer patients who started to implement the intelligent medical intervention method in the department in September were classified as intelligent medical B. By reviewing patient cases and departmental statistics, general information, length of stay, hospitalization cost, complication rate, pain score, bowel movement recovery time, and pulmonary function index of the two groups A and B were compared. Nursing satisfaction was investigated by using a questionnaire. All the data in the study were processed and analyzed using SPSS 17.0 software. Results There were no differences in preoperative general data, pathological findings, preoperative underlying diseases, lesion involvement sites, and postoperative TNM stages (P > 0.05), which were comparable; the incidence of postoperative pulmonary infection and atelectasis complications, postoperative hospitalization time, and hospitalization cost were lower in group B than in group A; the postoperative chest tube drain placement time was shorter in group B than in group A, and the difference between the two groups was statistically significant (P < 0.05). The incidence of postoperative pain and discomfort in group B was lower than that in group A, and the difference between the two groups was statistically significant (P < 0.05); the incidence of postoperative chest pain, bleeding, pneumothorax, pulmonary infection, and atelectasis in group B was lower than that in group A, and the difference between the two groups was statistically significant (P < 0.05). Conclusion Intelligent medical rehabilitation nursing has good application value in thoracoscopic lung cancer surgery. Applying the concept of intelligent medical rehabilitation nursing provides an important experimental basis and theoretical basis for improving the postoperative survival quality and clinical symptoms of patients undergoing thoracoscopic lung cancer resection, which helps to promote the postoperative recovery of patients with thoracoscopic lung cancer, improves the recovery efficiency of patients and their overall quality of life, and is superior to the conventional nursing group.
Collapse
Affiliation(s)
- Yujing Zhou
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai, China
| | - Ming Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai, China
| |
Collapse
|
11
|
Taha A, Flury DV, Enodien B, Taha-Mehlitz S, Schmid RA. The development of machine learning in lung surgery: A narrative review. Front Surg 2022; 9:914903. [PMID: 36171812 PMCID: PMC9510630 DOI: 10.3389/fsurg.2022.914903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 08/22/2022] [Indexed: 11/20/2022] Open
Abstract
Background Machine learning reflects an artificial intelligence that allows applications to improve their accuracy to predict outcomes, eliminating the need to conduct explicit programming on them. The medical field has increased its focus on establishing tools for integrating machine learning algorithms in laboratory and clinical settings. Despite their importance, their incorporation is minimal in the medical sector yet. The primary goal of this study is to review the development of machine learning in the field of thoracic surgery, especially lung surgery. Methods This article used the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA). The sources used to gather data are the PubMed, Cochrane, and CINAHL databases and the Google Scholar search engine. Results The study included 19 articles, where ten concentrated on the application of machine learning in especially lung surgery, six focused on the benefits and limitations of machine learning algorithms in lung surgery, and three provided an overview of the future of machine learning in lung surgery. Conclusion The outcome of this study indicates that the field of lung surgery has attempted to integrate machine learning algorithms. However, the implementation rate is low, owing to the newness of the concept and the various challenges it encompasses. Also, this study reveals the absence of sufficient literature discussing the application of machine learning in lung surgery. The necessity for future research on the topic area remains evident.
Collapse
Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Dominik Valentin Flury
- Department of Thoracic Surgery, Hirslanden Clinic Beau-Site (Hirslanden Group) / Lindenhof Hospital (Lindenhof Group Bern); University of Bern, Bern, Switzerland
| | - Bassey Enodien
- Department of Surgery, Wetzikon Hospital, Wetzikon, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Basel, Switzerland
| | - Ralph A. Schmid
- Thorax-Schweiz, Hirslanden Cooperate Office, Glattpark, Switzerland
- Correspondence: Ralph A. Schmid
| |
Collapse
|
12
|
Wen JY, Liu CF, Chung MT, Tsai YC. Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET). Taiwan J Obstet Gynecol 2022; 61:837-846. [PMID: 36088053 DOI: 10.1016/j.tjog.2021.11.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/23/2021] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To decrease multiple pregnancy risk and sustain optimal pregnancy chance by choosing suitable number of embryos during transfer, this study aims to construct artificial intelligence models to predict the pregnancy outcome and multiple pregnancy risk after IVF-ET. MATERIALS AND METHODS From Jan 2010 to Dec 2019, 1507 fresh embryo transfer cycles contained 20 features were obtained. After eliminating incomplete records, 949 treatment cycles were included in the pregnancy model dataset and 380 cycles in the twin pregnancy model dataset. Six machine learning algorithms were used for model building based on the dataset which 70% of the dataset were randomly selected for training and 30% for validation. Model performances were quantified with the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity. RESULTS Models built with XGBoost performed best. The pregnancy prediction model produced accuracy of 0.716, sensitivity of 0.711, specificity of 0.719, and AUC of 0.787. The multiple pregnancy prediction model produced accuracy of 0.711, sensitivity of 0.649, specificity of 0.740, and AUC of 0.732. CONCLUSIONS The AI models provide reliable outcome prediction and could be a promising method to decrease multiple pregnancy risk after IVF-ET.
Collapse
Affiliation(s)
- Jen-Yu Wen
- Center for Reproductive Medicine, Depart of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan
| | - Chung-Fen Liu
- Center for Big Medical Data and AI Computing, Chi Mei Medical Center, Tainan, Taiwan
| | - Ming-Ting Chung
- Center for Reproductive Medicine, Depart of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan
| | - Yung-Chieh Tsai
- Center for Reproductive Medicine, Depart of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan; Department of Sport Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.
| |
Collapse
|
13
|
Mumtaz H, Saqib M, Ansar F, Zargar D, Hameed M, Hasan M, Muskan P. The future of Cardiothoracic surgery in Artificial intelligence. Ann Med Surg (Lond) 2022; 80:104251. [PMID: 36045824 PMCID: PMC9422274 DOI: 10.1016/j.amsu.2022.104251] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 12/23/2022] Open
Abstract
Humans' great and quick technological breakthroughs in the previous decade have undoubtedly influenced how surgical procedures are executed in the operating room. AI is becoming incredibly influential for surgical decision-making to help surgeons make better projections about the implications of surgical operations by considering different sources of data such as patient health conditions, disease natural history, patient values, and finance. Although the application of artificial intelligence in healthcare settings is rapidly increasing, its mainstream application in clinical practice remains limited. The use of machine learning algorithms in thoracic surgery is extensive, including different clinical stages. By leveraging techniques such as machine learning, computer vision, and robotics, AI may play a key role in diagnostic augmentation, operative management, pre-and post-surgical patient management, and upholding safety standards. AI, particularly in complex surgical procedures such as cardiothoracic surgery, may be a significant help to surgeons in executing more intricate surgeries with greater success, fewer complications, and ensuring patient safety, while also providing resources for robust research and better dissemination of knowledge. In this paper, we present an overview of AI applications in thoracic surgery and its related components, including contemporary projects and technology that use AI in cardiothoracic surgery and general care. We also discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. It is vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible. This review is a great addition to literature, keeping practicing and aspiring surgeons up to date on the most recent advances in AI and cardiothoracic surgery. This literature review tells about the role of Artificial Intelligence in Cardiothoracic Surgery. Discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. Vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible.
Collapse
|
14
|
Big Data, Decision Models, and Public Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148543. [PMID: 35886394 PMCID: PMC9324609 DOI: 10.3390/ijerph19148543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022]
|
15
|
Liao KM, Ko SC, Liu CF, Cheng KC, Chen CM, Sung MI, Hsing SC, Chen CJ. Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers. Diagnostics (Basel) 2022; 12:diagnostics12040975. [PMID: 35454023 PMCID: PMC9030191 DOI: 10.3390/diagnostics12040975] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 12/04/2022] Open
Abstract
Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is very challenging in RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 intubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed.
Collapse
Affiliation(s)
- Kuang-Ming Liao
- Department of Pulmonary Medicine, Chi Mei Medical Center, Chiali, Tainan 72263, Taiwan;
| | - Shian-Chin Ko
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan 710402, Taiwan; (S.-C.K.); (M.-I.S.); (S.-C.H.)
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan
- Correspondence: (C.-F.L.); (K.-C.C.); Tel.: +886-6-2812811 (C.-F.L.); +886-6-2812811 (K.-C.C.)
| | - Kuo-Chen Cheng
- Department of Internal Medicine, Chi Mei Medical Center, Tainan 710402, Taiwan
- Correspondence: (C.-F.L.); (K.-C.C.); Tel.: +886-6-2812811 (C.-F.L.); +886-6-2812811 (K.-C.C.)
| | - Chin-Ming Chen
- Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan 710402, Taiwan;
| | - Mei-I Sung
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan 710402, Taiwan; (S.-C.K.); (M.-I.S.); (S.-C.H.)
| | - Shu-Chen Hsing
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan 710402, Taiwan; (S.-C.K.); (M.-I.S.); (S.-C.H.)
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan 710402, Taiwan;
| |
Collapse
|
16
|
Bellini V, Valente M, Del Rio P, Bignami E. Artificial intelligence in thoracic surgery: a narrative review. J Thorac Dis 2022; 13:6963-6975. [PMID: 35070380 PMCID: PMC8743413 DOI: 10.21037/jtd-21-761] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022]
Abstract
Objective The aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction. Background Artificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway. Methods We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021. Conclusion Machine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decision-making process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice. Keywords Artificial intelligence (AI); thoracic surgery; machine learning; lung resection; perioperative medicine
Collapse
Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| |
Collapse
|
17
|
Saeed U, Shah SY, Ahmad J, Imran MA, Abbasi QH, Shah SA. Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review. J Pharm Anal 2022; 12:193-204. [PMID: 35003825 PMCID: PMC8724017 DOI: 10.1016/j.jpha.2021.12.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 12/20/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions. This article describes cutting-edge technology (invasive/non-invasive) and its role in the recognition of COVID-19 symptoms. This article summarizes state-of-art machine-learning algorithms and their roles in modern healthcare systems. This article presents the challenges associated with wireless sensing techniques and potential future research directions.
Collapse
Affiliation(s)
- Umer Saeed
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, UK
| | - Syed Yaseen Shah
- School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK
| | - Jawad Ahmad
- School of Computing, Edinburgh Napier University, Edinburgh, EH11 4BN, UK
| | - Muhammad Ali Imran
- James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Qammer H Abbasi
- James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Syed Aziz Shah
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, UK
| |
Collapse
|
18
|
Park JE, Kim TY, Jung YJ, Han C, Park CM, Park JH, Park KJ, Yoon D, Chung WY. Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18179229. [PMID: 34501829 PMCID: PMC8430549 DOI: 10.3390/ijerph18179229] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 12/20/2022]
Abstract
We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data's variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70-0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.
Collapse
Affiliation(s)
- Ji Eun Park
- Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea; (J.E.P.); (Y.J.J.); (J.H.P.); (K.J.P.)
| | | | - Yun Jung Jung
- Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea; (J.E.P.); (Y.J.J.); (J.H.P.); (K.J.P.)
| | - Changho Han
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin 16995, Korea; (C.H.); (C.M.P.)
| | - Chan Min Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin 16995, Korea; (C.H.); (C.M.P.)
| | - Joo Hun Park
- Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea; (J.E.P.); (Y.J.J.); (J.H.P.); (K.J.P.)
| | - Kwang Joo Park
- Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea; (J.E.P.); (Y.J.J.); (J.H.P.); (K.J.P.)
| | - Dukyong Yoon
- BUD.on Inc., Jeonju 54871, Korea;
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin 16995, Korea; (C.H.); (C.M.P.)
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin 16995, Korea
- Correspondence: (D.Y.); (W.Y.C.); Tel.: +82-31-5189-8450 (D.Y.); +82-31-219-5120 (W.Y.C.)
| | - Wou Young Chung
- Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Korea; (J.E.P.); (Y.J.J.); (J.H.P.); (K.J.P.)
- Correspondence: (D.Y.); (W.Y.C.); Tel.: +82-31-5189-8450 (D.Y.); +82-31-219-5120 (W.Y.C.)
| |
Collapse
|
19
|
Teng IC, Chang YJ, Lin YT, Chu CC, Chen JY, Wu ZF. Sugammadex induced bradycardia and hypotension: A case report and literature review. Medicine (Baltimore) 2021; 100:e26796. [PMID: 34397735 PMCID: PMC8322479 DOI: 10.1097/md.0000000000026796] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 07/12/2021] [Indexed: 01/04/2023] Open
Abstract
RATIONALE There is evidence that sugammadex can facilitate extubation post-surgery and attenuate postoperative pulmonary complications resulting from postoperative residual neuromuscular blockade. However, it may induce adverse effects, including bronchospasm, laryngospasm, bradycardia, hypotension, and cardiac arrest. Here, we present a case of sugammadex-induced bradycardia and hypotension. PATIENT CONCERNS An 82-year-old female received video-assisted thoracic surgery decortication and wedge resection of the lung for empyema. Post-surgery, she developed bradycardia, hypotension, hypoxia, and weakness. DIAGNOSES The patient was suspected to have sugammadex-induced bradycardia, hypotension, hypoxia and weakness. INTERVENTIONS The patient received immediate treatment with atropine (0.5 mg) for bradycardia. Glycopyrrolate (0.1 mg) and neostigmine (1 mg) were administered to improve the train-of-four (TOF) ratio. OUTCOMES Following initial management, we observed improvement in the hemodynamics of the patient. She was discharged without any sequelae. LESSONS Sugammadex-induced bradycardia or cardiac arrest are rare; however, anesthesiologists must consider the possibility of the occurrence of such events and initiate appropriate management measures. Immediate treatment with atropine and inotropic or vasopressors is warranted if the patient presents with bradycardia.
Collapse
Affiliation(s)
- I-Chia Teng
- Department of Anesthesiology, Chi Mei Medical Center, Tainan City, Taiwan
| | - Ying-Jen Chang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan City, Taiwan
| | - Yao-Tsung Lin
- Department of Anesthesiology, Chi Mei Medical Center, Tainan City, Taiwan
- Department of Food Science and Technology, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Chin-Chen Chu
- Department of Anesthesiology, Chi Mei Medical Center, Tainan City, Taiwan
| | - Jen-Yin Chen
- Department of Anesthesiology, Chi Mei Medical Center, Tainan City, Taiwan
| | - Zhi-Fu Wu
- Department of Anesthesiology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Anesthesiology, Tri-Service General Hospital and National Defense Medical Center, Taipei, Taiwan
| |
Collapse
|
20
|
The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center. Healthcare (Basel) 2021; 9:healthcare9060685. [PMID: 34200189 PMCID: PMC8226916 DOI: 10.3390/healthcare9060685] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 05/31/2021] [Accepted: 06/04/2021] [Indexed: 11/23/2022] Open
Abstract
Healthcare Artificial Intelligence (AI) has the greatest opportunity for development. Since healthcare and technology are two of Taiwan’s most competitive industries, the development of healthcare AI is an excellent chance for Taiwan to improve its health-related services. From the perspective of economic development, promoting healthcare AI must be a top priority. However, despite having many breakthroughs in research and pilot projects, healthcare AI is still considered rare and is broadly used in the healthcare setting. Based on a medical center in Taiwan that has introduced a variety of healthcare AI into practice, this study discussed and analyzed the issues and concerns in the development and scaling of medical AIs from the perspective of various stakeholders in the healthcare setting, including the government, healthcare institutions, users (healthcare workers), and AI providers. The present study also identified critical influential factors for the deployment and scaling of healthcare AI. It is hoped that this paper can serve as an important reference for the advancement of healthcare AI not only in Taiwan but also in other countries.
Collapse
|