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Panagiotou OA, Högg LH, Hricak H, Khleif SN, Levy MA, Magnus D, Murphy MJ, Patel B, Winn RA, Nass SJ, Gatsonis C, Cogle CR. Clinical Application of Computational Methods in Precision Oncology: A Review. JAMA Oncol 2021; 6:1282-1286. [PMID: 32407443 DOI: 10.1001/jamaoncol.2020.1247] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Importance There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems. Objective To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. Evidence Review The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology. Findings Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics. Conclusions and Relevance Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.
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Affiliation(s)
- Orestis A Panagiotou
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island
| | - Lori Hoffman Högg
- National Center for Health Promotion and Disease Prevention, Veterans Health Administration, Durham, North Carolina.,Office of Nursing Services, Veterans Health Administration, Washington, DC
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Samir N Khleif
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Mia A Levy
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee.,Division of Hematology and Oncology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - David Magnus
- Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California
| | | | - Bakul Patel
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Robert A Winn
- University of Illinois at Chicago Cancer Center, University of Illinois Hospital and Health Sciences System, Chicago
| | - Sharyl J Nass
- Health and Medicine Division, National Academies of Sciences, Engineering, and Medicine, Washington, DC
| | - Constantine Gatsonis
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island
| | - Christopher R Cogle
- Division of Hematology & Oncology, Department of Medicine, University of Florida College of Medicine, Gainesville
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Harrison JH, Gilbertson JR, Hanna MG, Olson NH, Seheult JN, Sorace JM, Stram MN. Introduction to Artificial Intelligence and Machine Learning for Pathology. Arch Pathol Lab Med 2021; 145:1228-1254. [PMID: 33493264 DOI: 10.5858/arpa.2020-0541-cp] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/10/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data. The principles of operation and management of machine learning systems are unfamiliar to pathologists, who anticipate a need for additional education to be effective as expert users and managers of the new tools. OBJECTIVE.— To provide a background on machine learning for practicing pathologists, including an overview of algorithms, model development, and performance evaluation; to examine the current status of machine learning in pathology and consider possible roles and requirements for pathologists in local deployment and management of machine learning systems; and to highlight existing challenges and gaps in deployment methodology and regulation. DATA SOURCES.— Sources include the biomedical and engineering literature, white papers from professional organizations, government reports, electronic resources, and authors' experience in machine learning. References were chosen when possible for accessibility to practicing pathologists without specialized training in mathematics, statistics, or software development. CONCLUSIONS.— Machine learning offers an array of techniques that in recent published results show substantial promise. Data suggest that human experts working with machine learning tools outperform humans or machines separately, but the optimal form for this combination in pathology has not been established. Significant questions related to the generalizability of machine learning systems, local site verification, and performance monitoring remain to be resolved before a consensus on best practices and a regulatory environment can be established.
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Affiliation(s)
- James H Harrison
- From the Department of Pathology, University of Virginia School of Medicine, Charlottesville (Harrison)
| | - John R Gilbertson
- the Departments of Biomedical Informatics and Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania (Gilbertson)
| | - Matthew G Hanna
- the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York (Hanna)
| | - Niels H Olson
- the Defense Innovation Unit, Mountain View, California (Olson).,the Department of Pathology, Uniformed Services University, Bethesda, Maryland (Olson)
| | - Jansen N Seheult
- the Department of Pathology, University of Pittsburgh, and Vitalant Specialty Labs, Pittsburgh, Pennsylvania (Seheult)
| | - James M Sorace
- the US Department of Health and Human Services, retired, Lutherville, Maryland (Sorace)
| | - Michelle N Stram
- the Department of Forensic Medicine, New York University, and Office of Chief Medical Examiner, New York, New York (Stram)
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Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc 2021; 27:491-497. [PMID: 31682262 DOI: 10.1093/jamia/ocz192] [Citation(s) in RCA: 168] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/24/2019] [Accepted: 10/10/2019] [Indexed: 01/14/2023] Open
Abstract
As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in health care. These concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments. While there has been extensive discussion about the ethics of AI in health care, there has been little dialogue or recommendations as to how to practically address these concerns in health care. In this article, we propose a governance model that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance of AI in health care.
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Affiliation(s)
- Sandeep Reddy
- School of Medicine, Geelong, Deakin University, Australia
| | - Sonia Allan
- Deakin Law School, Melbourne, Deakin University, Australia
| | - Simon Coghlan
- School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Paul Cooper
- School of Medicine, Geelong, Deakin University, Australia
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Park SH. Artificial intelligence for ultrasonography: unique opportunities and challenges. Ultrasonography 2021; 40:3-6. [PMID: 33227844 PMCID: PMC7758099 DOI: 10.14366/usg.20078] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/31/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
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AIM in Surgical Pathology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_278-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Mijderwijk HJ, Beez T, Hänggi D, Nieboer D. Application of clinical prediction modeling in pediatric neurosurgery: a case study. Childs Nerv Syst 2021; 37:1495-1504. [PMID: 33783617 PMCID: PMC8084798 DOI: 10.1007/s00381-021-05112-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/02/2021] [Indexed: 12/23/2022]
Abstract
There has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual's risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.
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Affiliation(s)
- Hendrik-Jan Mijderwijk
- Medical Faculty, Department of Neurosurgery, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany.
| | - Thomas Beez
- Medical Faculty, Department of Neurosurgery, Heinrich Heine University, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Daniel Hänggi
- Medical Faculty, Department of Neurosurgery, Heinrich Heine University, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Daan Nieboer
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
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Choudhury A, Renjilian E, Asan O. Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review. JAMIA Open 2020; 3:459-471. [PMID: 33215079 PMCID: PMC7660963 DOI: 10.1093/jamiaopen/ooaa034] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/26/2020] [Accepted: 07/11/2020] [Indexed: 12/13/2022] Open
Abstract
Objectives Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Emily Renjilian
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
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Navathe AS, Lei VJ, Fleisher LA, Luong T, Chen X, Kennedy E, Volpp KG, Polsky DE, Groeneveld PW, Weiner M, Holmes JH, Neuman MD. Improving Identification of Patients at Low Risk for Major Cardiac Events After Noncardiac Surgery Using Intraoperative Data. J Hosp Med 2020; 15:581-587. [PMID: 32966202 PMCID: PMC7531939 DOI: 10.12788/jhm.3459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 04/13/2020] [Indexed: 11/20/2022]
Abstract
BACKGROUND/OBJECTIVE Risk-stratification tools for cardiac complications after noncardiac surgery based on preoperative risk factors are used to inform postoperative management. However, there is limited evidence on whether risk stratification can be improved by incorporating data collected intraoperatively, particularly for low-risk patients. METHODS We conducted a retrospective cohort study of adults who underwent noncardiac surgery between 2014 and 2018 at four hospitals in the United States. Logistic regression with elastic net selection was used to classify in-hospital major adverse cardiovascular events (MACE) using preoperative and intraoperative data ("perioperative model"). We compared model performance to standard risk stratification tools and professional society guidelines that do not use intraoperative data. RESULTS Of 72,909 patients, 558 (0.77%) experienced MACE. Those with MACE were older and less likely to be female. The perioperative model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI, 0.85-0.92). This was higher than the Lee Revised Cardiac Risk Index (RCRI) AUC of 0.79 (95% CI, 0.74-0.84; P < .001 for AUC comparison). There were more MACE complications in the top decile (n = 1,465) of the perioperative model's predicted risk compared with that of the RCRI model (n = 58 vs 43). Additionally, the perioperative model identified 2,341 of 7,597 (31%) patients as low risk who did not experience MACE but were recommended to receive postoperative biomarker testing by a risk factor-based guideline algorithm. CONCLUSIONS Addition of intraoperative data to preoperative data improved prediction of cardiovascular complication outcomes after noncardiac surgery and could potentially help reduce unnecessary postoperative testing.
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Affiliation(s)
- Amol S Navathe
- Corporal Michael J. Crescenz VA Medical Center, Department of Veterans Affairs, Philadelphia, Pennsylvania
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Corresponding Author: Amol S Navathe, MD, PhD; ; Telephone: 215-573-4047; Twitter: @AmolNavathe
| | - Victor J Lei
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Lee A Fleisher
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Anesthesiology and Critical Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania
| | - ThaiBinh Luong
- Predictive Healthcare, University of Pennsylvania Health System, Philadelphia, Pennsylvania
| | - Xinwei Chen
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Edward Kennedy
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Kevin G Volpp
- Corporal Michael J. Crescenz VA Medical Center, Department of Veterans Affairs, Philadelphia, Pennsylvania
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Daniel E Polsky
- Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter W Groeneveld
- Corporal Michael J. Crescenz VA Medical Center, Department of Veterans Affairs, Philadelphia, Pennsylvania
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mark Weiner
- Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania
| | - John H Holmes
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mark D Neuman
- Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Anesthesiology and Critical Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania
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Kim DW, Jang HY, Ko Y, Son JH, Kim PH, Kim SO, Lim JS, Park SH. Inconsistency in the use of the term "validation" in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging. PLoS One 2020; 15:e0238908. [PMID: 32915901 PMCID: PMC7485764 DOI: 10.1371/journal.pone.0238908] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 08/26/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND The development of deep learning (DL) algorithms is a three-step process-training, tuning, and testing. Studies are inconsistent in the use of the term "validation", with some using it to refer to tuning and others testing, which hinders accurate delivery of information and may inadvertently exaggerate the performance of DL algorithms. We investigated the extent of inconsistency in usage of the term "validation" in studies on the accuracy of DL algorithms in providing diagnosis from medical imaging. METHODS AND FINDINGS We analyzed the full texts of research papers cited in two recent systematic reviews. The papers were categorized according to whether the term "validation" was used to refer to tuning alone, both tuning and testing, or testing alone. We analyzed whether paper characteristics (i.e., journal category, field of study, year of print publication, journal impact factor [JIF], and nature of test data) were associated with the usage of the terminology using multivariable logistic regression analysis with generalized estimating equations. Of 201 papers published in 125 journals, 118 (58.7%), 9 (4.5%), and 74 (36.8%) used the term to refer to tuning alone, both tuning and testing, and testing alone, respectively. A weak association was noted between higher JIF and using the term to refer to testing (i.e., testing alone or both tuning and testing) instead of tuning alone (vs. JIF <5; JIF 5 to 10: adjusted odds ratio 2.11, P = 0.042; JIF >10: adjusted odds ratio 2.41, P = 0.089). Journal category, field of study, year of print publication, and nature of test data were not significantly associated with the terminology usage. CONCLUSIONS Existing literature has a significant degree of inconsistency in using the term "validation" when referring to the steps in DL algorithm development. Efforts are needed to improve the accuracy and clarity in the terminology usage.
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Affiliation(s)
- Dong Wook Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hye Young Jang
- Department of Radiology, National Cancer Center, Goyang, Republic of Korea
| | - Yousun Ko
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Jung Hee Son
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Pyeong Hwa Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seon-Ok Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Joon Seo Lim
- Scientific Publications Team, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Jiang H, Song Q, Gao K, Song Q, Zhao X. Rule-based expert system to assess caving output ratio in top coal caving. PLoS One 2020; 15:e0238138. [PMID: 32886664 PMCID: PMC7473562 DOI: 10.1371/journal.pone.0238138] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 08/10/2020] [Indexed: 11/19/2022] Open
Abstract
Coal mining professionals in coal mining have recognized that the assessment of top coal release rate can not only improve the recovery rate of top coal, but also improve the quality of coal. But the process was often performed using a manual-based operation mode, which intensifies workload and difficulty, and is at risk of human errors. The study designs a assessment system to give the caving output ratio in top coal caving as accurately as possible based on the parameters adaptive Takagi-Sugeno (T-S) fuzzy system and the Levenberg-Marquardt (LM) algorithm. The main goal of the adaptive parameters based on LM algorithm is to construct its damping factor in the light of lowering of the objective function which is as taken as the index of termination iteration. The performance of the system is evaluated by Pearson correlation coefficient, Coefficient of Determination and relative error where the results of the Takagi-Sugeno method and the parameters adaptive Takagi-Sugeno method are compared to make the evaluation more robust and comprehensive.
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Affiliation(s)
- HaiYan Jiang
- Tai-an School, Shandong University of Science & Technology, Tai-an, Shandong, China
| | - Qinghui Song
- Department of Mechanical and Electronic Engineering, Shandong University of Science & Technology, Qingdao, Shandong, China
| | - Kuidong Gao
- Shandong Province Key Laboratory of Mine Mechanical Engineering, Shandong University of Science & Technology, Qingdao, Shandong, China
| | - QingJun Song
- Tai-an School, Shandong University of Science & Technology, Tai-an, Shandong, China
| | - XieGuang Zhao
- Tai-an School, Shandong University of Science & Technology, Tai-an, Shandong, China
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Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform 2020; 8:e18599. [PMID: 32706688 PMCID: PMC7414411 DOI: 10.2196/18599] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/26/2020] [Accepted: 06/13/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
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Esmaeilzadeh P. Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives. BMC Med Inform Decis Mak 2020; 20:170. [PMID: 32698869 PMCID: PMC7376886 DOI: 10.1186/s12911-020-01191-1] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/16/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Several studies highlight the effects of artificial intelligence (AI) systems on healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning. It is believed that AI will be an integral part of healthcare services in the near future and will be incorporated into several aspects of clinical care. Thus, many technology companies and governmental projects have invested in producing AI-based clinical tools and medical applications. Patients can be one of the most important beneficiaries and users of AI-based applications whose perceptions may affect the widespread use of AI-based tools. Patients should be ensured that they will not be harmed by AI-based devices, and instead, they will be benefited by using AI technology for healthcare purposes. Although AI can enhance healthcare outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care. METHODS We develop a model mainly based on value perceptions due to the specificity of the healthcare field. This study aims at examining the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers' perspectives. We use an online survey to collect data from 307 individuals in the United States. RESULTS The proposed model identifies the sources of motivation and pressure for patients in the development of AI-based devices. The results show that technological, ethical (trust factors), and regulatory concerns significantly contribute to the perceived risks of using AI applications in healthcare. Of the three categories, technological concerns (i.e., performance and communication feature) are found to be the most significant predictors of risk beliefs. CONCLUSIONS This study sheds more light on factors affecting perceived risks and proposes some recommendations on how to practically reduce these concerns. The findings of this study provide implications for research and practice in the area of AI-based CDS. Regulatory agencies, in cooperation with healthcare institutions, should establish normative standard and evaluation guidelines for the implementation and use of AI in healthcare. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI-based services.
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Affiliation(s)
- Pouyan Esmaeilzadeh
- Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, 33199, USA.
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Abstract
Causal reasoning can shed new light on the major challenges in machine learning for medical imaging: scarcity of high-quality annotated data and mismatch between the development dataset and the target environment. A causal perspective on these issues allows decisions about data collection, annotation, preprocessing, and learning strategies to be made and scrutinized more transparently, while providing a detailed categorisation of potential biases and mitigation techniques. Along with worked clinical examples, we highlight the importance of establishing the causal relationship between images and their annotations, and offer step-by-step recommendations for future studies.
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Affiliation(s)
- Daniel C Castro
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
| | - Ian Walker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
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Jayakumar P, Bozic KJ. Advanced decision-making using patient-reported outcome measures in total joint replacement. J Orthop Res 2020; 38:1414-1422. [PMID: 31994752 DOI: 10.1002/jor.24614] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 01/21/2020] [Indexed: 02/04/2023]
Abstract
Up to one-third of total joint replacement (TJR) procedures may be performed inappropriately in a subset of patients who remain dissatisfied with their outcomes, stressing the importance of shared decision-making. Patient-reported outcome measures capture physical, emotional, and social aspects of health and wellbeing from the patient's perspective. Powerful computer systems capable of performing highly sophisticated analysis using different types of data, including patient-derived data, such as patient-reported outcomes, may eliminate guess work, generating impactful metrics to better inform the decision-making process. We have created a shared decision-making tool which generates personalized predictions of risks and benefits from TJR based on patient-reported outcomes as well as clinical and demographic data. We present the protocol for a randomized controlled trial designed to assess the impact of this tool on decision quality, level of shared decision-making, and patient and process outcomes. We also discuss current concepts in this field and highlight opportunities leveraging patient-reported data and artificial intelligence for decision support across the care continuum.
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Affiliation(s)
- Prakash Jayakumar
- Department of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, Texas
| | - Kevin J Bozic
- Department of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, Texas
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Asan O, Bayrak AE, Choudhury A. Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. J Med Internet Res 2020; 22:e15154. [PMID: 32558657 PMCID: PMC7334754 DOI: 10.2196/15154] [Citation(s) in RCA: 188] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 05/12/2020] [Accepted: 06/03/2020] [Indexed: 12/18/2022] Open
Abstract
Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable-though imperfect-clinical decisions or suggestions. In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians' use and adoption of AI. Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown. Several research studies have highlighted the need for improving AI-based systems and enhancing their capabilities to help clinicians. However, assessing the magnitude and impact of human trust on AI technology demands substantial attention. Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI. We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use.
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Affiliation(s)
- Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Alparslan Emrah Bayrak
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
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Panch T, Pollard TJ, Mattie H, Lindemer E, Keane PA, Celi LA. "Yes, but will it work for my patients?" Driving clinically relevant research with benchmark datasets. NPJ Digit Med 2020; 3:87. [PMID: 32577534 PMCID: PMC7305156 DOI: 10.1038/s41746-020-0295-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 05/26/2020] [Indexed: 01/15/2023] Open
Abstract
Benchmark datasets have a powerful normative influence: by determining how the real world is represented in data, they define which problems will first be solved by algorithms built using the datasets and, by extension, who these algorithms will work for. It is desirable for these datasets to serve four functions: (1) enabling the creation of clinically relevant algorithms; (2) facilitating like-for-like comparison of algorithmic performance; (3) ensuring reproducibility of algorithms; (4) asserting a normative influence on the clinical domains and diversity of patients that will potentially benefit from technological advances. Without benchmark datasets that satisfy these functions, it is impossible to address two perennial concerns of clinicians experienced in computational research: "the data scientists just go where the data is rather than where the needs are," and, "yes, but will this work for my patients?" If algorithms are to be developed and applied for the care of patients, then it is prudent for the research community to create benchmark datasets proactively, across specialties. As yet, best practice in this area has not been defined. Broadly speaking, efforts will include design of the dataset; compliance and contracting issues relating to the sharing of sensitive data; enabling access and reuse; and planning for translation of algorithms to the clinical environment. If a deliberate and systematic approach is not followed, not only will the considerable benefits of clinical algorithms fail to be realized, but the potential harms may be regressively incurred across existing gradients of social inequity.
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Affiliation(s)
- Trishan Panch
- Division of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Wellframe Inc., Boston, MA USA
| | - Tom J. Pollard
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Heather Mattie
- Wellframe Inc., Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | | | - Pearse A. Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA USA
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Jamshidi MB, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P, Jamshidi M, Spada LL, Mirmozafari M, Dehghani M, Sabet A, Roshani S, Roshani S, Bayat-Makou N, Mohamadzade B, Malek Z, Jamshidi A, Kiani S, Hashemi-Dezaki H, Mohyuddin W. Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:109581-109595. [PMID: 34192103 PMCID: PMC8043506 DOI: 10.1109/access.2020.3001973] [Citation(s) in RCA: 180] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/02/2020] [Indexed: 05/15/2023]
Abstract
COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.
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Affiliation(s)
- Mohammad Behdad Jamshidi
- Department of Electromechanical Engineering and Power Electronics (KEV)University of West Bohemia in Pilsen301 00PilsenCzech Republic
| | - Ali Lalbakhsh
- School of EngineeringMacquarie UniversitySydneyNSW2109Australia
| | - Jakub Talla
- Department of Electromechanical Engineering and Power Electronics (KEV)University of West Bohemia in Pilsen301 00PilsenCzech Republic
| | - Zdeněk Peroutka
- Regional Innovation Centre for Electrical engineering (RICE)University of West Bohemia in Pilsen301 00PilsenCzech Republic
| | - Farimah Hadjilooei
- Department of Radiation OncologyCancer Institute, Tehran University of Medical SciencesTehran1416753955Iran
| | - Pedram Lalbakhsh
- Department of English Language and LiteratureRazi UniversityKermanshah6714414971Iran
| | - Morteza Jamshidi
- Young Researchers and Elite Club, Kermanshah BranchIslamic Azad UniversityKermanshah1477893855Iran
| | - Luigi La Spada
- School of Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH11 4DYU.K.
| | - Mirhamed Mirmozafari
- Department of Electrical and Computer EngineeringUniversity of Wisconsin–MadisonMadisonWI53706USA
| | - Mojgan Dehghani
- Physics and Astronomy DepartmentLouisiana State UniversityBaton RougeLA70803USA
| | - Asal Sabet
- Irma Lerma Rangel College of PharmacyTexas A&M UniversityKingsvilleTX78363USA
| | - Saeed Roshani
- Department of Electrical EngineeringKermanshah Branch, Islamic Azad UniversityKermanshah1477893855Iran
| | - Sobhan Roshani
- Department of Electrical EngineeringKermanshah Branch, Islamic Azad UniversityKermanshah1477893855Iran
| | - Nima Bayat-Makou
- The Edward S. Rogers, Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoON M5SCanada
| | | | - Zahra Malek
- Medical Sciences Research Center, Faculty of Medicine, Tehran Medical Sciences BranchIslamic Azad UniversityTehran1477893855Iran
| | - Alireza Jamshidi
- Dentistry SchoolBabol University of Medical SciencesBabol4717647745Iran
| | - Sarah Kiani
- Medical Biology Research CenterHealth Technology Institute, Kermanshah University of Medical SciencesKermanshah6715847141Iran
| | - Hamed Hashemi-Dezaki
- Regional Innovation Centre for Electrical engineering (RICE)University of West Bohemia in Pilsen301 00PilsenCzech Republic
| | - Wahab Mohyuddin
- Research Institute for Microwave and Millimeter-Wave Studies, National University of Sciences and TechnologyIslamabad24090Pakistan
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Reyes M, Meier R, Pereira S, Silva CA, Dahlweid FM, von Tengg-Kobligk H, Summers RM, Wiest R. On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. Radiol Artif Intell 2020; 2:e190043. [PMID: 32510054 DOI: 10.1148/ryai.2020190043] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 01/08/2020] [Accepted: 02/10/2020] [Indexed: 01/01/2023]
Abstract
As artificial intelligence (AI) systems begin to make their way into clinical radiology practice, it is crucial to assure that they function correctly and that they gain the trust of experts. Toward this goal, approaches to make AI "interpretable" have gained attention to enhance the understanding of a machine learning algorithm, despite its complexity. This article aims to provide insights into the current state of the art of interpretability methods for radiology AI. This review discusses radiologists' opinions on the topic and suggests trends and challenges that need to be addressed to effectively streamline interpretability methods in clinical practice. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Gastounioti and Kontos in this issue.
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Affiliation(s)
- Mauricio Reyes
- Artorg Center for Biomedical Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland (M.R.); Insel Data Science Center, University of Bern, Bern, Switerland (F.M.D.); Institute of Diagnostic and Interventional Neuroradiology (R.M., R.W.) and Department of Diagnostic, Interventional and Paediatric Radiology (H.v.T.K.), Inselspital University Hospital Bern, Bern, Switzerland; Center for Microelectromechanical Systems-University of Minho Research Unit, University of Minho, Guimarães, Portugal (S.P., C.A.S.); and Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Raphael Meier
- Artorg Center for Biomedical Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland (M.R.); Insel Data Science Center, University of Bern, Bern, Switerland (F.M.D.); Institute of Diagnostic and Interventional Neuroradiology (R.M., R.W.) and Department of Diagnostic, Interventional and Paediatric Radiology (H.v.T.K.), Inselspital University Hospital Bern, Bern, Switzerland; Center for Microelectromechanical Systems-University of Minho Research Unit, University of Minho, Guimarães, Portugal (S.P., C.A.S.); and Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Sérgio Pereira
- Artorg Center for Biomedical Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland (M.R.); Insel Data Science Center, University of Bern, Bern, Switerland (F.M.D.); Institute of Diagnostic and Interventional Neuroradiology (R.M., R.W.) and Department of Diagnostic, Interventional and Paediatric Radiology (H.v.T.K.), Inselspital University Hospital Bern, Bern, Switzerland; Center for Microelectromechanical Systems-University of Minho Research Unit, University of Minho, Guimarães, Portugal (S.P., C.A.S.); and Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Carlos A Silva
- Artorg Center for Biomedical Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland (M.R.); Insel Data Science Center, University of Bern, Bern, Switerland (F.M.D.); Institute of Diagnostic and Interventional Neuroradiology (R.M., R.W.) and Department of Diagnostic, Interventional and Paediatric Radiology (H.v.T.K.), Inselspital University Hospital Bern, Bern, Switzerland; Center for Microelectromechanical Systems-University of Minho Research Unit, University of Minho, Guimarães, Portugal (S.P., C.A.S.); and Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Fried-Michael Dahlweid
- Artorg Center for Biomedical Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland (M.R.); Insel Data Science Center, University of Bern, Bern, Switerland (F.M.D.); Institute of Diagnostic and Interventional Neuroradiology (R.M., R.W.) and Department of Diagnostic, Interventional and Paediatric Radiology (H.v.T.K.), Inselspital University Hospital Bern, Bern, Switzerland; Center for Microelectromechanical Systems-University of Minho Research Unit, University of Minho, Guimarães, Portugal (S.P., C.A.S.); and Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Hendrik von Tengg-Kobligk
- Artorg Center for Biomedical Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland (M.R.); Insel Data Science Center, University of Bern, Bern, Switerland (F.M.D.); Institute of Diagnostic and Interventional Neuroradiology (R.M., R.W.) and Department of Diagnostic, Interventional and Paediatric Radiology (H.v.T.K.), Inselspital University Hospital Bern, Bern, Switzerland; Center for Microelectromechanical Systems-University of Minho Research Unit, University of Minho, Guimarães, Portugal (S.P., C.A.S.); and Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Ronald M Summers
- Artorg Center for Biomedical Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland (M.R.); Insel Data Science Center, University of Bern, Bern, Switerland (F.M.D.); Institute of Diagnostic and Interventional Neuroradiology (R.M., R.W.) and Department of Diagnostic, Interventional and Paediatric Radiology (H.v.T.K.), Inselspital University Hospital Bern, Bern, Switzerland; Center for Microelectromechanical Systems-University of Minho Research Unit, University of Minho, Guimarães, Portugal (S.P., C.A.S.); and Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Roland Wiest
- Artorg Center for Biomedical Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland (M.R.); Insel Data Science Center, University of Bern, Bern, Switerland (F.M.D.); Institute of Diagnostic and Interventional Neuroradiology (R.M., R.W.) and Department of Diagnostic, Interventional and Paediatric Radiology (H.v.T.K.), Inselspital University Hospital Bern, Bern, Switzerland; Center for Microelectromechanical Systems-University of Minho Research Unit, University of Minho, Guimarães, Portugal (S.P., C.A.S.); and Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
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Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol 2020; 30:5525-5532. [PMID: 32458173 PMCID: PMC7476917 DOI: 10.1007/s00330-020-06946-y] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 04/03/2020] [Accepted: 05/08/2020] [Indexed: 12/22/2022]
Abstract
Objective The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and methods Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. Results Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. Conclusion In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications. Key Points • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation. Electronic supplementary material The online version of this article (10.1007/s00330-020-06946-y) contains supplementary material, which is available to authorized users.
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Schulz WL, Durant TJS, Krumholz HM. Validation and Regulation of Clinical Artificial Intelligence. Clin Chem 2020; 65:1336-1337. [PMID: 32100825 DOI: 10.1373/clinchem.2019.308304] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 06/14/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Wade L Schulz
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT.,Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT
| | - Thomas J S Durant
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.,Department of Health Policy and Management, Yale School of Public Health, New Haven, CT
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Fu LH, Schwartz J, Moy A, Knaplund C, Kang MJ, Schnock KO, Garcia JP, Jia H, Dykes PC, Cato K, Albers D, Rossetti SC. Development and validation of early warning score system: A systematic literature review. J Biomed Inform 2020; 105:103410. [PMID: 32278089 PMCID: PMC7295317 DOI: 10.1016/j.jbi.2020.103410] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 03/19/2020] [Accepted: 03/21/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVES This review aims to: 1) evaluate the quality of model reporting, 2) provide an overview of methodology for developing and validating Early Warning Score Systems (EWSs) for adult patients in acute care settings, and 3) highlight the strengths and limitations of the methodologies, as well as identify future directions for EWS derivation and validation studies. METHODOLOGY A systematic search was conducted in PubMed, Cochrane Library, and CINAHL. Only peer reviewed articles and clinical guidelines regarding developing and validating EWSs for adult patients in acute care settings were included. 615 articles were extracted and reviewed by five of the authors. Selected studies were evaluated based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. The studies were analyzed according to their study design, predictor selection, outcome measurement, methodology of modeling, and validation strategy. RESULTS A total of 29 articles were included in the final analysis. Twenty-six articles reported on the development and validation of a new EWS, while three reported on validation and model modification. Only eight studies met more than 75% of the items in the TRIPOD checklist. Three major techniques were utilized among the studies to inform their predictive algorithms: 1) clinical-consensus models (n = 6), 2) regression models (n = 15), and 3) tree models (n = 5). The number of predictors included in the EWSs varied from 3 to 72 with a median of seven. Twenty-eight models included vital signs, while 11 included lab data. Pulse oximetry, mental status, and other variables extracted from electronic health records (EHRs) were among other frequently used predictors. In-hospital mortality, unplanned transfer to the intensive care unit (ICU), and cardiac arrest were commonly used clinical outcomes. Twenty-eight studies conducted a form of model validation either within the study or against other widely-used EWSs. Only three studies validated their model using an external database separate from the derived database. CONCLUSION This literature review demonstrates that the characteristics of the cohort, predictors, and outcome selection, as well as the metrics for model validation, vary greatly across EWS studies. There is no consensus on the optimal strategy for developing such algorithms since data-driven models with acceptable predictive accuracy are often site-specific. A standardized checklist for clinical prediction model reporting exists, but few studies have included reporting aligned with it in their publications. Data-driven models are subjected to biases in the use of EHR data, thus it is particularly important to provide detailed study protocols and acknowledge, leverage, or reduce potential biases of the data used for EWS development to improve transparency and generalizability.
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Affiliation(s)
- Li-Heng Fu
- Department of Biomedical Informatics, Columbia University, New York, NY, United States.
| | - Jessica Schwartz
- School of Nursing, Columbia University, New York, NY, United States
| | - Amanda Moy
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Chris Knaplund
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Min-Jeoung Kang
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Kumiko O Schnock
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Jose P Garcia
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, United States
| | - Haomiao Jia
- School of Nursing, Columbia University, New York, NY, United States; Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Patricia C Dykes
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Kenrick Cato
- School of Nursing, Columbia University, New York, NY, United States
| | - David Albers
- Department of Biomedical Informatics, Columbia University, New York, NY, United States; Department of Pediatrics, Section of Informatics and Data Science, University of Colorado, Aurora, CO, United States
| | - Sarah Collins Rossetti
- Department of Biomedical Informatics, Columbia University, New York, NY, United States; School of Nursing, Columbia University, New York, NY, United States
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Benton DC, Scheidt L, Guerrero A. Regulating Disruptive Technologies: Oxymoron or Essential Evolution? JOURNAL OF NURSING REGULATION 2020. [DOI: 10.1016/s2155-8256(20)30057-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Ho CWL, Ali J, Caals K. Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance. Bull World Health Organ 2020; 98:263-269. [PMID: 32284650 PMCID: PMC7133481 DOI: 10.2471/blt.19.234732] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 01/13/2020] [Accepted: 01/21/2020] [Indexed: 12/22/2022] Open
Abstract
Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.
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Affiliation(s)
- Calvin W L Ho
- Faculty of Law, Cheng Yu Tung Tower, Centennial Campus, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Joseph Ali
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States of America
| | - Karel Caals
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Weisz HA, Kennedy D, Widen S, Spratt H, Sell SL, Bailey C, Sheffield-Moore M, DeWitt DS, Prough DS, Levin H, Robertson C, Hellmich HL. MicroRNA sequencing of rat hippocampus and human biofluids identifies acute, chronic, focal and diffuse traumatic brain injuries. Sci Rep 2020; 10:3341. [PMID: 32094409 PMCID: PMC7040013 DOI: 10.1038/s41598-020-60133-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 01/29/2020] [Indexed: 01/17/2023] Open
Abstract
High-throughput sequencing technologies could improve diagnosis and classification of TBI subgroups. Because recent studies showed that circulating microRNAs (miRNAs) may serve as noninvasive markers of TBI, we performed miRNA-seq to study TBI-induced changes in rat hippocampal miRNAs up to one year post-injury. We used miRNA PCR arrays to interrogate differences in serum miRNAs using two rat models of TBI (controlled cortical impact [CCI] and fluid percussion injury [FPI]). The translational potential of our results was evaluated by miRNA-seq analysis of human control and TBI (acute and chronic) serum samples. Bioinformatic analyses were performed using Ingenuity Pathway Analysis, miRDB, and Qlucore Omics Explorer. Rat miRNA profiles identified TBI across all acute and chronic intervals. Rat CCI and FPI displayed distinct serum miRNA profiles. Human miRNA profiles identified TBI across all acute and chronic time points and, at 24 hours, discriminated between focal and diffuse injuries. In both species, predicted gene targets of differentially expressed miRNAs are involved in neuroplasticity, immune function and neurorestoration. Chronically dysregulated miRNAs (miR-451a, miR-30d-5p, miR-145-5p, miR-204-5p) are linked to psychiatric and neurodegenerative disorders. These data suggest that circulating miRNAs in biofluids can be used as "molecular fingerprints" to identify acute, chronic, focal or diffuse TBI and potentially, presence of neurodegenerative sequelae.
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Affiliation(s)
- Harris A Weisz
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Deborah Kennedy
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Steven Widen
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Heidi Spratt
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Stacy L Sell
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Christine Bailey
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | | | - Douglas S DeWitt
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Donald S Prough
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | | | | | - Helen L Hellmich
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA.
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Özdemir V, Arga KY, Aziz RK, Bayram M, Conley SN, Dandara C, Endrenyi L, Fisher E, Garvey CK, Hekim N, Kunej T, Şardaş S, Von Schomberg R, Yassin AS, Yılmaz G, Wang W. Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2020; 24:62-80. [PMID: 32027574 DOI: 10.1089/omi.2019.0220] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Precision/personalized medicine is a hot topic in health care. Often presented with the motto "the right drug, for the right patient, at the right dose, and the right time," precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the "sugar code" (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the "sugar lag" in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.
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Affiliation(s)
- Vural Özdemir
- OMICS: A Journal of Integrative Biology, New Rochelle, New York.,Senior Advisor and Writer, Emerging Technology Governance and Responsible Innovation, Toronto, Ontario, Canada
| | - K Yalçın Arga
- Health Institutes of Turkey, Istanbul, Turkey.,Department of Bioengineering, Faculty of Engineering, Marmara University, İstanbul, Turkey
| | - Ramy K Aziz
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, Cairo, Egypt.,The Center for Genome and Microbiome Research, Cairo University, Cairo, Egypt
| | - Mustafa Bayram
- Department of Food Engineering, Faculty of Engineering, Gaziantep University, Gaziantep, Turkey
| | - Shannon N Conley
- STS Futures Lab, School of Integrated Sciences, James Madison University, Harrisonburg, Virginia
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology and Institute for Infectious Disease and Molecular Medicine (IDM), Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Laszlo Endrenyi
- Department of Pharmacology and Toxicology, Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Erik Fisher
- School for the Future of Innovation in Society and the Consortium for Science, Policy and Outcomes, Arizona State University, Tempe, Arizona
| | - Colin K Garvey
- Stanford Institute for Human-Centered Artificial Intelligence, Stanford University, Palo Alto, California
| | - Nezih Hekim
- Department of Biochemistry, Faculty of Medicine, İstanbul Medipol University, İstanbul, Turkey
| | - Tanja Kunej
- University of Ljubljana, Biotechnical Faculty, Department of Animal Science, Domzale, Slovenia
| | - Semra Şardaş
- Faculty of Pharmacy, İstinye University, İstanbul, Turkey
| | - Rene Von Schomberg
- Directorate General for Research and Innovation, European Commission, Brussel, Belgium.,Technical University Darmstadt, Darmstadt, Germany
| | - Aymen S Yassin
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, Cairo, Egypt.,The Center for Genome and Microbiome Research, Cairo University, Cairo, Egypt
| | - Gürçim Yılmaz
- Writer and Editor, Cultural Studies, and Curator of Contemporary Arts, İstanbul, Turkey
| | - Wei Wang
- Key Municipal Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
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76
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Lee H, Park SH, Kim C, Kim S, Cha J. Survey of the Knowledge of Korean Radiology Residents on Medical Artificial Intelligence. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2020; 81:1397-1411. [PMID: 36237714 PMCID: PMC9431847 DOI: 10.3348/jksr.2019.0179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/05/2020] [Accepted: 02/23/2020] [Indexed: 11/23/2022]
Abstract
목적 이 연구는 인공지능(artificial intelligence; 이하 AI)에 대한 영상의학과 전공의들의 인식 및 의견을 알아보고자 하였다 대상과 방법 2019년 6월 4일부터 7일까지 AI와 관련한 18개의 객관식 문항과 1개의 주관식 문항이 포함된 설문의 응답을 받았다. 모집된 결과를 로지스틱 회귀분석을 이용하여 전공의 연차, 소속 병원의 위치 및 규모 등의 요인에 따라 분석하였다 결과 총 101명(89.4%)의 전공의가 응답하였다. AI의 지식적 측면에서 응답자의 50명(49.5%)이 AI에 대해 평균 이상으로 공부하고 있으며, 68명(67.3%)이 AI 관련 용어에 대한 이해도가 평균 이상이라고 응답하였다. 또한 서울 및 경기 지역 응답자가 기타 지역 응답자에 비하여 AI에 대한 자가 평가 및 지식수준이 의미 있게 높았으며, 4년차 전공의에 비해 1~2년차 전공의가 AI에 대한 자가 평가 및 지식수준이 의미 있게 낮았다. AI 관련 연구에 참여해본 적 있는 전공의는 15.8%이었지만, 추후 연구 참여 의향이 있는 전공의는 90%에 달하였다. 전공의들은 또한 학회 주도의 AI 교육 및 적극적 홍보를 원하고 있었다. 결론 영상의학과 전공의의 AI 교육 수요를 충족시키고, 의료 AI 시대의 영상의학과 의사의 역할을 제대로 알리기 위해 보다 많은 학회 차원의 노력이 요청된다.
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Affiliation(s)
- Hyeonbin Lee
- Department of Radiology, Korea University Ansan Hospital, Ansan, Korea
| | - Seong Ho Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Cherry Kim
- Department of Radiology, Korea University Ansan Hospital, Ansan, Korea
| | - Seungkwan Kim
- Department of Radiology, Korea University Ansan Hospital, Ansan, Korea
| | - Jaehyung Cha
- Medical Science Research Center, Korea University College of Medicine, Ansan, Korea
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Lovis C. Unlocking the Power of Artificial Intelligence and Big Data in Medicine. J Med Internet Res 2019; 21:e16607. [PMID: 31702565 PMCID: PMC6874800 DOI: 10.2196/16607] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 12/17/2022] Open
Abstract
Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.
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Affiliation(s)
- Christian Lovis
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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Abstract
INTRODUCTION Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health. OBJECTIVE The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future. METHODS Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. RESULTS The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed. CONCLUSION Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.
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Affiliation(s)
- Fei Wang
- Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, NY, USA
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Jayakumar P, Moore MLG, Bozic KJ. Value-based Healthcare: Can Artificial Intelligence Provide Value in Orthopaedic Surgery? Clin Orthop Relat Res 2019; 477:1777-1780. [PMID: 31335596 PMCID: PMC7000015 DOI: 10.1097/corr.0000000000000873] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 06/10/2019] [Indexed: 01/31/2023]
Affiliation(s)
- Prakash Jayakumar
- P. Jayakumar, UK Harkness Fellow in Health Care Policy and Practice Innovation, Value Institute for Health and Care/Department of Surgery and Perioperative Care, Dell Medical School at The University of Texas at Austin, Austin, TX, USA. M. L. G. Moore, Value Based Care Fellow, Department of Surgery and Perioperative Care, Dell Medical School at The University of Texas at Austin, Austin, TX, USA K. J. Bozic, Chair, Department of Surgery and Perioperative Care, Dell Medical School at The University of Texas at Austin, Austin, TX, USA
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80
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Bellemo V, Lim G, Rim TH, Tan GSW, Cheung CY, Sadda S, He MG, Tufail A, Lee ML, Hsu W, Ting DSW. Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application. Curr Diab Rep 2019; 19:72. [PMID: 31367962 DOI: 10.1007/s11892-019-1189-3] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
PURPOSE OF REVIEW This paper systematically reviews the recent progress in diabetic retinopathy screening. It provides an integrated overview of the current state of knowledge of emerging techniques using artificial intelligence integration in national screening programs around the world. Existing methodological approaches and research insights are evaluated. An understanding of existing gaps and future directions is created. RECENT FINDINGS Over the past decades, artificial intelligence has emerged into the scientific consciousness with breakthroughs that are sparking increasing interest among computer science and medical communities. Specifically, machine learning and deep learning (a subtype of machine learning) applications of artificial intelligence are spreading into areas that previously were thought to be only the purview of humans, and a number of applications in ophthalmology field have been explored. Multiple studies all around the world have demonstrated that such systems can behave on par with clinical experts with robust diagnostic performance in diabetic retinopathy diagnosis. However, only few tools have been evaluated in clinical prospective studies. Given the rapid and impressive progress of artificial intelligence technologies, the implementation of deep learning systems into routinely practiced diabetic retinopathy screening could represent a cost-effective alternative to help reduce the incidence of preventable blindness around the world.
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Affiliation(s)
- Valentina Bellemo
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Gilbert Lim
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Tyler Hyungtaek Rim
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Gavin S W Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - SriniVas Sadda
- Doheny Eye Institute, University of California, Los Angeles, CA, USA
| | - Ming-Guang He
- Center of Eye Research Australia, Melbourne, Victoria, Australia
| | - Adnan Tufail
- Moorfields Eye Hospital & Institute of Ophthalmology, UCL, London, UK
| | - Mong Li Lee
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Wynne Hsu
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
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Wiegand T, Krishnamurthy R, Kuglitsch M, Lee N, Pujari S, Salathé M, Wenzel M, Xu S. WHO and ITU establish benchmarking process for artificial intelligence in health. Lancet 2019; 394:9-11. [PMID: 30935732 DOI: 10.1016/s0140-6736(19)30762-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 03/20/2019] [Indexed: 12/31/2022]
Affiliation(s)
- Thomas Wiegand
- Fraunhofer Heinrich Hertz Institute and Technische Universität Berlin, Berlin 10587, Germany.
| | | | - Monique Kuglitsch
- Fraunhofer Heinrich Hertz Institute and Technische Universität Berlin, Berlin 10587, Germany
| | | | | | - Marcel Salathé
- École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Markus Wenzel
- Fraunhofer Heinrich Hertz Institute and Technische Universität Berlin, Berlin 10587, Germany
| | - Shan Xu
- China Academy of Information and Communications Technology, Beijing, China
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Garvey C. Hypothesis: Is “Terminator Syndrome” a Barrier to Democratizing Artificial Intelligence and Public Engagement in Digital Health? ACTA ACUST UNITED AC 2019; 23:362-363. [DOI: 10.1089/omi.2019.0070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Colin Garvey
- Department of Science and Technology Studies, Rensselaer Polytechnic Institute, Troy, New York
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Cabitza F, Zeitoun JD. The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:161. [PMID: 31168442 PMCID: PMC6526255 DOI: 10.21037/atm.2019.04.07] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 02/20/2019] [Indexed: 02/05/2023]
Affiliation(s)
- Federico Cabitza
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
- University of Milano-Bicocca, Milano, Italy
| | - Jean-David Zeitoun
- Centre d’Epidémiologie Clinique, Hôtel Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- Gastroenterology and Nutrition, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
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Parikh RB, Gdowski A, Patt DA, Hertler A, Mermel C, Bekelman JE. Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology. Am Soc Clin Oncol Educ Book 2019; 39:e53-e58. [PMID: 31099672 DOI: 10.1200/edbk_238891] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology. We then highlight promising future use cases of predictive analytics in clinical decision support and genomic risk stratification. We conclude by describing challenges in the future applications of big data in oncology, namely (1) difficulties in acquisition of comprehensive data and endpoints, (2) the lack of prospective validation of predictive tools, and (3) the risk of automating bias in observational datasets. If such challenges can be overcome, computational techniques for clinical risk stratification will in short order improve clinical risk stratification for patients with cancer.
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Affiliation(s)
- Ravi B Parikh
- 1 Penn Center for Cancer Care Innovation at the Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
- 2 Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA
| | - Andrew Gdowski
- 3 Dell Medical School at The University of Texas at Austin, Austin, TX
| | - Debra A Patt
- 3 Dell Medical School at The University of Texas at Austin, Austin, TX
- 4 Texas Oncology, Dallas, TX
| | | | | | - Justin E Bekelman
- 1 Penn Center for Cancer Care Innovation at the Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
- 2 Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA
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Park SH, Do KH, Kim S, Park JH, Lim YS. What should medical students know about artificial intelligence in medicine? JOURNAL OF EDUCATIONAL EVALUATION FOR HEALTH PROFESSIONS 2019; 16:18. [PMID: 31319450 PMCID: PMC6639123 DOI: 10.3352/jeehp.2019.16.18] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Accepted: 07/03/2019] [Indexed: 05/09/2023]
Abstract
Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.
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Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- Corresponding
| | - Kyung-Hyun Do
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sungwon Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Joo Hyun Park
- Department of Medical Education, University of Ulsan College of Medicine, Seoul, Korea
| | - Young-Suk Lim
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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