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Varghese AP, Naik S, Asrar Ul Haq Andrabi S, Luharia A, Tivaskar S, John J, Mishra GV, Uke A, Pisulkar SG, Wanjari M. Emerging Applications of Picture Archiving and Communication Systems and Their Impact on Research and Education: A Literature Review. Cureus 2024; 16:e65019. [PMID: 39165454 PMCID: PMC11335171 DOI: 10.7759/cureus.65019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 07/21/2024] [Indexed: 08/22/2024] Open
Abstract
In recent times, technological advancements have remarkably improved picture archiving and communication system (PACS) capabilities beyond their conventional use in radiology departments. Researchers and instructors have started employing PACS functionalities to improve medical research processes, promote interdisciplinary collaborations, and facilitate learning. To illustrate this point further, the PACS enables researchers to handle and analyze huge amounts of imaging data with superior precision and speed, supporting innovative studies in areas like disease progression, treatment outcomes, and imaging modalities. Moreover, a PACS integrated with artificial intelligence (AI) algorithms leads to significant improvements in image processing, diagnostic accuracy, and personalized treatment, thus marking a new approach to medical imaging. The PACS supported by AI is mostly transformative since they allow for improved early disease detection capabilities as well as automated image processing and decision assistance, which increase diagnostic accuracy and clinical outcomes. Such systems can rapidly process large quantities of visual data with an accuracy rate surpassing earlier endeavors. In medical research, however, combining PACS with AI allows challenging imaging datasets to be examined, thereby making findings that were not previously possible. The capacity to combine imaging outcomes with clinical information is valuable for medical students and professionals in the field of education. They can access extensive medical image collections and case studies using PACS. This link is critical for teaching and learning as it allows students to interact with concrete events and improve their diagnostic accuracy in a controlled environment. This review discusses how the PACS affects educational courses and clinical outcomes based on the available literature. Our aim was not only to outline recent research or developments but also to present a comprehensive overview regarding the growing role played by PACS in the modern healthcare system and academics. Similarly, we look at the challenges and opportunities associated with the wide adoption of PACS, highlighting possible future areas of study or teaching methodologies. Issues such as data security, interoperability, and the need for defined protocols are included to give an exhaustive understanding of what PACS can and cannot do. Through this study, we stress PACS's revolutionary potential in advancing research methodology and educational practices, eventually contributing to enhanced patient care and knowledge dissemination in healthcare areas. The continual growth of PACS technology and its applications is expected to reshape the landscape of medical research and education, making it a vital component in the quest for medical excellence. By knowing the present trends and future potential, stakeholders in healthcare and education may better employ PACS to reach their objectives and boost overall results.
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Affiliation(s)
- Albert P Varghese
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Shreya Naik
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | | | - Anurag Luharia
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Suhas Tivaskar
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Jubin John
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Gaurav V Mishra
- Department of Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Ashish Uke
- Department of Radiation Oncology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sweta G Pisulkar
- Department of Prosthodontics and Crown and Bridge, Sharad Pawar Dental College, Wardha, IND
| | - Mayur Wanjari
- Department of Research and Development, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Jelassi M, Jemai O, Demongeot J. Revolutionizing Radiological Analysis: The Future of French Language Automatic Speech Recognition in Healthcare. Diagnostics (Basel) 2024; 14:895. [PMID: 38732310 PMCID: PMC11083196 DOI: 10.3390/diagnostics14090895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 04/09/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in the French language. The methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. This research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with electronic health records (EHRs) and its utility in medical education. This study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.
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Affiliation(s)
- Mariem Jelassi
- RIADI Laboratory, Ecole Nationale des Sciences de l’Informatique (ENSI), Manouba University, La Manouba 2010, Tunisia;
- Health Tech Innovation Systems Inc., ENSI Innovation Hub, La Manouba 2010, Tunisia;
| | - Oumaima Jemai
- Health Tech Innovation Systems Inc., ENSI Innovation Hub, La Manouba 2010, Tunisia;
- Ecole Supérieure des Communications de Tunis (SUP’COM), Carthage University, Ariana 2083, Tunisia
| | - Jacques Demongeot
- AGEIS Laboratory, Faculté de Médecine, Université Grenoble Alpes (UGA), 38700 La Tronche, France
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Bhatnagar A, Kekatpure AL, Velagala VR, Kekatpure A. A Review on the Use of Artificial Intelligence in Fracture Detection. Cureus 2024; 16:e58364. [PMID: 38756254 PMCID: PMC11097122 DOI: 10.7759/cureus.58364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) simulates intelligent behavior using computers with minimum human intervention. Recent advances in AI, especially deep learning, have made significant progress in perceptual operations, enabling computers to convey and comprehend complicated input more accurately. Worldwide, fractures affect people of all ages and in all regions of the planet. One of the most prevalent causes of inaccurate diagnosis and medical lawsuits is overlooked fractures on radiographs taken in the emergency room, which can range from 2% to 9%. The workforce will soon be under a great deal of strain due to the growing demand for fracture detection on multiple imaging modalities. A dearth of radiologists worsens this rise in demand as a result of a delay in hiring and a significant percentage of radiologists close to retirement. Additionally, the process of interpreting diagnostic images can sometimes be challenging and tedious. Integrating orthopedic radio-diagnosis with AI presents a promising solution to these problems. There has recently been a noticeable rise in the application of deep learning techniques, namely convolutional neural networks (CNNs), in medical imaging. In the field of orthopedic trauma, CNNs are being documented to operate at the proficiency of expert orthopedic surgeons and radiologists in the identification and categorization of fractures. CNNs can analyze vast amounts of data at a rate that surpasses that of human observations. In this review, we discuss the use of deep learning methods in fracture detection and classification, the integration of AI with various imaging modalities, and the benefits and disadvantages of integrating AI with radio-diagnostics.
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Affiliation(s)
- Aayushi Bhatnagar
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aditya L Kekatpure
- Orthopedic Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Vivek R Velagala
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aashay Kekatpure
- Orthopedic Surgery, Narendra Kumar Prasadrao Salve Institute of Medical Sciences and Research, Nagpur, IND
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4
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Fico N, Grezia GD, Cuccurullo V, Salvia AAH, Iacomino A, Sciarra A, La Forgia D, Gatta G. Breast Imaging Physics in Mammography (Part II). Diagnostics (Basel) 2023; 13:3582. [PMID: 38066823 PMCID: PMC10706410 DOI: 10.3390/diagnostics13233582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/16/2023] [Accepted: 11/28/2023] [Indexed: 10/16/2024] Open
Abstract
One of the most frequently detected neoplasms in women in Italy is breast cancer, for which high-sensitivity diagnostic techniques are essential for early diagnosis in order to minimize mortality rates. As addressed in Part I of this work, we have seen how conditions such as high glandular density or limitations related to mammographic sensitivity have driven the optimization of technology and the use of increasingly advanced and specific diagnostic methodologies. While the first part focused on analyzing the use of a mammography machine from a physical and dosimetric perspective, in this paper, we will examine other techniques commonly used in breast imaging: contrast-enhanced mammography, digital breast tomosynthesis, radio imaging, and include some notes on image processing. We will also explore the differences between these various techniques to provide a comprehensive overview of breast lesion detection techniques. We will examine the strengths and weaknesses of different diagnostic modalities and observe how, with the implementation of improvements over time, increasingly effective diagnoses can be achieved.
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Affiliation(s)
- Noemi Fico
- Department of Physics “Ettore Pancini”, Università di Napoli Federico II, 80127 Naples, Italy
| | | | - Vincenzo Cuccurullo
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80013 Naples, Italy; (V.C.); (A.A.H.S.); (G.G.)
| | | | - Aniello Iacomino
- Department of Human Science, Guglielmo Marconi University, 00193 Rome, Italy;
| | - Antonella Sciarra
- Department of Experimental Medicine, Università della Campania “Luigi Vanvitelli”, 80013 Naples, Italy;
| | | | - Gianluca Gatta
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80013 Naples, Italy; (V.C.); (A.A.H.S.); (G.G.)
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Chatterjee S, Bhattacharya M, Pal S, Lee SS, Chakraborty C. ChatGPT and large language models in orthopedics: from education and surgery to research. J Exp Orthop 2023; 10:128. [PMID: 38038796 PMCID: PMC10692045 DOI: 10.1186/s40634-023-00700-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.
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Affiliation(s)
- Srijan Chatterjee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, 756020, Odisha, India
| | - Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea.
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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Serrano E, Moreno J, Llull L, Rodríguez A, Zwanzger C, Amaro S, Oleaga L, López-Rueda A. Radiomic-based nonlinear supervised learning classifiers on non-contrast CT to predict functional prognosis in patients with spontaneous intracerebral hematoma. RADIOLOGIA 2023; 65:519-530. [PMID: 38049251 DOI: 10.1016/j.rxeng.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/03/2023] [Indexed: 12/06/2023]
Abstract
PURPOSE To evaluate if nonlinear supervised learning classifiers based on non-contrast CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma. METHODS Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE > 18 years and with TCCSC performed within the first 24 h of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS 0-2) and poor prognosis (mRS 3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30% respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort. RESULTS 105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC 0.798, 0.752 and 0.742 respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (0.778-1;95%CI), with a false-negative rate of 0% for predicting poor functional prognosis at discharge. CONCLUSION The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.
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Affiliation(s)
- E Serrano
- Departamento Radiología, Hospital Universitario Bellvitge, Hospitalet de Llobregat, Barcelona, Spain
| | - J Moreno
- Clínica Iribas-IRM, Asunción, Paraguay
| | - L Llull
- Departamento de Neurología, Hospital Clínic, Barcelona, Spain
| | - A Rodríguez
- Departamento de Neurología, Hospital Clínic, Barcelona, Spain
| | - C Zwanzger
- Departamento Radiología, Hospital del Mar, Barcelona, Spain
| | - S Amaro
- Departamento de Neurología, Hospital Clínic, Barcelona, Spain
| | - L Oleaga
- Departamento Radiología, Hospital Clínic, Barcelona, Spain
| | - A López-Rueda
- Departamento Radiología, Hospital Clínic, Barcelona, Spain; Servicio de Informática Clínica, Hospital Clínic, Barcelona, Spain.
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Fico N, Di Grezia G, Cuccurullo V, Salvia AAH, Iacomino A, Sciarra A, Gatta G. Breast Imaging Physics in Mammography (Part I). Diagnostics (Basel) 2023; 13:3227. [PMID: 37892053 PMCID: PMC10606465 DOI: 10.3390/diagnostics13203227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 10/04/2023] [Indexed: 10/29/2023] Open
Abstract
Breast cancer is the most frequently diagnosed neoplasm in women in Italy. There are several risk factors, but thanks to screening and increased awareness, most breast cancers are diagnosed at an early stage when surgical treatment can most often be conservative and the adopted therapy is more effective. Regular screening is essential but advanced technology is needed to achieve quality diagnoses. Mammography is the gold standard for early detection of breast cancer. It is a specialized technique for detecting breast cancer and, thus, distinguishing normal tissue from cancerous breast tissue. Mammography techniques are based on physical principles: through the proper use of X-rays, the structures of different tissues can be observed. This first part of the paper attempts to explain the physical principles used in mammography. In particular, we will see how a mammogram is composed and what physical principles are used to obtain diagnostic images.
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Affiliation(s)
- Noemi Fico
- Department of Physics Ettore Pancini, Università di Napoli Federico II, 80126 Naples, Italy
| | | | - Vincenzo Cuccurullo
- Nuclear Medicine Unit, Department of Precision Medicine, Università della Campania Luigi Vanvitelli, 81100 Napoli, Italy;
| | | | - Aniello Iacomino
- Department of Human Science, Guglielmo Marconi University, 00193 Rome, Italy;
| | - Antonella Sciarra
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, 80138 Napoli, Italy;
| | - Gianluca Gatta
- Department of Precision Medicine, Università della Campania Luigi Vanvitelli, 81100 Napoli, Italy; (A.A.H.S.); (G.G.)
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Nabrawi E, Alanazi AT. Imaging in Healthcare: A Glance at the Present and a Glimpse Into the Future. Cureus 2023; 15:e36111. [PMID: 37065355 PMCID: PMC10098436 DOI: 10.7759/cureus.36111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 03/18/2023] Open
Abstract
The utilization of artificial intelligence (AI) applications in medical imaging relies heavily on imaging informatics. That is a one-of-a-kind professional who works at the crossroads of clinical radiography, data science, and information technology. Imaging informaticians are becoming crucial players in expanding, assessing, and implementing AI in the medical setting. Teleradiology will continue to be a cost-effective healthcare facility that expands. Vendor neutral archive (VNA) isolates image presentation and storing systems, permitting platforms to develop quickly, and is a repository for organization-wide healthcare image data. Efforts are made to incorporate and integrate diagnostic facilities such as radiography and pathology to fulfill the needs and demands of targeted therapy. Developments in computer-aided medical object identification may alter the environment of patient services. Finally, interpreting and processing distinct complex healthcare data will create a data-rich context where evidence-based care and performance development may be driven.
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Sharpe RE, Huffman RI, McLaughlin CG, Blubaugh P, Strobel MJ, Palen T. Applying Implementation Science Principles to Systematize High-Quality Care for Potentially Significant Imaging Findings. J Am Coll Radiol 2023; 20:324-334. [PMID: 36922106 DOI: 10.1016/j.jacr.2022.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/29/2022] [Accepted: 11/16/2022] [Indexed: 03/14/2023]
Abstract
OBJECTIVE Use principles of implementation science to improve the diagnosis and management of potentially significant imaging findings. METHODS Multidisciplinary stakeholders codified the diagnosis and management of potentially significant imaging findings in eight organs and created a finding tracking management system that was embedded in radiologist workflows and IT systems. Radiologists were trained to use this system. An automated finding tracking management system was created to support consistent high-quality care through care pathway visualizations, increased awareness of specific findings in the electronic medical record, templated notifications, and creation of an electronic safety net. Primary outcome was the rate of quality reviews related to eight targeted imaging findings. Secondary outcome was radiologist use of the finding tracking management tool. RESULTS In the 4 years after implementation, the tool was used to track findings in 7,843 patients who received 10,015 ultrasound, CT, MRI, x-ray, and nuclear medicine examinations that were interpreted by all 34 radiologists. Use of the tool lead to a decrease in related quality reviews (from 8.0% to 0.0%, P < .007). Use of the system increased from 1.7% of examinations in the early implementation phase to 3.1% (+82%, P < .00001) in the postimplementation phase. Each radiologist used the tool on an average of 294.6 unique examinations (SD 404.8). Overall, radiologists currently use the tool approximately 4,000 times per year. DISCUSSION Radiologists frequently used a finding tracking management system to ensure effective communication and raise awareness of the importance of recommended future follow-up studies. Use of this system was associated with a decrease in the rate of quality review requests in this domain.
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Affiliation(s)
- Richard E Sharpe
- Division Chair of Breast Imaging and Radiologist, Mayo Clinic, Phoenix, Arizona; Member, ACR Peer Learning Committee; Member, ACR Appropriateness Panel for Breast Imaging; and Member, ACR Commission on Screening & Emerging Technology Committee.
| | - Ryan I Huffman
- Radiologist, Scripps Clinic Medical Group, La Jolla, California
| | - Christopher G McLaughlin
- Radiologist, Department Technical Lead, Radiology, Colorado Permanente Medical Group, Denver, Colorado
| | | | - Mary Jo Strobel
- Director, Clinical Quality Oversight, Quality, Risk, and Patient Safety, Kaiser Permanente Colorado, Denver, Colorado
| | - Ted Palen
- Internal Medicine Physician and Scientific Investigator, Colorado Permanente Medical Group, Denver, Colorado
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Koebe P, Bohnet-Joschko S. The Impact of Digital Transformation on Inpatient Care: A Mixed Design Study (Preprint). JMIR Public Health Surveill 2022; 9:e40622. [PMID: 37083473 PMCID: PMC10163407 DOI: 10.2196/40622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/13/2023] [Accepted: 02/07/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND In the context of the digital transformation of all areas of society, health care providers are also under pressure to change. New technologies and a change in patients' self-perception and health awareness require rethinking the provision of health care services. New technologies and the extensive use of data can change provision processes, optimize them, or replace them with new services. The inpatient sector, which accounts for a particularly large share of health care spending, plays a major role in this regard. OBJECTIVE This study examined the influences of current trends in digitization on inpatient service delivery. METHODS We conducted a scoping review. This was applied to identify the international trends in digital transformation as they relate to hospitals. Future trends were considered from different perspectives. Using the defined inclusion criteria, international peer-reviewed articles published between 2016 and 2021 were selected. The extracted core trends were then contextualized for the German hospital sector with 12 experts. RESULTS We included 44 articles in the literature analysis. From these, 8 core trends could be deduced. A heuristic impact model of the trends was derived from the data obtained and the experts' assessments. This model provides a development corridor for the interaction of the trends with regard to technological intensity and supply quality. Trend accelerators and barriers were identified. CONCLUSIONS The impact analysis showed the dependencies of a successful digital transformation in the hospital sector. Although data interoperability is of particular importance for technological intensity, the changed self-image of patients was shown to be decisive with regard to the quality of care. We show that hospitals must find their role in new digitally driven ecosystems, adapt their business models to customer expectations, and use up-to-date information and communications technologies.
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Affiliation(s)
- Philipp Koebe
- Faculty of Management, Economics and Society, Witten/Herdecke University, Witten, Germany
| | - Sabine Bohnet-Joschko
- Faculty of Management, Economics and Society, Witten/Herdecke University, Witten, Germany
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Zong K, Wang* Z. An Empirical Study on Impact of Management Capabilities for the Multinational Company's Sustainable Competitive Advantage. J ORGAN END USER COM 2022. [DOI: 10.4018/joeuc.300763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Based on the deep learning (DL) theory, the study takes the multinational corporation technology company A as the research target, and explores the impact of information technology (IT) management capabilities on the sustainable competitive advantage of the company. Firstly, the proposed method summarizes the connotation and characteristics of IT management capabilities, and analyzes the nature and functions of enterprise IT management capabilities. Secondly, the study expounds three different theories of sustainable competition theory. Then, it briefly elaborated the principles of Artificial Neural Network (ANN) algorithms and the classification and definition of DL algorithms. Finally, Long Short-Term Memory (LSTM) is selected as the training algorithm of the model. The stock price trend of technology company A is used as the basis for judging competitiveness, and the influence of IT management ability on the company’s sustainable competitiveness is quantitatively analyzed.
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Karantanas AH, Efremidis S. The concept of the invisible radiologist in the era of artificial intelligence. Eur J Radiol 2022; 155:110147. [PMID: 35000823 DOI: 10.1016/j.ejrad.2021.110147] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/03/2021] [Accepted: 12/30/2021] [Indexed: 12/12/2022]
Abstract
The radiologists were traditionally working in the background. What upgraded them as physicians during the second half of the past century was their clinical training and function precipitated by the evolution of Interventional Radiology and Medical Imaging, especially with ultrasonography. These allowed them to participate in patient's diagnosis and treatment by direct contact as well asvia multidisciplinary medical consultations. The wide application of teleradiology and PACS pushed radiologists back again which is no longer acceptable, especially in view of the amazing applications of artificial intelligence (AI) in Radiology. It is our belief that clinical radiologists have to be able to control the penetration of AI in Radiology, securing their work for the benefit of both clinicians and patients.
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Affiliation(s)
- Apostolos H Karantanas
- Department of Radiology, Medical School, University of Crete, 71110 Heraklion, Greece; Department of Medical Imaging, University Hospital, 71110 Heraklion, Greece; Foundation for Research and Technology Hellas (FORTH), Computational Biomedicine Laboratory (CBML) - Hybrid Imaging, 70013 Heraklion, Greece.
| | - Stavros Efremidis
- Prof. Emeritus, Department of Radiology, University of Ioannina, 45110 Ioannina, Greece
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Coppola F, Faggioni L, Gabelloni M, De Vietro F, Mendola V, Cattabriga A, Cocozza MA, Vara G, Piccinino A, Lo Monaco S, Pastore LV, Mottola M, Malavasi S, Bevilacqua A, Neri E, Golfieri R. Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging. Front Psychol 2021; 12:710982. [PMID: 34650476 PMCID: PMC8505993 DOI: 10.3389/fpsyg.2021.710982] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/02/2021] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
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Affiliation(s)
- Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Fabrizio De Vietro
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vincenzo Mendola
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Alberto Piccinino
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Silvia Lo Monaco
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Margherita Mottola
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Silvia Malavasi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Emanuele Neri
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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14
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Jungmann F, Jorg T, Hahn F, Pinto Dos Santos D, Jungmann SM, Düber C, Mildenberger P, Kloeckner R. Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry. Acad Radiol 2021; 28:834-840. [PMID: 32414637 DOI: 10.1016/j.acra.2020.04.011] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 03/30/2020] [Accepted: 04/06/2020] [Indexed: 01/18/2023]
Abstract
OBJECTIVES We investigated the attitudes of radiologists, information technology (IT) specialists, and industry representatives on artificial intelligence (AI) and its future impact on radiological work. MATERIALS AND METHODS During a national meeting for AI, eHealth, and IT infrastructure in 2019, we conducted a survey to obtain participants' attitudes. A total of 123 participants completed 28 items exploring AI usage in medicine. The Kruskal-Wallis test was used to identify differences between radiologists, IT specialists, and industry representatives. RESULTS The strongest agreement between all respondents occurred with the following: plausibility checks are important to understand the decisions of the AI (93% agreement), validation of AI algorithms is mandatory (91%), and medicine becomes more efficient in the age of AI (86%). In contrast, only 25% of the respondents had confidence in the AI results, and only 17% believed that medicine will become more human through the use of AI. The answers were significantly different between the three professions for four items: relevance for protocol selection in cross-sectional imaging (p = 0.034), medical societies should be involved in validation (p = 0.028), patients should be informed about the use of AI (p = 0.047), and AI should be part of medical education (p = 0.026). CONCLUSION Currently, a discrepancy exists between high expectations for the future role of AI and low confidence in the results. This attitude was similar across all three groups. The demand for plausibility checks and the need to prove the usefulness in randomized controlled studies indicate what is needed in future research.
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Affiliation(s)
- Florian Jungmann
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany.
| | - Tobias Jorg
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | | | | | - Christoph Düber
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Peter Mildenberger
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Roman Kloeckner
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
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Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation. Invest Radiol 2021; 55:619-627. [PMID: 32776769 DOI: 10.1097/rli.0000000000000673] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Although artificial intelligence (AI) has been a focus of medical research for decades, in the last decade, the field of radiology has seen tremendous innovation and also public focus due to development and application of machine-learning techniques to develop new algorithms. Interestingly, this innovation is driven simultaneously by academia, existing global medical device vendors, and-fueled by venture capital-recently founded startups. Radiologists find themselves once again in the position to lead this innovation to improve clinical workflows and ultimately patient outcome. However, although the end of today's radiologists' profession has been proclaimed multiple times, routine clinical application of such AI algorithms in 2020 remains rare. The goal of this review article is to describe in detail the relevance of appropriate imaging data as a bottleneck for innovation, provide insights into the many obstacles for technical implementation, and give additional perspectives to radiologists who often view AI solely from their clinical role. As regulatory approval processes for such medical devices are currently under public discussion and the relevance of imaging data is transforming, radiologists need to establish themselves as the leading gatekeepers for evolution of their field and be aware of the many stakeholders and sometimes conflicting interests.
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Bosmans H, Zanca F, Gelaude F. Procurement, commissioning and QA of AI based solutions: An MPE's perspective on introducing AI in clinical practice. Phys Med 2021; 83:257-263. [PMID: 33984579 DOI: 10.1016/j.ejmp.2021.04.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/24/2021] [Accepted: 04/06/2021] [Indexed: 12/11/2022] Open
Abstract
PURPOSE In this study, we propose a framework to help the MPE take up a unique and important role at the introduction of AI solutions in clinical practice, and more in particular at procurement, acceptance, commissioning and QA. MATERIAL AND METHODS The steps for the introduction of Medical Radiological Equipment in a hospital setting were extrapolated to AI tools. Literature review and in-house experience was added to prepare similar, yet dedicated test methods. RESULTS Procurement starts from the clinical cases to be solved and is usually a complex process with many stakeholders and possibly many candidate AI solutions. Specific KPIs and metrics need to be defined. Acceptance testing follows, to verify the installation and test for critical exams. Commissioning should test the suitability of the AI tool for the intended use in the local institution. Results may be predicted from peer reviewed papers that treat representative populations. If not available, local data sets can be prepared to assess the KPIs, or 'virtual clinical trials' could be used to create large, simulated test data sets. Quality assurance must be performed periodically to verify if KPIs are stable, especially if the software is upscaled or upgraded, and as soon as self-learning AI tools would enter the medical practice. DISCUSSION MPEs are well placed to bridge between manufacturer and medical team and help from procurement up to reporting to the management board. More work is needed to establish consolidated test protocols.
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Affiliation(s)
- Hilde Bosmans
- University Hospitals of the KU Leuven, Leuven, Belgium.
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17
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Aghnia Farda N, Lai JY, Wang JC, Lee PY, Liu JW, Hsieh IH. Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques. Injury 2021; 52:616-624. [PMID: 32962829 DOI: 10.1016/j.injury.2020.09.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 09/15/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND Classification of the type of calcaneal fracture on CT images is essential in driving treatment. However, human-based classification can be challenging due to anatomical complexities and CT image constraints. The use of computer-aided classification system in standard practice is additionally hindered by the availability of training images. The aims of this study is to 1) propose a deep learning network combined with data augmentation technique to classify calcaneal fractures on CT images into the Sanders system, and 2) assess the efficiency of such approach with differential training methods. METHODS In this study, the Principle component analysis (PCA) network was selected for the deep learning neural network architecture for its superior performance. CT calcaneal images were processed through PCA filters, binary hashing, and a block-wise histogram. The Augmentor pipeline including rotation, distortion, and flips was applied to generate artificial calcaneus fractured images. Two types of training approaches and five data sample sizes were investigated to evaluate the performance of the proposed system with and without data augmentation. RESULTS Compared to the original performance, use of augmented images during training improved network performance accuracy by almost twofold in classifying Sanders fracture types for all dataset sizes. A fivefold increase in the number of augmented training images improved network classification accuracy by 35%. The proposed deep CNN model achieved 72% accuracy in classifying CT calcaneal images into the four Sanders categories when trained with sufficient augmented artificial images. CONCLUSION The proposed deep-learning algorithm coupled with data augmentation provides a feasible and efficient approach to the use of computer-aided system in assisting physicians in evaluating calcaneal fracture types.
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Affiliation(s)
- Nurya Aghnia Farda
- Department of Computer Science and Information Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan
| | - Jiing-Yih Lai
- Department of Mechanical Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan
| | - Jia-Ching Wang
- Department of Computer Science and Information Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan; Pervasive Artificial Intelligence (PAIR) Labs, Taipei City, Taiwan
| | - Pei-Yuan Lee
- Orthopedic Department, Show Chwan Memorial Hospital, Changhua City, Taiwan
| | - Jia-Wei Liu
- Institute of Cognitive Neuroscience, National Central University, No. 300, Jhongda Rd., Jhongli County, Taoyuan City 32001, Taiwan
| | - I-Hui Hsieh
- Institute of Cognitive Neuroscience, National Central University, No. 300, Jhongda Rd., Jhongli County, Taoyuan City 32001, Taiwan.
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18
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Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective? Eur Radiol 2020; 30:5510-5524. [PMID: 32377810 PMCID: PMC7476980 DOI: 10.1007/s00330-020-06874-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 03/18/2020] [Accepted: 04/08/2020] [Indexed: 12/18/2022]
Abstract
Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)–based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems. Key Points • Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration. • The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems. • While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns.
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19
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Neri E, Miele V, Coppola F, Grassi R. Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology. Radiol Med 2020; 125:505-508. [PMID: 32350794 PMCID: PMC7189175 DOI: 10.1007/s11547-020-01197-9] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 04/13/2020] [Indexed: 01/08/2023]
Abstract
The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian radiology departments found themselves at the forefront in the management of suspected and positive COVID cases, both in diagnosis, in estimating the severity of the disease and in follow-up. In this context SIRM recommends chest X-ray as first-line imaging tool, CT as additional tool that shows typical features of COVID pneumonia, and ultrasound of the lungs as monitoring tool. SIRM recommends, as high priority, to ensure appropriate sanitation procedures on the scan equipment after detecting any suspected or positive COVID-19 patients. In this emergency situation, several expectations have been raised by the scientific community about the role that artificial intelligence can have in improving the diagnosis and treatment of coronavirus infection, and SIRM wishes to deliver clear statements to the radiological community, on the usefulness of artificial intelligence as a radiological decision support system in COVID-19 positive patients. (1) SIRM supports the research on the use of artificial intelligence as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care, and welcomes single center of multicenter studies for a clinical validation of the test. (2) SIRM does not support the use of CT with artificial intelligence for screening or as first-line test to diagnose COVID-19. (3) Chest CT with artificial intelligence cannot replace molecular diagnosis tests with nose-pharyngeal swab (rRT-PCR) in suspected for COVID-19 patients.
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Affiliation(s)
- Emanuele Neri
- Department of Translational Research, Diagnostic Radiology 3, University of Pisa, Pisa, Italy.
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Francesca Coppola
- Malpighi Radiology Unit, Department of Diagnostic and Preventive Medicine, Sant'Orsola Malpighi University Hospital, Bologna, Italy
| | - Roberto Grassi
- Department of Clinical and Experimental Medicine, "F. Magrassi-A. Lanzara", University of Campania "Luigi Vanvitelli", Naples, Italy
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20
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The Importance of Data Analytics and Business Intelligence for Radiologists. J Am Coll Radiol 2020; 17:511-514. [PMID: 31958416 DOI: 10.1016/j.jacr.2019.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 12/20/2019] [Indexed: 11/22/2022]
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21
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Kalmet PHS, Sanduleanu S, Primakov S, Wu G, Jochems A, Refaee T, Ibrahim A, Hulst LV, Lambin P, Poeze M. Deep learning in fracture detection: a narrative review. Acta Orthop 2020; 91:215-220. [PMID: 31928116 PMCID: PMC7144272 DOI: 10.1080/17453674.2019.1711323] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, particularly deep learning, has recently made substantial strides in perception tasks allowing machines to better represent and interpret complex data. Deep learning is a subset of AI represented by the combination of artificial neuron layers. In the last years, deep learning has gained great momentum. In the field of orthopaedics and traumatology, some studies have been done using deep learning to detect fractures in radiographs. Deep learning studies to detect and classify fractures on computed tomography (CT) scans are even more limited. In this narrative review, we provide a brief overview of deep learning technology: we (1) describe the ways in which deep learning until now has been applied to fracture detection on radiographs and CT examinations; (2) discuss what value deep learning offers to this field; and finally (3) comment on future directions of this technology.
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Affiliation(s)
| | - Sebastian Sanduleanu
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Sergey Primakov
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Guangyao Wu
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Turkey Refaee
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Abdalla Ibrahim
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Luca v. Hulst
- Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Martijn Poeze
- Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht
- Nutrim School for Nutrition, Toxicology and Metabolism, Maastricht University, Maastricht, The Netherlands
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22
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van Hoek J, Huber A, Leichtle A, Härmä K, Hilt D, von Tengg-Kobligk H, Heverhagen J, Poellinger A. A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol 2019; 121:108742. [DOI: 10.1016/j.ejrad.2019.108742] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/28/2019] [Accepted: 11/06/2019] [Indexed: 02/07/2023]
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Ooi SKG, Makmur A, Soon AYQ, Fook-Chong S, Liew C, Sia SY, Ting YH, Lim CY. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey. Singapore Med J 2019; 62:126-134. [PMID: 31680181 DOI: 10.11622/smedj.2019141] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION We aimed to assess the attitudes and learner needs of radiology residents and faculty radiologists regarding artificial intelligence (AI) and machine learning (ML) in radiology. METHODS A web-based questionnaire, designed using SurveyMonkey, was sent out to residents and faculty radiologists in all three radiology residency programmes in Singapore. The questionnaire comprised four sections and aimed to evaluate respondents' current experience, attempts at self-learning, perceptions of career prospects and expectations of an AI/ML curriculum in their residency programme. Respondents' anonymity was ensured. RESULTS A total of 125 respondents (86 male, 39 female; 70 residents, 55 faculty radiologists) completed the questionnaire. The majority agreed that AI/ML will drastically change radiology practice (88.8%) and makes radiology more exciting (76.0%), and most would still choose to specialise in radiology if given a choice (80.0%). 64.8% viewed themselves as novices in their understanding of AI/ML, 76.0% planned to further advance their AI/ML knowledge and 67.2% were keen to get involved in an AI/ML research project. An overwhelming majority (84.8%) believed that AI/ML knowledge should be taught during residency, and most opined that this was as important as imaging physics and clinical skills/knowledge curricula (80.0% and 72.8%, respectively). More than half thought that their residency programme had not adequately implemented AI/ML teaching (59.2%). In subgroup analyses, male and tech-savvy respondents were more involved in AI/ML activities, leading to better technical understanding. CONCLUSION A growing optimism towards radiology undergoing technological transformation and AI/ML implementation has led to a strong demand for an AI/ML curriculum in residency education.
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Affiliation(s)
- Su Kai Gideon Ooi
- Department of Nuclear Medicine and Molecular Imaging, Division of Radiological Sciences, Singapore General Hospital, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | | | | | - Charlene Liew
- Department of Diagnostic Radiology, Changi General Hospital, Singapore
| | - Soon Yiew Sia
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Yong Han Ting
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore
| | - Chee Yeong Lim
- Department of Diagnostic Radiology, Division of Radiological Sciences, Singapore General Hospital, Singapore
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Dewey M, Wilkens U. The Bionic Radiologist: avoiding blurry pictures and providing greater insights. NPJ Digit Med 2019; 2:65. [PMID: 31388567 PMCID: PMC6616477 DOI: 10.1038/s41746-019-0142-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 05/28/2019] [Indexed: 12/11/2022] Open
Abstract
Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care. The Bionic Radiologist is a concept that combines humanity and digitalization for better health care integration of radiology. At a practical level, this concept will achieve critical goals: (1) testing decisions being made scientifically on the basis of disease probabilities and patient preferences; (2) image analysis done consistently at any time and at any site; and (3) treatment suggestions that are closely linked to imaging results and are seamlessly integrated with other information. The Bionic Radiologist will thus help avoiding missed care opportunities, will provide continuous learning in the work process, and will also allow more time for radiologists' primary roles: interacting with patients and referring physicians. To achieve that potential, one has to cope with many implementation barriers at both the individual and institutional levels. These include: reluctance to delegate decision making, a possible decrease in image interpretation knowledge and the perception that patient safety and trust are at stake. To facilitate implementation of the Bionic Radiologist the following will be helpful: uncertainty quantifications for suggestions, shared decision making, changes in organizational culture and leadership style, maintained expertise through continuous learning systems for training, and role development of the involved experts. With the support of the Bionic Radiologist, disparities are reduced and the delivery of care is provided in a humane and personalized fashion.
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Affiliation(s)
- Marc Dewey
- Charité—Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, Germany
| | - Uta Wilkens
- Ruhr-University Bochum, Institute of Work Science, Bochum, Germany
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25
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What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging 2019; 10:44. [PMID: 30949865 PMCID: PMC6449411 DOI: 10.1186/s13244-019-0738-2] [Citation(s) in RCA: 159] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 03/20/2019] [Indexed: 02/08/2023] Open
Abstract
This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution.Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.
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Schlemmer HP, Bittencourt LK, D’Anastasi M, Domingues R, Khong PL, Lockhat Z, Muellner A, Reiser MF, Schilsky RL, Hricak H. Global Challenges for Cancer Imaging. J Glob Oncol 2018; 4:1-10. [PMID: 30241164 PMCID: PMC6180759 DOI: 10.1200/jgo.17.00036] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Imaging plays many essential roles in nearly all aspects of high-quality cancer care. However, challenges to the delivery of optimal cancer imaging in both developing and advanced countries are manifold. Developing countries typically face dramatic shortages of both imaging equipment and general radiologists, and efforts to improve cancer imaging in these countries are often complicated by poor infrastructure, cultural barriers, and other obstacles. In advanced countries, on the other hand, although imaging equipment and general radiologists are typically accessible, the complexity of oncologic imaging and the need for subspecialists in the field are largely unrecognized; as a result, training opportunities are lacking, and there is a shortage of radiologists with the necessary subspecialty expertise to provide optimal cancer care and participate in advanced clinical research. This article is intended to raise awareness of these challenges and catalyze further efforts to address them. Some promising strategies and ongoing efforts are reviewed, and some specific actions are proposed.
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Affiliation(s)
- Heinz-Peter Schlemmer
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Leonardo K. Bittencourt
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Melvin D’Anastasi
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Romeu Domingues
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Pek-Lan Khong
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Zarina Lockhat
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Ada Muellner
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Maximilian F. Reiser
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Richard L. Schilsky
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
| | - Hedvig Hricak
- Heinz-Peter Schlemmer, German Cancer Research Center,
Heidelberg; Melvin D’Anastasi and Maximilian F.
Reiser, Ludwig-Maximilians-University Hospital, Munich, Germany;
Leonardo K. Bittencourt, Fluminense Federal University,
Niterói; Leonardo K. Bittencourt and Romeu
Domingues, Clínica de Diagnóstico por Imagem
(CDPI/Dasa), Rio de Janeiro, Brazil; Pek-Lan Khong, University of
Hong Kong, Queen Mary Hospital, Hong Kong, China; Zarina Lockhat,
University of Pretoria, Steve Biko Academic Hospital, Pretoria, South Africa;
Ada Muellner and Hedvig Hricak, Memorial Sloan
Kettering Cancer Center, New York, NY; and Richard L. Schilsky,
American Society of Clinical Oncology, Alexandria, VA
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Affiliation(s)
- Marc Dewey
- Charité -Universitätsmedizin Berlin, Berlin 10117, Germany.
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Radiomics - the value of the numbers in present and future radiology. Pol J Radiol 2018; 83:e171-e174. [PMID: 30627231 PMCID: PMC6323541 DOI: 10.5114/pjr.2018.75794] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 09/29/2017] [Indexed: 01/06/2023] Open
Abstract
Radiomics is a new concept that has been functioning in medicine for only a few years. This idea, created recently, relies on processing innumerable quantities of metadata acquired from every examination, followed by extraction thereof from relevant imaging examinations, such as computer tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) images, by means of appropriate created algorithms. The extracted results have great potential and broad possibilities of application. Thanks to these we can verify efficiency of treatment, predict locations of metastases of tumours, correlate results with histopathological examinations, or define the type of cancer more precisely. In effect, we obtain more personalised treatment for each patient, which is extremely important and highly recommendable in the tests and applicable treatment therapies conducted nowadays. Radiomics is a non-invasive and high efficiency post-processing method. This article is intended to explain the idea of radiomics, the mechanisms of data acquisition, existing possibilities, and the challenges incurred by radiologists and physicians at the stage of making diagnosis or conducting treatment.
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Abstract
The European Society of Radiology (ESR) established a Working Group on Value-Based Imaging (VBI WG) in August 2016 in response to developments in European healthcare systems in general, and the trend within radiology to move from volume- to value-based practice in particular. The value-based healthcare (VBH) concept defines "value" as health outcomes achieved for patients relative to the costs of achieving them. Within this framework, value measurements start at the beginning of therapy; the whole diagnostic process is disregarded, and is considered only if it is the cause of errors or complications. Making the case for a new, multidisciplinary organisation of healthcare delivery centred on the patient, this paper establishes the diagnosis of disease as a first outcome in the interrelated activities of the healthcare chain. Metrics are proposed for measuring the quality of radiologists' diagnoses and the various ways in which radiologists provide value to patients, other medical specialists and healthcare systems at large. The ESR strongly believes value-based radiology (VBR) is a necessary complement to existing VBH concepts. The Society is determined to establish a holistic VBR programme to help European radiologists deal with changes in the evolution from volume- to value-based evaluation of radiological activities. Main Messages • Value-based healthcare defines value as patient's outcome over costs. • The VBH framework disregards the diagnosis as an outcome. • VBH considers diagnosis only if wrong or a cause of complications. • A correct diagnosis is the first outcome that matters to patients. • Metrics to measure radiologists' impacts on patient outcomes are key. • The value provided by radiology is multifaceted, going beyond exam volumes.
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