EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases

BackgroundTremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).MethodsA multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.ResultsThree overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.ConclusionThese EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

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Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations

RMD Open ◽

10.1136/rmdopen-2019-001004 ◽

2019 ◽

Vol 5 (2) ◽

pp. e001004 ◽

Cited By ~ 10

Author(s):

Joanna Kedra ◽

Timothy Radstake ◽

Aridaman Pandit ◽

Xenofon Baraliakos ◽

Francis Berenbaum ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Literature Review ◽

Systematic Literature Review ◽

Musculoskeletal Diseases ◽

Data Sources ◽

Current Status ◽

Eular Recommendations ◽

European League Against Rheumatism ◽

Data Points

ObjectiveTo assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).MethodsA systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.ResultsOf 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000–5 billion) in RMDs, and 9.1 billion (range 100 000–200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).ConclusionsBig data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.

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The 2017 EULAR standardised procedures for ultrasound imaging in rheumatology

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2017-211585 ◽

2017 ◽

Vol 76 (12) ◽

pp. 1974-1979 ◽

Cited By ~ 63

Author(s):

Ingrid Möller ◽

Iustina Janta ◽

Marina Backhaus ◽

Sarah Ohrndorf ◽

David A Bong ◽

...

Keyword(s):

Musculoskeletal Diseases ◽

Task Force ◽

Nominal Group Technique ◽

Nominal Group ◽

Anatomic Structure ◽

Scanning Method ◽

European League Against Rheumatism ◽

The Us ◽

Technological Developments ◽

Probe Placement

BackgroundIn 2001, the European League Against Rheumatism developed and disseminated the first guidelines for musculoskeletal (MS) ultrasound (US) in rheumatology. Fifteen years later, the dramatic expansion of new data on MSUS in the literature coupled with technological developments in US imaging has necessitated an update of these guidelines.ObjectivesTo update the existing MSUS guidelines in rheumatology as well as to extend their scope to other anatomic structures relevant for rheumatology.MethodsThe project consisted of the following steps: (1) a systematic literature review of MSUS evaluable structures; (2) a Delphi survey among rheumatologist and radiologist experts in MSUS to select MS and non-MS anatomic structures evaluable by US that are relevant to rheumatology, to select abnormalities evaluable by US and to prioritise these pathologies for rheumatology and (3) a nominal group technique to achieve consensus on the US scanning procedures and to produce an electronic illustrated manual (ie, App of these procedures).ResultsStructures from nine MS and non-MS areas (ie, shoulder, elbow, wrist and hand, hip, knee, ankle and foot, peripheral nerves, salivary glands and vessels) were selected for MSUS in rheumatic and musculoskeletal diseases (RMD) and their detailed scanning procedures (ie, patient position, probe placement, scanning method and bony/other landmarks) were used to produce the App. In addition, US evaluable abnormalities present in RMD for each anatomic structure and their relevance for rheumatology were agreed on by the MSUS experts.ConclusionsThis task force has produced a consensus-based comprehensive and practical framework on standardised procedures for MSUS imaging in rheumatology.

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2019 EULAR points to consider for the assessment of competences in rheumatology specialty training

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-218015 ◽

2020 ◽

Vol 80 (1) ◽

pp. 65-70 ◽

Cited By ~ 1

Author(s):

Francisca Sivera ◽

Alessia Alunno ◽

Aurélie Najm ◽

Tadej Avcin ◽

Xenofon Baraliakos ◽

...

Keyword(s):

Musculoskeletal Diseases ◽

Task Force ◽

Training Programme ◽

Assessment Practices ◽

Specialty Training ◽

Sufficient Time ◽

Effective Implementation ◽

European League Against Rheumatism ◽

Training Programmes ◽

Level Of Agreement

Background and aimStriving for harmonisation of specialty training and excellence of care in rheumatology, the European League Against Rheumatism (EULAR) established a task force to develop points to consider (PtCs) for the assessment of competences during rheumatology specialty training.MethodsA systematic literature review on the performance of methods for the assessment of competences in rheumatology specialty training was conducted. This was followed by focus groups in five selected countries to gather information on assessment practices and priorities. Combining the collected evidence with expert opinion, the PtCs were formulated by the multidisciplinary task force, including rheumatologists, medical educationalists, and people with rheumatic and musculoskeletal diseases. The level of agreement (LoA) for each PtC was anonymously voted online.ResultsFour overarching principles and 10 PtCs were formulated. The overarching principles highlighted the importance of assessments being closely linked to the rheumatology training programme and protecting sufficient time and resources to ensure effective implementation. In the PtCs, two were related to overall assessment strategy (PtCs 1 and 5); three focused on formative assessment and portfolio (PtCs 2–4); three focused on the assessment of knowledge, skills or professionalism (PtCs 6–8); one focused on trainees at risk of failure (PtC 9); and one focused on training the trainers (PtC 10). The LoA (0–10) ranged from 8.75 to 9.9.ConclusionThese EULAR PtCs provide European guidance on assessment methods throughout rheumatology training programmes. These can be used to benchmark current practices and to develop future strategies, thereby fostering continuous improvement in rheumatology learning and, ultimately, in patient care.

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Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

International Journal of Research in Medical Sciences ◽

10.18203/2320-6012.ijrms20195940 ◽

2019 ◽

Vol 8 (1) ◽

pp. 382

Author(s):

Rahul Badwaik

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Healthcare Setting ◽

Digital Transformation ◽

Current Status ◽

Healthcare Industry ◽

Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

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Research on quantitative analysis method of street space quality evaluation, Whuan City centre

Proceedings of the 56th ISOCARP World Planning Congress ◽

10.47472/oxms9596 ◽

2020 ◽

Author(s):

Huihui Yan ◽

◽

Runzhi Huang ◽

Yunming Cheng ◽

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Evaluation System ◽

Information Technologies ◽

Quality Evaluation ◽

Hot Spot ◽

Central City ◽

Current Status ◽

City Centre ◽

Street Space

ith the continuous development of technical means, information technologies such as big data and artificial intelligence have gradually become one of the core technical means of planning and design. Applying AI and big data to evaluate street space has also become one hot spot in recent years. However, there are few studies on the street space quality of Wuhan based on new technology, and especially there is almost no evaluation system that combines planning technology and information technology. This study employs big data, traditional planning data and current status survey data, combined with artificial intelligence, ArcGIS spatial analysis and spatial syntax and other analytical techniques, to propose a comprehensive system for evaluating street space quality. This paper selects an area in the central city of Wuhan for the case study on the quality evaluation system, and accordingly provides an analytic idea for the planning and construction of streets, so as to guide the implementation of street-related projects and planning.

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Translation of Genomics into Precision Medicine with Artificial Intelligence: A Narrative Review on Applications, Challenges and Future Perspectives

10.14293/s2199-1006.1.sor-.pp75wqs.v1 ◽

2020 ◽

Author(s):

Arin Natania. S

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Precision Medicine ◽

Language Processing ◽

Variant Calling ◽

Genomic Analysis ◽

Current Status ◽

Sequencing Technologies ◽

Main Driver ◽

Beneficial Impact

In the field of genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence. (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

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AB1274 OF 60 PUBLICATIONS REFERRING TO BIG DATA IN RHEUMATIC AND MUSCULOSKELETAL DISEASES, 33 APPLIED ARTIFICIAL INTELLIGENCE STATISTICAL TECHNIQUES: A SYSTEMATIC LITERATURE REVIEW INFORMING A EULAR TASKFORCE

10.1136/annrheumdis-2019-eular.3340 ◽

2019 ◽

Author(s):

KEDRA Joanna ◽

Timothy R. Radstake ◽

Laure Gossec

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Literature Review ◽

Systematic Literature Review ◽

Musculoskeletal Diseases ◽

Statistical Techniques ◽

Applied Artificial Intelligence

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Learning Analytics Considered Harmful

Online Learning ◽

10.24059/olj.v16i3.272 ◽

2012 ◽

Vol 16 (3) ◽

Cited By ~ 3

Author(s):

Laurie P Dringus

Keyword(s):

Big Data ◽

Online Learning ◽

Learning Analytics ◽

Online Courses ◽

Research Community ◽

Current Status ◽

Online Course ◽

Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

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