Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers

Mongan, John; Moy, Linda; Kahn, Charles Η.

doi:10.1148/ryai.2020200029

Cited by 759 publications

(535 citation statements)

References 30 publications

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“…In addition to the above-mentioned standardized guidelines for research in health care, future research should make use of already existing guidelines for reporting the technical part of AI-based conversational agents used in health care and medicine [ 72 , 73 ]. More generalized checklists aimed at assessing the overall structure of AI-related medical research such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) could be also consulted; they offer guidance on which specific information should be reported on the chosen AI model and its subsequent training, evaluation, and performance [ 74 ]. We further recommend future research to synthesize a generic system architecture and derive a framework for AI-based chatbots in the context of health care for chronic diseases once the field has progressed and more standardized data are available.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Schachner¹,

Keller²,

Wangenheim³

2020

J Med Internet Res

157

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Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Schachner¹,

Keller²,

Wangenheim³

2020

J Med Internet Res

157

View full text Add to dashboard Cite

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“…Although comparable to the third-party algorithm (A3), A2 showed in average lower maximal Hausdorff distances, pointing at a better agreement with the reference standard segmentation. This might be in part attributed to the fact that the reference standard of training and test subsets were created by the same human raters [ 22 ], but is also reflected by higher segmentation accuracy in specific cases with coexisting pneumothorax or massive pleural effusion.…”

Section: Discussionmentioning

confidence: 99%

Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning

Anastasopoulos

Weikert

Yang

et al. 2020

European Journal of Radiology

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Highlights It is feasible to develop clinically useful AI-based software for quantification of pulmonary opacities in COVID-19 in just 10 days. An established pipeline for fast transition of prototypes to full clinical implementation is an important key to success. Human-level performance, even in the presence of advanced disease, was achieved with less than 200 chest CT scans for training of the AI algorithm.

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“…During the development period of this instrument, we also considered machine learning and deep learning relevant checklists, e.g. radiomics quality score [54], Checklist for Arti cial Intelligence in Medical Imaging [55], Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research [56], all of which are specialized assessment tools for cutting-edge arti cial intelligence models.…”

Section: -Critical Appraisalmentioning

confidence: 99%

Prognostic Models for Knee Osteoarthritis: A Protocol for Systematic Review, Critical Appraisal and Meta-Analysis

Zhong

Zhang

et al. 2020

Preprint

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Background: Osteoarthritis is the most common degenerative joint disease diagnosed in clinical practice. It is associated with significant socioeconomic burden and poor quality of life, a large proportion of which is due to knee osteoarthritis (KOA), mainly driven by total knee arthroplasty (TKA). As the difficulty of being detected early and deficiency of disease-modifying drug, the focus of KOA is shifting to disease prevention and the treatment to delay its rapid progression. Thus, the prognostic prediction models are called for, to stratify individuals to guide clinical decision making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA which concern about three clinical concerns: (1) the risk of developing KOA in general population; (2) the risk of receiving TKA in KOA patients; and (3) the outcome of TKA in KOA patients who plan to receive TKA.Methods: Studies will be identified by searching seven electronic databases. Title and abstract screening and full-text review will be accomplished by two independent reviewers. Data extraction instrument and critical appraisal instrument will be developed before formal assessment, and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST). Prognostic prediction models will be summarized qualitatively. Quantitative metrics on predictive performance of these models will be synthesized with meta-analyses if appropriate.Discussion: Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation or impact assessment will be made.Systematic review registration: PROSPERO (registered, waiting for assessment, ID 203543)

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Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers

Abstract: Section/TopicNo. Item TITLE or ABSTRACT 1 Identification as a study of AI methodology, specifying the category of technology used (eg, deep learning) ABSTRACT 2 Structured summary of study design, methods, results, and conclusions

Cited by 759 publications

References 30 publications

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning

Prognostic Models for Knee Osteoarthritis: A Protocol for Systematic Review, Critical Appraisal and Meta-Analysis

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