The impact of artificial intelligence on the reading times of radiologists for chest radiographs

Shin, Hyun Joo; Han, Kyunghwa; Ryu, Leeha; Kim, Min Jung

doi:10.1038/s41746-023-00829-4

Cited by 41 publications

(21 citation statements)

References 27 publications

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“…Due to the heterogeneity of algorithms applied, varying experience and background of clinical readers as well as different data sets used for performance testing, comparability between studies is limited. However, AI-based analyses offer several advantages, including improved accuracy, time savings, reproducibility and objectivity [ 22 ]. However, concerns regarding the application of AI for analyses of radiographs need to be recognized.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence‐based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility

Stotter,

Klestil,

Chen

et al. 2023

Knee surg. sports traumatol. arthrosc.

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Purpose The aim of this study was to investigate the performance of an artificial intelligence (AI)-based software for fully automated analysis of leg alignment pre- and postoperatively after high tibial osteotomy (HTO) on long-leg radiographs (LLRs). Methods Long-leg radiographs of 95 patients with varus malalignment that underwent medial open-wedge HTO were analyzed pre- and postoperatively. Three investigators and an AI software using deep learning algorithms (LAMA™, ImageBiopsy Lab, Vienna, Austria) evaluated the hip–knee–ankle angle (HKA), mechanical axis deviation (MAD), joint line convergence angle (JLCA), medial proximal tibial angle (MPTA), and mechanical lateral distal femoral angle (mLDFA). All measurements were performed twice and the performance of the AI software was compared with individual human readers using a Bayesian mixed model. In addition, the inter-observer intraclass correlation coefficient (ICC) for inter-observer reliability was evaluated by comparing measurements from manual readers. The intra-reader variability for manual measurements and the AI-based software was evaluated using the intra-observer ICC. Results Initial varus malalignment was corrected to slight valgus alignment after HTO. Measured by the AI algorithm and manually HKA (5.36° ± 3.03° and 5.47° ± 2.90° to − 0.70 ± 2.34 and − 0.54 ± 2.31), MAD (19.38 mm ± 11.39 mm and 20.17 mm ± 10.99 mm to − 2.68 ± 8.75 and − 2.10 ± 8.61) and MPTA (86.29° ± 2.42° and 86.08° ± 2.34° to 91.6 ± 3.0 and 91.81 ± 2.54) changed significantly from pre- to postoperative, while JLCA and mLDFA were not altered. The fully automated AI-based analyses showed no significant differences for all measurements compared with manual reads neither in native preoperative radiographs nor postoperatively after HTO. Mean absolute differences between the AI-based software and mean manual observer measurements were 0.5° or less for all measurements. Inter-observer ICCs for manual measurements were good to excellent for all measurements, except for JLCA, which showed moderate inter-observer ICCs. Intra-observer ICCs for manual measurements were excellent for all measurements, except for JLCA and for MPTA postoperatively. For the AI-aided analyses, repeated measurements showed entirely consistent results for all measurements with an intra-observer ICC of 1.0. Conclusions The AI-based software can provide fully automated analyses of native long-leg radiographs in patients with varus malalignment and after HTO with great accuracy and reproducibility and could support clinical workflows. Level of evidence Diagnostic study, Level III.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence‐based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility

Stotter,

Klestil,

Chen

et al. 2023

Knee surg. sports traumatol. arthrosc.

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“…The articles on efficiency or accuracy were published during 2021-2023 and were mainly from China and the United States, with three papers each. In seven articles, AI was used to diagnose lung abnormalities [28][29][30][31][32][33][34] and in two articles, AI was applied in breast cancer detection [7,35]. In the remaining articles, AI was used in detecting thyroid nodules [36], acute fractures [37], and intracranial hemorrhage [38] as well as checking endotracheal tube (ETT) placement [39].…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

He,

Liu,

et al. 2024

iRADIOLOGY

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In this scoping review, we evaluated the performance of artificial intelligence (AI) in clinical radiology practice and examined health professionals' perspectives regarding AI use in radiology. This review followed the Joanna Briggs Institute (JBI) methodological guidelines. We searched multiple databases and the gray literature from March 15, 2016 to December 31, 2023. Of 49 articles reviewed, 13 assessed the performance of AI in radiology clinical practice, and 36 examined the attitudes of health professionals toward the use of AI in radiology. In four separate studies, AI significantly improved the diagnostic sensitivity or detection rate. Furthermore, six articles emphasized a significant reduction in case reading times with AI use. Although three studies suggested an increase in specificity with the assistance of AI, these findings did not reach statistical significance. Health professionals expressed the belief that AI would have a significant impact on radiology but would not replace radiologists in the near future. Limited knowledge of AI was observed among health professionals, who supported increased education and explicit regulations and guidelines related to AI. Overall, AI can enhance diagnostic efficiency and accuracy in clinical radiology practice. However, knowledge gaps and the concerns of health professionals should be addressed by prioritizing education and reinforcing ethical and legal regulations to facilitate the advancement of AI use in radiology. This scoping review provides evidence toward a comprehensive understanding of AI's potential in clinical radiology practice, promoting its use and stimulating further discussion on related challenges and implications.

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“…Nevertheless, a basic theoretical understanding of AI can be achieved based on the capabilities of a given AI system: [ 55 ] Narrow AI is the only form of AI that is currently implemented and applied in several disciplines within and outside of the medical domain. Designed to perform a specific task, narrow AI systems have consistently shown the ability to augment the performance of human clinicians in those specific domains [ 9 , 54 , 71 , 72 , 76 ]. However, narrow AI systems remain limited to performing an assigned task and are unable to perform well outside of the predefined framework.…”

Section: Key Technical Terms For Getting Started With Ai‐driven Researchmentioning

confidence: 99%

A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction

Zsidai,

Kaarre,

Narup

et al. 2024

J. exp. orthop.

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Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI‐based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self‐supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI‐driven orthopaedic research.Level of EvidenceLevel IV.

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The impact of artificial intelligence on the reading times of radiologists for chest radiographs

Cited by 41 publications

References 27 publications

Artificial intelligence‐based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility

Artificial intelligence‐based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility

Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction

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