AI Improves Nodule Detection on Chest Radiographs in a Health                     Screening Population: A Randomized Controlled Trial

Nam, Ju Gang; Hwang, Eui Jin; Kim, Jayoun; Park, Nanhee; Lee, Eun Hee; Kim, Hyun Jin; Nam, Mi-Yeon; Lee, Jong Hyuk; Park, Chang Min; Goo, Jin Mo

doi:10.1148/radiol.221894

Cited by 33 publications

(16 citation statements)

References 26 publications

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“…The supplementary literature search identified 48 eligible studies ( Fig. 1 ) [ 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 ]. Table 6 shows the count of studies that addressed the four value elements provided by AI.…”

Section: Resultsmentioning

confidence: 99%

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Jhang,

Park,

Sul

et al. 2024

Korean J Radiol

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Objective This study aims to explore the opinions on the insurance coverage of artificial intelligence (AI), as categorized based on the distinct value elements offered by AI, with a specific focus on patient-centered outcomes (PCOs). PCOs are distinguished from traditional clinical outcomes and focus on patient-reported experiences and values such as quality of life, functionality, well-being, physical or emotional status, and convenience. Materials and Methods We classified the value elements provided by AI into four dimensions: clinical outcomes, economic aspects, organizational aspects, and non-clinical PCOs. The survey comprised three sections: 1) experiences with PCOs in evaluating AI, 2) opinions on the coverage of AI by the National Health Insurance of the Republic of Korea when AI demonstrated benefits across the four value elements, and 3) respondent characteristics. The opinions regarding AI insurance coverage were assessed dichotomously and semi-quantitatively: non-approval (0) vs. approval (on a 1–10 weight scale, with 10 indicating the strongest approval). The survey was conducted from July 4 to 26, 2023, using a web-based method. Responses to PCOs and other value elements were compared. Results Among 200 respondents, 44 (22%) were patients/patient representatives, 64 (32%) were industry/developers, 60 (30%) were medical practitioners/doctors, and 32 (16%) were government health personnel. The level of experience with PCOs regarding AI was low, with only 7% (14/200) having direct experience and 10% (20/200) having any experience (either direct or indirect). The approval rate for insurance coverage for PCOs was 74% (148/200), significantly lower than the corresponding rates for other value elements (82.5%–93.5%; P ≤ 0.034). The approval strength was significantly lower for PCOs, with a mean weight ± standard deviation of 5.1 ± 3.5, compared to other value elements ( P ≤ 0.036). Conclusion There is currently limited demand for insurance coverage for AI that demonstrates benefits in terms of non-clinical PCOs.

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Section: Resultsmentioning

confidence: 99%

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Jhang,

Park,

Sul

et al. 2024

Korean J Radiol

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“…We found that the detection sensitivity of the CAD system was higher than that of the radiologists, with no marked increase in the false-positive rate. CAD nodule detection techniques have experienced rapid development since the development of neural network–based systems ( 25 , 26 ). Zhang et al reported a study using a deep-learning algorithm to detect and classify lung nodules in 50 CT scans.…”

Section: Discussionmentioning

confidence: 99%

Application value of a computer-aided diagnosis and management system for the detection of lung nodules

Chen,

Cao,

Jiao

et al. 2023

Quant Imaging Med Surg

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Background Computer-aided diagnosis (CAD) systems can help reduce radiologists’ workload. This study assessed the value of a CAD system for the detection of lung nodules on chest computed tomography (CT) images. Methods The study retrospectively analyzed the CT images of patients who underwent routine health checkups between August 2019 and November 2019 at 3 hospitals in China. All images were first assessed by 2 radiologists manually in a blinded manner, which was followed by assessment with the CAD system. The location and classification of the lung nodules were determined. The final diagnosis was made by a panel of experts, including 2 associate chief radiologists and 1 chief radiologist at the radiology department. The sensitivity for nodule detection and false-positive nodules per case were calculated. Results A total of 1,002 CT images were included in the study, and the process was completed for 999 images. The sensitivity of the CAD system and manual detection was 90.19% and 49.88% (P<0.001), respectively. Similar sensitivity was observed between manual detection and the CAD system in lung nodules >15 mm (P=0.08). The false-positive nodules per case for the CAD system were 0.30±0.84 and those for manual detection were 0.24±0.68 (P=0.12). The sensitivity of the CAD system was higher than that of the radiologists, but the increase in the false-positive rate was only slight. Conclusions In addition to reducing the workload for medical professionals, a CAD system developed using a deep-learning model was highly effective and accurate in detecting lung nodules and did not demonstrate a meaningfully higher the false-positive rate.

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“…For instance, image reconstruction and analysis in radiology has undergone substantial improvement due to the integration of deep neural networks [13]. AI’s assistance to healthcare professionals through computer-aided detection and diagnosis has shown to have the potential to improve efficiency and accuracy [14, 15]. Furthermore, algorithms that identify areas of interest during image screening have proven effective by supporting clinicians, thus enhancing the diagnostic process without supplanting human expertise [14].…”

Section: Ai Deployment In Healthcarementioning

confidence: 99%

From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven healthcare research for low-income settings

Krones,

Walker

2023

Preprint

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This paper critically explores the opportunities and challenges of deploying Artificial Intelligence (AI) in healthcare. This study has two parallel components: (1) A narrative literature summary, which assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- and low-income countries. Despite the development of machine learning models for a wide range of diseases, many are never deployed in practice. We highlight various challenges that contribute to the lack of deployed models. A main challenge that is not always sufficiently addressed in the literature is the evaluation of model generalisation. For example, by using a multi-site set-up with test sets that were collected separately to the train and validation sets, or by using evaluation metrics which are both understandable and clinically applicable. Moreover, we discuss how the emerging trend of human-centred deployment research is a promising avenue for overcoming barriers towards deployment. (2) A case study on developing and evaluating a predictive AI model tailored for low-income environments. The focus of this case study is heart murmur detection in rural Brazil. Our Binary Bayesian ResNet model leverages overlapping log mel spectrograms of patient heart sound recordings and integrates demographic data and signal features via XGBoost to optimise performance. We discuss the model’s limitations, its robustness, and the obstacles preventing its practical application. We especially highlight how our model, and other state-of-the-art models, struggle to generalise to out-of-distribution data. The research accentuates the transformative potential of AI-enabled healthcare, particularly affordable point-of-care monitoring systems, in low-income settings. It also emphasises the necessity for effective implementation and integration strategies to guarantee the successful deployment of these technologies.

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AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial

Cited by 33 publications

References 26 publications

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Application value of a computer-aided diagnosis and management system for the detection of lung nodules

From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven healthcare research for low-income settings

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