Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort

Yoo, Hyunsuk; Kim, Eun Young; Kim, Hyungjin; Choi, Ye Ra; Kim, Moon Young; Hwang, Sung Ho; Kim, Young Joong; Cho, Young Jun; Jin, Kwang Nam

doi:10.3348/kjr.2022.0189

Cited by 12 publications

(5 citation statements)

References 27 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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“…Common DL models include segmentation and detection algorithms for lung nodules, masses, consolidations, pneumothorax etc. [ 2 3 4 ]. However, CRs may contain prognostic information beyond the traditional diagnostic findings, and DL models can effectively quantify this prognostic signature.…”

Section: External Validation Of An Open-source DL Model For Crsmentioning

confidence: 99%

Performing a Research Study Using Open-Source Deep Learning Models

Kim

2024

Korean J Radiol

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“…Most studies have targeted radiologists in related fields rather than non-radiologists; however, radiologists do not interact with patients directly or make decisions regarding care. We believe that DeepCADCR can improve patient treatment and prognosis [23,24]. We implemented DeepCADCR into clinicians' workflows and found that EPs made most use of the system when they had to make a diagnosis or develop a treatment plan.…”

Section: Ta B L E 4 Perceptions Of Deepcadcr Use In the Futurementioning

confidence: 99%

Effect of an artificial-intelligent chest radiographs reporting system in an emergency department

2023

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Though chest radiography is a first-line diagnostic tool in the emergency department (ED), interpretation has a high error rate. We aimed to evaluate the usability and acceptability of deep learning-based computer-aided detection for chest radiography (DeepCADCR) in an ED environment. We conducted a single-institution survey of emergency physicians (EPs) who had used DeepCADCR (Lunit INSIGHT Chest Xray (CXR), version 3.1.4.1) as part of their ED workflow for at least three months. We developed 22 questions that assessed the subscales of effectiveness, efficiency, safety, satisfaction, and reliability. A seven-point Likert agreement scale was used to rate the responses. A total of 23 EPs who completed the survey was enrolled in the study. When averaged by subscale, satisfaction scores were highest (mean 4.71, standard deviation (SD) 1.43), and safety scores were lowest (mean 4.3, SD 0.72). When scores were converted to acceptability, the total average acceptance of DeepCADCR was 86.0%, with higher scores in ED residents than ED specialists for all subscales. Use of DeepCADCR in the ED workflow was well accepted by EPs.

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Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort

Cited by 12 publications

References 27 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

Performing a Research Study Using Open-Source Deep Learning Models

Effect of an artificial-intelligent chest radiographs reporting system in an emergency department

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