Pub Date : 2024-11-01DOI: 10.1016/S1546-1440(24)00824-X
{"title":"Cover","authors":"","doi":"10.1016/S1546-1440(24)00824-X","DOIUrl":"10.1016/S1546-1440(24)00824-X","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 11","pages":"Page OFC"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.02.034
Purpose
A comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)–powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI.
Methods
A calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers.
Results
In the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients.
Conclusions
The authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management–accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.
{"title":"Unlocking the Value: Quantifying the Return on Investment of Hospital Artificial Intelligence","authors":"","doi":"10.1016/j.jacr.2024.02.034","DOIUrl":"10.1016/j.jacr.2024.02.034","url":null,"abstract":"<div><h3>Purpose</h3><div>A comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)–powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI.</div></div><div><h3>Methods</h3><div>A calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers.</div></div><div><h3>Results</h3><div>In the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients.</div></div><div><h3>Conclusions</h3><div>The authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management–accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1677-1685"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.03.017
Objective
Patients who miss screening mammogram appointments without notifying the health care system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience screening mammogram no-shows at a community health center and whether and when the missed examinations are completed.
Methods
We included patients with screening mammogram appointments at a community health center between January 1, 2021, and December 31, 2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancelation, completed), and dates of completed screening mammograms after no-show appointments with ≥1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes.
Results
Of 6,159 patients, 12.1% (743 of 6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all P < .05). Patients with no-shows more often had: primary language other than English (32.0% [238 of 743] versus 26.7% [1,265 of 4,741]), race and ethnicity other than White non-Hispanic (42.3% [314 of 743] versus 33.6% [1,595 of 4,742]), Medicaid or means-tested insurance (62.0% [461 of 743] versus 34.4% [1,629 of 4,742]), and residential ZCTAs with ≥20% poverty (19.5% [145 of 743] versus 14.1% [670 of 4,742]). Independent predictors of no-shows were Black non-Hispanic race and ethnicity (adjusted odds ratio [aOR], 1.52; 95% confidence interval [CI], 1.12-2.07; P = .007), Medicaid or other means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; P < .001), and ZCTAs with ≥20% poverty (aOR, 1.76; 95% CI, 1.14-2.72; P = .011). At 1-year follow-up, 40.6% (302 of 743) of patients with no-shows had not completed screening mammogram.
Discussion
Screening mammogram no-shows is a health equity issue in which socio-economically disadvantaged and racially and ethnically minoritized patients are more likely to experience missed appointments and continued delays in screening mammogram completion.
目标未通知医疗保健系统而错过乳腺 X 线照相筛查预约（未预约）的患者有可能延误治疗。我们调查了社区卫生中心乳腺 X 光筛查缺席患者的社会人口学特征，以及缺席的检查是否和何时完成。方法我们纳入了 2021 年 1 月 1 日至 2021 年 12 月 31 日期间在社区卫生中心预约乳腺 X 光筛查的患者。我们收集了语言、种族、民族、保险类型、居住地邮政编码表区（ZCTA）贫困程度、预约结果（未预约、当天取消预约、已完成预约）以及未预约后完成乳腺 X 光筛查的日期，并进行了≥1 年的随访。结果 在 6,159 名患者中，12.1%（6,159 人中的 743 人）的患者没有预约。未赴约组与已赴约组在语言、种族和民族、保险类型和贫困程度方面存在差异（所有 P 均为 0.05）。未就诊患者的主要语言多为非英语（32.0% [743 人中的 238 人] 与 26.7% [4,741 人中的 1,265 人]），种族和族裔多为非西班牙裔白人（42.3% [743 人中的 314 人] 与 33.6% [4,741 人中的 1,595 人]）。6%[4,742人中的1,595人]）、医疗补助或经济情况调查保险（62.0%[743人中的461人]对34.4%[4,742人中的1,629人]）以及贫困率≥20%的居住区（19.5%[743人中的145人]对14.1%[4,742人中的670人]）。非西班牙裔黑人种族和民族（调整赔率比 [aOR]，1.52；95% 置信区间 [CI]，1.12-2.07；P = .007）、医疗补助计划或其他经济情况调查保险（aOR，2.75；95% 置信区间，2.29-3.30；P <.001）以及贫困率≥20% 的 ZCTAs（aOR，1.76；95% 置信区间，1.14-2.72；P = .011）是不就诊的独立预测因素。讨论乳房 X 光筛查缺席是一个健康公平问题，社会经济状况不佳、种族和民族少数的患者更有可能错过预约，并继续推迟完成乳房 X 光筛查。
{"title":"Missed Screening Mammography Appointments: Patient Sociodemographic Characteristics and Mammography Completion After 1 Year","authors":"","doi":"10.1016/j.jacr.2024.03.017","DOIUrl":"10.1016/j.jacr.2024.03.017","url":null,"abstract":"<div><h3>Objective</h3><div>Patients who miss screening mammogram appointments without notifying the health care system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience screening mammogram no-shows at a community health center and whether and when the missed examinations are completed.</div></div><div><h3>Methods</h3><div>We included patients with screening mammogram appointments at a community health center between January 1, 2021, and December 31, 2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancelation, completed), and dates of completed screening mammograms after no-show appointments with ≥1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes.</div></div><div><h3>Results</h3><div>Of 6,159 patients, 12.1% (743 of 6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all <em>P</em> < .05). Patients with no-shows more often had: primary language other than English (32.0% [238 of 743] versus 26.7% [1,265 of 4,741]), race and ethnicity other than White non-Hispanic (42.3% [314 of 743] versus 33.6% [1,595 of 4,742]), Medicaid or means-tested insurance (62.0% [461 of 743] versus 34.4% [1,629 of 4,742]), and residential ZCTAs with ≥20% poverty (19.5% [145 of 743] versus 14.1% [670 of 4,742]). Independent predictors of no-shows were Black non-Hispanic race and ethnicity (adjusted odds ratio [aOR], 1.52; 95% confidence interval [CI], 1.12-2.07; <em>P</em> = .007), Medicaid or other means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; <em>P</em> < .001), and ZCTAs with ≥20% poverty (aOR, 1.76; 95% CI, 1.14-2.72; <em>P</em> = .011). At 1-year follow-up, 40.6% (302 of 743) of patients with no-shows had not completed screening mammogram.</div></div><div><h3>Discussion</h3><div>Screening mammogram no-shows is a health equity issue in which socio-economically disadvantaged and racially and ethnically minoritized patients are more likely to experience missed appointments and continued delays in screening mammogram completion.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1645-1656"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140757094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.04.004
{"title":"Limitations of the Medical Specialty Preference Inventory (MSPI) for Radiation Oncology","authors":"","doi":"10.1016/j.jacr.2024.04.004","DOIUrl":"10.1016/j.jacr.2024.04.004","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1706-1714"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140759300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.04.029
Objective
To characterize the patient population using weekend and evening appointments for screening mammography versus standard appointment times across four outpatient facilities in our academic health system.
Methods
In this institutional review board–approved retrospective cohort study, there were 203,101 screening mammograms from 67,323 patients who had a screening mammogram performed at outpatient centers at a multisite academic institution from January 1, 2015, to December 31, 2022. Screening appointments were defined as “standard appointment time” (between 8 am and 5 pm on Monday through Friday) or “weekend or evening appointment time” (scheduled after 5 pm on Monday through Friday or at any time on a Saturday or Sunday). Associations between appointment group and patient characteristics were analyzed using univariate and multivariate logistic regression.
Results
Most screening mammograms (n = 185,436, 91.3%) were performed at standard times. The remainder (n = 17,665, 8.7%) were performed during weekends or evenings. As we created additional weekend and evening appointments after the coronavirus disease 2019 pandemic, the annual percentage of all screening mammograms performed on evenings and weekends increased. On multivariate analysis, when compared with standard appointment times, we found that patients who were younger than age 50 (P < .001), a race other than non-Hispanic White (P < .001), non-English speakers (P < .001), and from less advantaged zip codes (P < .03) were more likely to use weekend and evening appointment times compared with those aged 70 and above, non-Hispanic White patients, English speakers, and those from the most advantaged zip codes.
Conclusions
Weekend and evening appointment availability for screening mammograms might improve screening access for all patients, particularly for those younger than age 50, those of races other than non-Hispanic White, and those from less advantaged zip codes.
{"title":"Patient Utilization of Weekend and Evening Appointments for Screening Mammography: An 8-Year Observational Cohort Study","authors":"","doi":"10.1016/j.jacr.2024.04.029","DOIUrl":"10.1016/j.jacr.2024.04.029","url":null,"abstract":"<div><h3>Objective</h3><div>To characterize the patient population using weekend and evening appointments for screening mammography versus standard appointment times across four outpatient facilities in our academic health system.</div></div><div><h3>Methods</h3><div>In this institutional review board–approved retrospective cohort study, there were 203,101 screening mammograms from 67,323 patients who had a screening mammogram performed at outpatient centers at a multisite academic institution from January 1, 2015, to December 31, 2022. Screening appointments were defined as “standard appointment time” (between 8 am and 5 pm on Monday through Friday) or “weekend or evening appointment time” (scheduled after 5 pm on Monday through Friday or at any time on a Saturday or Sunday). Associations between appointment group and patient characteristics were analyzed using univariate and multivariate logistic regression.</div></div><div><h3>Results</h3><div>Most screening mammograms (n = 185,436, 91.3%) were performed at standard times. The remainder (n = 17,665, 8.7%) were performed during weekends or evenings. As we created additional weekend and evening appointments after the coronavirus disease 2019 pandemic, the annual percentage of all screening mammograms performed on evenings and weekends increased. On multivariate analysis, when compared with standard appointment times, we found that patients who were younger than age 50 (<em>P</em> < .001), a race other than non-Hispanic White (<em>P</em> < .001), non-English speakers (<em>P</em> < .001), and from less advantaged zip codes (<em>P</em> < .03) were more likely to use weekend and evening appointment times compared with those aged 70 and above, non-Hispanic White patients, English speakers, and those from the most advantaged zip codes.</div></div><div><h3>Conclusions</h3><div>Weekend and evening appointment availability for screening mammograms might improve screening access for all patients, particularly for those younger than age 50, those of races other than non-Hispanic White, and those from less advantaged zip codes.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1657-1667"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141143599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.03.019
{"title":"Task of Leadership","authors":"","doi":"10.1016/j.jacr.2024.03.019","DOIUrl":"10.1016/j.jacr.2024.03.019","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1546-1547"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.06.009
{"title":"Using Divergent Thinking Processes to Identify Breast Cancer Screening Barriers","authors":"","doi":"10.1016/j.jacr.2024.06.009","DOIUrl":"10.1016/j.jacr.2024.06.009","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1564-1568"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.06.019
Expected to grow at a 5.5% compound annual growth rate and reach a market of $34.6 billion by 2028, the diagnostic radiology market is an innovation powerhouse, in significant part due to artificial intelligence and digital products. Many radiologists, researchers, technologists, and leaders possess the skills to develop cutting-edge innovations to improve patient care. However, invariably funding is needed to bring these innovations to fruition. Here we describe, from the vantage point of a practicing venture partner, the key considerations, criteria, and frameworks used when making decisions of what, when, and who to invest funding in. We also describe the current funding climate for these innovations.
{"title":"Investing in Artificial Intelligence and Digital Health—What Radiology Innovators Need to Know","authors":"","doi":"10.1016/j.jacr.2024.06.019","DOIUrl":"10.1016/j.jacr.2024.06.019","url":null,"abstract":"<div><div>Expected to grow at a 5.5% compound annual growth rate and reach a market of $34.6 billion by 2028, the diagnostic radiology market is an innovation powerhouse, in significant part due to artificial intelligence and digital products. Many radiologists, researchers, technologists, and leaders possess the skills to develop cutting-edge innovations to improve patient care. However, invariably funding is needed to bring these innovations to fruition. Here we describe, from the vantage point of a practicing venture partner, the key considerations, criteria, and frameworks used when making decisions of what, when, and who to invest funding in. We also describe the current funding climate for these innovations.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1595-1600"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.04.030
Introduction
Prostate MRI reports use standardized language to describe risk of clinically significant prostate cancer (csPCa) from “equivocal” (Prostate Imaging Reporting and Data System [PI-RADS] 3), “likely” (PI-RADS 4), to “highly likely” (PI-RADS 5). These terms correspond to risks of 11%, 37%, and 70% according to American Urological Association guidelines, respectively. We assessed how men perceive risk associated with standardized PI-RADS language.
Methodology
We conducted a crowdsourced survey of 1,204 men matching a US prostate cancer demographic. We queried participants’ risk perception associated with standardized PI-RADS language across increasing contexts: words only, PI-RADS sentence, full report, and full report with numeric estimate. Median perceived risk (interquartile range) and absolute under/overestimation compared with American Urological Association standards were reported. Multivariable linear mixed-effects analysis identified factors associated with accuracy of risk perception.
Results
Median perceived risks of csPCa (interquartile range) for the word-only context were “equivocal” 50% (50%-74%), “likely” 75% (68%-85%), and “highly likely” 87% (78%-92%), corresponding to +39%, +38%, and +17% overestimation, respectively. Median perceived risks for the PI-RADS-sentence context were 50% (50%-50%), 75% (68%-81%), and 90% (80%-94%) for PI-RADS 3, 4, and 5, corresponding to +39%, +38%, and +20% overestimation, respectively. Median perceived risks for the full-report context were 50% (35%-70%), 72% (50%-80%), and 84% (54%-91%) for PI-RADS 3, 4, and 5, corresponding to +39%, +35%, and +14% overestimation, respectively. For the full-report-with-numeric-estimate context describing a PI-RADS 4 lesion, median perceived risk was 70% (50%-%80), corresponding to +33% overestimation. Including numeric estimates increased correct perception of risk from 3% to 11% (P < .001), driven by men with higher numeracy (odds ratio 1.24, P = .04).
Conclusion
Men overestimate risk of csPCa associated with standardized PI-RADS language regardless of context, especially for PI-RADS 3 and 4 lesions. Changes to PI-RADS language or data-sharing policies for imaging reports should be considered.
{"title":"Patient Perceptions of Standardized Risk Language Used in ACR Prostate MRI PI-RADS Scores","authors":"","doi":"10.1016/j.jacr.2024.04.030","DOIUrl":"10.1016/j.jacr.2024.04.030","url":null,"abstract":"<div><h3>Introduction</h3><div>Prostate MRI reports use standardized language to describe risk of clinically significant prostate cancer (csPCa) from “equivocal” (Prostate Imaging Reporting and Data System [PI-RADS] 3), “likely” (PI-RADS 4), to “highly likely” (PI-RADS 5). These terms correspond to risks of 11%, 37%, and 70% according to American Urological Association guidelines, respectively. We assessed how men perceive risk associated with standardized PI-RADS language.</div></div><div><h3>Methodology</h3><div>We conducted a crowdsourced survey of 1,204 men matching a US prostate cancer demographic. We queried participants’ risk perception associated with standardized PI-RADS language across increasing contexts: words only, PI-RADS sentence, full report, and full report with numeric estimate. Median perceived risk (interquartile range) and absolute under/overestimation compared with American Urological Association standards were reported. Multivariable linear mixed-effects analysis identified factors associated with accuracy of risk perception.</div></div><div><h3>Results</h3><div>Median perceived risks of csPCa (interquartile range) for the word-only context were “equivocal” 50% (50%-74%), “likely” 75% (68%-85%), and “highly likely” 87% (78%-92%), corresponding to +39%, +38%, and +17% overestimation, respectively. Median perceived risks for the PI-RADS-sentence context were 50% (50%-50%), 75% (68%-81%), and 90% (80%-94%) for PI-RADS 3, 4, and 5, corresponding to +39%, +38%, and +20% overestimation, respectively. Median perceived risks for the full-report context were 50% (35%-70%), 72% (50%-80%), and 84% (54%-91%) for PI-RADS 3, 4, and 5, corresponding to +39%, +35%, and +14% overestimation, respectively. For the full-report-with-numeric-estimate context describing a PI-RADS 4 lesion, median perceived risk was 70% (50%-%80), corresponding to +33% overestimation. Including numeric estimates increased correct perception of risk from 3% to 11% (<em>P</em> < .001), driven by men with higher numeracy (odds ratio 1.24, <em>P</em> = .04).</div></div><div><h3>Conclusion</h3><div>Men overestimate risk of csPCa associated with standardized PI-RADS language regardless of context, especially for PI-RADS 3 and 4 lesions. Changes to PI-RADS language or data-sharing policies for imaging reports should be considered.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1634-1642"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.jacr.2024.07.007
Ali S. Tejani MD , Ronald M. Peshock MD , Karuna M. Raj MD
{"title":"Evolving With Artificial Intelligence: Integrating Artificial Intelligence and Imaging Informatics in a General Residency Curriculum With an Advanced Track","authors":"Ali S. Tejani MD , Ronald M. Peshock MD , Karuna M. Raj MD","doi":"10.1016/j.jacr.2024.07.007","DOIUrl":"10.1016/j.jacr.2024.07.007","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"21 10","pages":"Pages 1608-1612"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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