Journal of the American College of Radiology最新文献

{"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":null,"pages":null},"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}

{"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> &lt; .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":null,"pages":null},"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}

{"title":"Building a Career in Radiology Innovation: A Primer for Trainees and First-Time Innovators to Act on Opportunities","authors":"","doi":"10.1016/j.jacr.2024.06.010","DOIUrl":"10.1016/j.jacr.2024.06.010","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141473281","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}

{"title":"Evolving With Artificial Intelligence: Integrating Artificial Intelligence and Imaging Informatics in a General Residency Curriculum With an Advanced Track","authors":"","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":null,"pages":null},"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}

{"title":"Medical School House Rock: Randomized Trial","authors":"","doi":"10.1016/j.jacr.2024.04.001","DOIUrl":"10.1016/j.jacr.2024.04.001","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783021","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}

{"title":"Analysis of National Resident Matching Program for Radiology Fellowships: Factors Affecting Program Fill Rates","authors":"","doi":"10.1016/j.jacr.2024.04.011","DOIUrl":"10.1016/j.jacr.2024.04.011","url":null,"abstract":"<div><h3>Purpose</h3><div>The National Resident Matching Program (NRMP) is used by an increasing number of diagnostic radiology (DR) residents applying to subspecialty fellowships. Data characterizing match outcomes on the basis of program characteristics are limited. The aim of this study was to determine if fellowship or residency size, location, or perceived reputation was related with a program filling its quota.</div></div><div><h3>Methods</h3><div><span><span><span><span>Using public NRMP data from 2004 to 2022, DR residency, breast imaging (BI), </span>musculoskeletal imaging<span> (MSK), interventional radiology (IR), and </span></span>neuroradiology (NR) fellowship programs were characterized by </span>geography, DR and fellowship quota, applicants per position (A/P), and reputation as determined by being an Aunt Minnie best DR program semifinalist, Doximity 2021-2022 top 25 program, or </span><span><em>U.S.</em><em> News &amp; World Report</em></span> top 20 hospital. The DR program’s reputation was substituted for fellowships at the same institution. A program was considered filled if it met its quota.</div></div><div><h3>Results</h3><div>The 2022 A/P ratios were 1.02 for IR, 0.83 for BI, 0.75 for MSK, and 0.88 for NR. IR was excluded from additional analysis because its A/P was &gt;1. The combined BI, MSK, and NR fellowships filled 78% of positions (529 of 679) and 56% of programs (132 of 234). Factors associated with higher program filling included Doximity top 25 program, Aunt Minnie semifinalist, and <em>U.S. News &amp; World Report</em> top 20 hospital affiliation (<em>P</em> &lt; .001 for all); DR residency quota greater than 9, and fellowship quota of three or more (<em>P</em> &lt; .01). The Ohio Valley (Ohio, western Pennsylvania, West Virginia, and Kentucky) filled the lowest, at 39% of programs (<em>P</em> = .06).</div></div><div><h3>Conclusions</h3><div>Larger fellowship programs with higher perceived reputations and larger underlying DR residency programs were significantly more likely to fill their NRMP quota.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140892995","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}

{"title":"Building Bridges: Future-Proofing Established Industries and Building Relationships with the Black Community","authors":"","doi":"10.1016/j.jacr.2023.08.039","DOIUrl":"10.1016/j.jacr.2023.08.039","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41161777","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}

{"title":"Addressing Mental Health in Professional Management","authors":"","doi":"10.1016/j.jacr.2023.08.040","DOIUrl":"10.1016/j.jacr.2023.08.040","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41154013","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}

{"title":"Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation","authors":"","doi":"10.1016/j.jacr.2024.04.027","DOIUrl":"10.1016/j.jacr.2024.04.027","url":null,"abstract":"<div><div>With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are pivotal to inspecting the accuracy and generalizability of these deep learning algorithms to ensure both patient safety and health equity. Protected health information concerns, intellectual property rights, and diverse requirements of models impede the development of rigorous external validation infrastructures. The authors propose various suggestions for addressing the challenges associated with the development of efficient, customizable, and cost-effective infrastructures for the external validation of AI models at large medical centers and institutions. The authors present comprehensive steps to establish an AI inferencing infrastructure outside clinical systems to examine the local performance of AI algorithms before health practice or systemwide implementation and promote an evidence-based approach for adopting AI models that can enhance radiology workflows and improve patient outcomes.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141094743","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}

{"title":"Assessing Laterality Errors in Radiology: Comparing Generative Artificial Intelligence and Natural Language Processing","authors":"","doi":"10.1016/j.jacr.2024.06.014","DOIUrl":"10.1016/j.jacr.2024.06.014","url":null,"abstract":"<div><h3>Purpose</h3><div>We compared the performance of generative artificial intelligence (AI) (Augmented Transformer Assisted Radiology Intelligence [ATARI, Microsoft Nuance, Microsoft Corporation, Redmond, Washington]) and natural language processing (NLP) tools for identifying laterality errors in radiology reports and images.</div></div><div><h3>Methods</h3><div>We used an NLP-based (mPower, Microsoft Nuance) tool to identify radiology reports flagged for laterality errors in its Quality Assurance Dashboard. The NLP model detects and highlights laterality mismatches in radiology reports. From an initial pool of 1,124 radiology reports flagged by the NLP for laterality errors, we selected and evaluated 898 reports that encompassed radiography, CT, MRI, and ultrasound modalities to ensure comprehensive coverage. A radiologist reviewed each radiology report to assess if the flagged laterality errors were present (reporting error—true-positive) or absent (NLP error—false-positive). Next, we applied ATARI to 237 radiology reports and images with consecutive NLP true-positive (118 reports) and false-positive (119 reports) laterality errors. We estimated accuracy of NLP and generative AI tools to identify overall and modality-wise laterality errors.</div></div><div><h3>Results</h3><div>Among the 898 NLP-flagged laterality errors, 64% (574 of 898) had NLP errors and 36% (324 of 898) were reporting errors. The text query ATARI feature correctly identified the absence of laterality mismatch (NLP false-positives) with a 97.4% accuracy (115 of 118 reports; 95% confidence interval [CI] = 96.5%-98.3%). Combined vision and text query resulted in 98.3% accuracy (116 of 118 reports or images; 95% CI = 97.6%-99.0%), and query alone had a 98.3% accuracy (116 of 118 images; 95% CI = 97.6%-99.0%).</div></div><div><h3>Conclusion</h3><div>The generative AI-empowered ATARI prototype outperformed the assessed NLP tool for determining true and false laterality errors in radiology reports while enabling an image-based laterality determination. Underlying errors in ATARI text query in complex radiology reports emphasize the need for further improvement in the technology.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499751","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}