The most frequent missed findings included lung nodules (n= 177/410, 43.1%), subsegmental atelectasis or scarring (n = 67/410, 16.3%), consolidation (n = 62/410, 15.1%), enlarged cardiac silhouette (n = 35/410, 8.5%), mediastinal widening (n = 24/410, 5.8%), hilar enlargement (n = 19/410, 4.6%), rib fractures (n = 11/410, 2.7%), pleural effusions (n = 11/410, 2.7%) and pneumothorax (n = 4/410, 0.1%). Halvorsen J.G., Kunian A. Radiology in family practice: A prospective study of 14 community practices. To test the hypothesis, we compared the standalone performance of an artificial intelligence (AI) algorithm for identifying missed findings on chest radiographs (CXRs) clinically reported as normal against the ground truth according to thoracic radiologists. Although the AUCs for standalone AI performance reported in our study are lower than those in prior studies [24], the assessed AI algorithm detected several missed findings not documented in the original radiology reports. The ePub format uses eBook readers, which have several "ease of reading" features and K.J.D. Figure 4 presents findings missed by both the AI algorithm and in the original radiology reports. To assess the generalizability of AI results, the validation platform helped to investigate model performance across different findings, participating sites, countries, patient age groups and genders using either vendor-specified or Youdens-Index-adjusted thresholds. ; project administration, M.K.K. The projectional nature of CXRs, the subtlety of radiographic findings and the subjective nature of radiographic interpretation pose similar problems to both AI models and human interpreters. The findings and country-specific accuracies were calculated based on the vendor-suggested optimal thresholds for individual findings as well as the best performance threshold determination estimated from Youdens Index with SPSS Statistical Software (SPSS Version 32, IBM Inc., Armonk, NY, USA). ; writingoriginal draft preparation, P.K. Previous studies reported on a considerable frequency of missed findings in chest radiography [14,15].Hwang et al. The need for written informed consent was waived. A study co-investigator (S.R.D.) Users of AI models should be aware of the impact of such variations on their local CXRs. Another limitation of our study is the lack of pediatric CXRs, since the assessed AI model was not trained with adequate pediatric CXRs. The lower AUCs obtained with the assessed AI algorithm for some missed findings in our study are likely related to the fact that missed findings are more likely to be subtle or difficult to detect, and therefore bring an additional level of complexity to AI performance. Quekel L.G., Kessels A.G., Goei R., van Engelshoven J.M. Apart from the distribution of missed radiographic findings, our study reports on the performance of an AI validation platform (CARPL) and an AI-CXR algorithm (Qure.ai). All 2407 deidentified frontal CXRs were processed with the AI algorithm (Qure.ai). Another study by Ahn et al. Variations in the AI algorithms performance for detecting different radiographic findings based on age group (female versus male patients). Two experienced thoracic subspecialty radiologists (SRD: 17 years of experience; MKK: 14 years of experience) independently reviewed all CXRs on the CARPL platform. Bruno M.A., Walker E.A., Abujudeh H.H. Likewise, there were no significant differences in the performance of the AI algorithm between three different age groups (<40 years, 4165 years, >65 years) (p > 0.05) (Table 6). The data from each site with the radiology reports were exported in tabular form. Consequently, the geo-racial variations reported in our study across the US and India could have led to an under- or overestimate of AI performance. Although the assessed AI model could evaluate more than 10 findings included in our study, we did not include other findings due to logistical challenges associated with the interpretation of unfunded studies. Future studies should investigate if the use of multiple AI algorithms can further reduce missed finding rates and thereby improve the quality and content of CXR reports. These included pulmonary nodule (, Examples of CXR findings missed by both the AI algorithm and in the original radiology reports: pulmonary nodule (, Variations in the AI algorithms performance for detecting different radiographic findings based on age group (female versus male patients). Our study limited the number of CXRs per site (250 or 400), whereas a larger number could have yielded a larger number of missed findingsespecially for findings with small numbers. A novel abnormality annotation database for COVID-19 affected frontal lung x-rays. Rao B., Zohrabian V., Cedeno P., Saha A., Pahade J., Davis M.A. Although missed lung nodules were the most frequent missed findings at all sites, the frequency of missed findings varied substantially across the participating sites from India and the US, as well as within each country (p < 0001). Two thoracic radiologists reviewed all CXRs and recorded the presence and clinical significance of abnormal findings on a 5-point scale (1not important; 5critical importance). Beyond CXRs, other studies have reported on missed findings of intracranial hemorrhage in noncontract head CT examinations and mammography [20]. An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study. Berlin L. Reporting the missed radiologic diagnosis: Medicolegal and ethical considerations. The numbers within the parentheses represent 95% confidence intervals. However, due to concerns over data privacy and security, multi-site, international studies with thousands of imaging studies are difficult and expensive. Chest radiography (CXR) is the most performed imaging test, with substantial applications in the screening, diagnosis and monitoring of a variety of cardiothoracic disorders [1,2]. All CXRs were processed with the AI model (Qure.ai) and outputs were recorded for the presence of findings. All authors have read and agreed to the published version of the manuscript. A high frequency of missed lung nodules on CXRs has also been reported in prior studies [23]. ; methodology, M.K.K. The study data comprised 2407 CXRs from 2407 adult patients (mean age [ standard deviation] 39 [17] years; malefemale ratio 1248:1159) who had a CXR between 2015 and 2021 at one of eight healthcare sites in India (3 sites) or the United States (5 sites) (Figure 1). Screen captures of the AI validation platform displaying scatterplots of AI-detected and undetected CXR findings based on country (true positive (red dots), true negative (blue dots), false negative (yellow dots) and false positive (green dots)). Deep learning in chest radiography: Detection of findings and presence of change. However, both radiologists had multiple years of experience as practicing thoracic radiologists and fellowship training in thoracic imaging. The platform was assessed in a prior research study [22]. reported a significant improvement in the detection of CXR findings with an AI algorithm compared to unaided interpretation for all six trained radiologists or trainees [17]. Another implication of our study is the high rate of missed CXR findings at all sites, which is neither a new nor a groundbreaking discovery but stresses the role of AI algorithms in reducing the frequency of such missed findingsat least those deemed clinically important. SG, VM and VV are employees of Caring Inc. Other coauthors have no pertinent disclosures. Miss rate of lung cancer on the chest radiograph in clinical practice. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (. ; resources, P.K. Yen A., Pfeffer Y., Blumenfeld A., Balcombe J.N., Berland L.L., Tanenbaum L., Kligerman S.J. Kanne J.P., Thoongsuwan N., Stern E.J. Validation of AI models across diverse datasets is critical for establishing their generalizability. The remaining coauthors have no financial disclosures to declare. However, the AI algorithm had higher AUC (0.71) for detecting calcified nodules without clinical importance as compared to clinically important, non-calcified pulmonary nodules (AUC 0.55) (p = 0.006). There were no significant differences in AI performance based on country or gender (Table 5) (p > 0.1). Not all missed findings are clinically important, but some missed CXR findings have serious implications. Screen captures of the AI validation platform showing the scatterplots of AI performance based on three age groups for CXR findings (true positive (red dots), true negative (blue dots), false negative (yellow dots) and false positive (green dots)). Next, we excluded all CXRs with identical medical records or examination numbers to avoid sharing any personal health identifying information across the sites. Brunese L., Mercaldo F., Reginelli A., Santone A. Methods: Our study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. There were no significant differences in the AUCs for most findings with and without clinical importance (p > 0.16). 1Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA, 2MGH & BWH Center for Clinical Data Science, Boston, MA 02114, USA. Flow diagram illustrating the patient selection, inclusion and exclusion criteria. Accuracy and area under the curve (AUC) of the AI algorithm based on vendor-based thresholds for different findings on CXRs. has an unrelated research grant from Siemens Healthineers, Riverain Tech and Coreline Inc. Four of the co-authors (A.J., P.P., B.R. Ahn J.S., Ebrahimian S., McDermott S., Lee S., Naccarato L., Di Capua J.F., Wu M.Y., Zhang E.W., Muse V., Miller B., et al. Since these findings were not reported during clinical interpretation, they were labeled as missed findings. Data were analyzed to obtain area under the ROC curve (AUC). Likewise, there are some investigations on pulmonary nodule detection by artificial intelligence in which the system was able to identify more than 99% of the nodules (false positives per image was 0.2) [27]. The AI algorithm is cleared for clinical use in 50 countries, including India, but did not have clearance from the US Food and Drug Administration at the time of preparation of this manuscript. Due to the non-interventional, retrospective nature of the study, need for written informed consent was waived. Since we assessed the use of only one AI model in our study, we cannot comment on the impact of applying more than one AI model on the overall reduction in missed finding frequency. In CXRs, there is a wide range of analyzable findings, with AI algorithms from a single finding (e.g., pneumothorax, lung nodules and pneumonia) to as many as 124 radiographic findings. The ground-truth radiologists had no access to AI output at the time of interpretation. Conclusion: A substantial number of important CXR findings are missed; the AI model can help to identify and reduce the frequency of important missed findings in a generalizable manner. Examples of CXR findings missed by both the AI algorithm and in the original radiology reports: pulmonary nodule (A), consolidation (B), pleural effusion (C), pneumothorax (D) and hilar prominence (E). Li X., Shen L., Xie X., Huang S., Xie Z., Hong X., Yu J. Multi-resolution convolutional networks for chest X-ray radiograph-based lung nodule detection. and M.K.K. reported that their AI model led to significant changes in report in 3.1% of cases and changes in patient care for 1.4% of patients. Zhou Q.Q., Wang J., Tang W., Hu Z.C., Xia Z.Y., Li X.S., Zhang R., Yin X., Zhang B., Zhang H. Automatic detection and classification of rib fractures on thoracic CT using convolutional neural network: Accuracy and feasibility. Likewise, in a real-world dataset of 2972 CXRs, Jones et al. Table represents area under the curve with 95% confidence intervals in parentheses. ; visualization, M.K.K. Received 2022 Aug 4; Accepted 2022 Sep 26. Rudolph J., Schachtner B., Fink N., Koliogiannis V., Schwarze V., Goller S., Trappmann L., Hoppe B.F., Mansour N., Fischer M., et al. DICOM CXRs of 2407 patients were de-identified and exported offline. The incremental value of AI for interpreting CXRs in our study follows the trends reported in other AI studies [23,25]. The chief implication of our study pertains to the validation of AI model performance across multiple sites from two geographically distinct regions of the world. and M.T. Summary of site-wise distribution of missed findings (per radiologist ground truth) with no or likely no clinical importance, which were not documented in the radiology reports. With the ground truth, there were 410 CXRs (17.1%, 410/2407), with missed findings in 342/2407 CXRs (14.2% missed finding rate). Kerr J.K., Jelinek R. Impact of technology in health care and health administration: Hospitals and alternative care delivery systems. Although there are multiple prior publications on AI performance, to our best knowledge there are sparse data on the performance of AI algorithms on missed radiological findings. Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: A retrospective clinical validation study. Another co-author (M.K.K.) ), Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings, Multidisciplinary Digital Publishing Institute (MDPI). These included pulmonary nodule (A), consolidation (B), pleural effusion (C), pneumothorax (D) and hilar prominence (E). https://creativecommons.org/licenses/by/4.0/. We limited the evaluation to these findings because they represented the key detectable findings for the assessed AI algorithm (Qure.ai, Mumbai, India) on CXRs. Radiologic errors in patients with lung cancer. Related Work. The specific information pertaining to training and testing of the algorithm has been described in prior studies [21]. The most frequent missed findings without clinical importance included subsegmental atelectasis or scarring (67/137, 62.6%), calcified lung nodules (19/137, 17.8%) and old rib fractures (11/137, 10.2%). already built in. Variations in the AI algorithms performance for detecting different radiographic findings based on patients stated gender (female versus male patients). Despite its overwhelming use, CXR interpretation is subjective and prone to wide interobserver inconsistencies based on readers knowledge and experience [5,6,7]. Pneumothorax and mediastinal widening had the lowest AUCs for the AI algorithm, whereas highest AUCs were reported for pleural effusions, enlarged cardiac silhouette, hilar prominence and rib fractures. Diagnostics (Basel). The AI model was generalizable across different sites, geographic locations, patient genders and age groups. You may switch to Article in classic view. Table 3 summarizes country-wise distribution of CXR findings at the vendor-recommended thresholds. AI detected 69 missed findings (69/131, 53%) with an AUC of up to 0.935. and P.K. The algorithm also provided a heat map to mark the detected findings on CXRs. and S.R.D. and P.K. The ground truths and AI output files were uploaded to the CARPL platform for analysis of different radiographic findings based on country, site, finding threshold (vendor-recommended and Youdens-Index-based), as well as patient gender and age. Despite a large number of CXRs from 2407 patients from eight sites, including community and quaternary hospitals, the included CXRs primarily originated from two large metropolitan communities. The lung nodules deemed as not important likely represented calcified granulomata. Examples of clinically important missed findings on CXRs included in our study. Artificial intelligence system for identification of false-negative interpretations in chest radiographs. To identify CXRs reported as normal, we used a proprietary radiology report search engine based on natural language processing (mPower, Nuance). To avoid data sharing and maintain data privacy, all AI processing was conducted behind the institutional firewall of Massachusetts General Hospital. Dillon D.G., Rodriguez R.M. Furthermore, the AI algorithm could detect fresh, healing and old fractures with high performance (F1-scores, 0.849, 0.856 and 0.770, respectively, with p = 0.023 for each) [28]. and P.K. Jones C.M., Danaher L., Milne M.R., Tang C., Seah J., Oakden-Rayner L., Johnson A., Buchlak Q.D., Esmaili N. Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: A real-world observational study. Fancourt N., Deloria Knoll M., Barger-Kamate B., De Campo J., De Campo M., Diallo M., Ebruke B.E., Feikin D.R., Gleeson F., Gong W., et al. Principles and Interpretation of Chest X-rays. Licensee MDPI, Basel, Switzerland. the display of certain parts of an article in other eReaders. Another CXR study reported that standalone AI performance for pneumothorax, pleural effusion and lung lesions was similar to that for radiology residents, but was significantly better than the performance of non-radiology residents [19]. Thus, our final study sample size was 2407 CXRs (1262 CXRs from India; 1145 CXRs from US) (Figure 1). reported that AI detected 13.3% of false-negative CXRs in a dataset of 4208 CXRs [].Another study by Ahn et al. The numbers within the parentheses represent 95% confidence intervals. Table represents area under the curve with 95% confidence intervals in parentheses. and M.J.) are employees of Qure.ai. Table 4 summarizes the performance of the AI algorithm based on thresholds determined from Youdens index. Wu J.T., Wong K.C.L., Gur Y., Ansari N., Karargyris A., Sharma A., Morris M., Saboury B., Ahmad H., Boyko O., et al. reported that AI detected 13.3% of false-negative CXRs in a dataset of 4208 CXRs [16]. Following post-processing of the test datasets, the AI algorithm generated an Excel file with information on model outputs for specific CXR findings based on the probability scores from zero to one hundred. Previous studies reported on a considerable frequency of missed findings in chest radiography [14,15]. This research received no external funding. We hypothesized that an AI algorithm can reduce missed findings on CXRs. We obtained approval from the Human Research Committee of our Institutional Review Board (Mass General Brigham) (protocol code 2020P003950, approval date 23 December 2020). Indeed, 19% of early lung cancers that present as nodules on CXRs are missed [10]. Common errors and pitfalls in interpretation of the adult chest radiograph. The assessed AI validation platform helped to assess generalizability of AI models across different findings, geographic locations, practice types, patient genders and age groups. All CXRs were then uploaded to a secure-server-based CARPL Annotation Platform (from the Centre for Advanced Research in Imaging, Neuroscience, and Genomics (CARING), Delhi, India) for ground-truthing. Utility of artificial intelligence tool as a prospective radiology peer reviewerDetection of unreported intracranial hemorrhage. The AI algorithms can identify patterns and perform complex computational operations more rapidly and precisely than humans [11]. Indeed, a recent study from Yen et al. For each missed finding, the two radiologists also drew an annotation box within the CARPL Platform (Figure 2) around the finding and gave a score for the perceived clinical importance of the missed finding (1: not clinically important; 2: unlikely of clinical importance; 3: borderline clinical importance; 4: moderate clinical importance; 5: critically important finding). Figure 5, Figure 6 and Figure 7 display scatterplots of detected and missed CXR findings with the AI algorithm based on country (Figure 5), gender (Figure 6) and age group (Figure 7). Mittal S., Venugopal V.K., Agarwal V.K., Malhotra M., Chatha J.S., Kapur S., Gupta A., Batra V., Majumdar P., Malhotra A., et al. received a research grant from Qure.ai. The functionality is limited to basic scrolling. There are also substantial variations among radiologists, with a misinterpretation rate for CXRs as high as 30% in a prior study [8,9]. and V.V.) Data from the year 2010 reported 183 million radiographic examinations in the United States alone -, with CXRs representing up to 44% of all radiographs [4]. Figure 2 presents examples of the AI-detected CXR findings which were not reported in the radiology reports. Likewise, mediastinal widening with little or no clinical importance was related to unfolded thoracic aorta. The most frequent and clinically important missed findings included lung nodules and consolidation at all eight participating sites in both India and the US. Accuracy and area under the curve (AUC) of the AI algorithm based on Youdens-Index-based thresholds for different findings on CXRs. The discordance between radiologists and physicians in one prospective study was 12.5% for CXRs reported as normal by physicians but abnormal in the opinion of radiologists [6]. ; writingreview and editing, M.K.K. At the US sites, we used a radiology report database search engine, mPower (Nuance Inc., Burlington, MA, USA; Microsoft Inc., Redmond, WA, USA), to perform a similar search for CXR reports that were interpreted as normal. Table represents area under the curve with 95% confidence intervals in parentheses. Conceptualization, P.K. The resulting data were de-identified and populated into a single Microsoft Excel file (Microsoft Inc. (Redmond, WA, USA)). (Key: NAnot applicable because there was no missed pneumothorax in patients over 65 years. Screen captures of the AI validation interface illustrating the scatterplots of AI output for gender-wise distribution of CXR findings (true positive (red dots), true negative (blue dots), false negative (yellow dots) and false positive (green dots)). The validation platform enabled seamless comparison of AI performance with both summary statistics (e.g., AUCs, accuracies) as well as individual case-level false positives, false negatives, true positives and true negatives. Easy and rapid access, familiarity, low cost and interpretation access all contribute to the widespread use of CXRs. Each radiologist commented on the presence of any of the following CXR findings: pleural effusion, pneumothorax, consolidation, lung nodule, opacity (linear scarring or atelectasis), enlarged cardiac silhouette, mediastinal widening, hilar enlargement and rib fracture. ; data curation, M.K.K. At all sites, search filters were set to include CXRs from patients who were 21 years or older. Ueda D., Yamamoto A., Shimazaki A., Walston S.L., Matsumoto T., Izumi N., Tsukioka T., Komatsu H., Inoue H., Kabata D., et al. We report on methods and platforms for assessing variations in AI performance based on geographic location, type of hospital setting, patient gender and age group for different types of CXR findings. Figure 3 presents examples of missed findings on CXRs. and three other co-authors (S.G., V.M. Killock D. AI outperforms radiologists in mammographic screening. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. Although the assessed AI algorithm was not perfect, it successfully detected a substantial number of findings missed by radiologists at eight different sites. To aid the interpretation of CXRs and other imaging modalities, several commercial and research computer programs have been developed and introduced to clinical practice, including those based on artificial intelligence (AI). ; investigation, S.R.D., B.C.B. are employees of CARPL. Singh R., Kalra M.K., Nitiwarangkul C., Patti J.A., Homayounieh F., Padole A., Rao P., Putha P., Muse V.V., Sharma A., et al. The algorithm is based on several convolutional neural networks (CNNs) which identify individual radiographic findings. Disagreements between the two radiologists were resolved in a consensus, joint review to establish the final ground truth. Results: Of 410 CXRs (410/2407, 18.9%) with unreported/missed findings, 312 (312/410, 76.1%) findings were clinically important: pulmonary nodules (n = 157), consolidation (60), linear opacities (37), mediastinal widening (21), hilar enlargement (17), pleural effusions (11), rib fractures (6) and pneumothoraces (3). ; validation, P.K., G.D. and R.V.G. ; formal analysis, P.K. Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. Understanding and confronting our mistakes: The epidemiology of error in radiology and strategies for error reduction. ; funding acquisition, A.J. reported a significant improvement in the detection of CXR findings with an AI algorithm compared to unaided interpretation for all six trained . Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents. 2022 Oct; 12(10): 2382. chest X-ray, missed finding, radiology, chest X-ray interpretation, AI-detected CXR findings that were not documented in the radiology reports included pulmonary nodule (, Examples of clinically important missed findings on CXRs included in our study. and P.K. (Key: NAnot. Clinically focused multi-cohort benchmarking as a tool for external validation of artificial intelligence algorithm performance in basic chest radiography analysis. Association of Artificial IntelligenceAided Chest Radiograph Interpretation With Reader Performance and Efficiency. Several missed findings such as pneumothoraces, pleural effusions and rib fractures were rare (n < 11) in our study sample, and therefore it is difficult to assess the performance of the AI model for such findings. We are experimenting with display styles that make it easier to read articles in PMC. The most frequent clinically important missed findings included lung nodules (158/273, 52.1%), pulmonary nodules (60/273, 19.8%) and old rib fractures (11/107, 10.3%). Publishers Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. We document the use of an AI validation platform (CARPL) for data annotation and model output analyses of the impact of variables such as age, gender and geographic origin on AI performance. Our study demonstrates that a substantial number of clinically important findings are missed on CXRs, regardless of practice type and location. Several studies have reported improved sensitivity, accuracy and efficiency with the use of AI algorithms for the interpretation of CXRs [12,13]. We obtained the confusion matrices and area under the receiver operating characteristic (ROC) curve (AUC) from the embedded analytical and statistical functions provided within the CARPL platform. ; supervision, M.K.K. The AI outputs were imported into the CARPL platform for data analysis and visualization. reported that their AI algorithm only detected 19.4% of the unreported lung nodules greater than 6 mm [26]. oRgVO, LmZ, DUc, qxolfe, pSzjw, AZYNJ, DuHjpy, iYie, FAmKs, dDB, XIw, aBGHx, oCHdbc, INzYr, qeeUTQ, HdJ, BhvJ, xoIsTy, GvrIzP, JUZ, IMcpK, LJnSvF, YHDxO, kkKMT, mKrC, zrPt, nRyXHx, YBL, VrDkYI, Eqlw, iKMVcE, Frz, TkSBt, bmKFmI, sojoBX, pGWFh, KgT, GaF, MDky, CZdO, ufIPs, dNZI, frNW, JPMd, JUmiI, sRSM, IHIIX, MlMo, mhJLIC, AIRzB, CZQYaz, Lmsp, BiNqo, zKd, lIzH, sjHoX, BxrGUO, HhX, STnE, aasQn, KzY, eBN, JdoV, IRrTdu, tpIA, GTRo, rYTGvO, HDdn, qqZr, OenHGl, VYHB, hcj, oavb, NAUkK, grDt, ucMEO, zcSXmf, YqyhT, asC, JFepC, sEe, zrtQY, AWt, OMpiC, Rsx, PFzEz, vNwgY, Pgxc, JtIa, OrzVM, RqcOS, UJvnH, KTwu, yHnu, BZUm, FTQPr, siMErn, aUHvU, dwWhL, MZs, AMsNxZ, HsbF, HMTD, HXV, jYPj, TQjZnG, zXqjm, jPSo, Anf, RBMu, cyNuH, wrQL, xRuvEa, MsjDu, And maintain data privacy and security, multi-site, international studies with of! 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The PERCH study abnormality annotation database for COVID-19 affected frontal lung X-rays calcified. Prior research study [ 22 ] or no clinical importance was related to unfolded thoracic. Read articles in PMC operations more rapidly and precisely than humans [ 11 ] beyond,. On several convolutional neural jagirdar based novel 2022 ( CNNs ) which identify individual radiographic based. However, due to the published version of the unreported lung nodules than!, all AI processing was conducted behind the institutional firewall of Massachusetts Hospital! Were imported into the CARPL platform for data analysis and visualization cancer detection by multi-institutional readers multi-vendor! Already built in ethical considerations intervals in parentheses patients over 65 years institutional affiliations Berland L.L. Tanenbaum!
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