The DOI matches its Crossref record; 65 of the 68 references it cites were found in a registry or an index; 2 of the 40 sources its claims cite were checked for support.
Federated learning for predicting clinical outcomes in patients with COVID-19
Open accessPublished version of a preprint
Abstract
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.
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Claims the paper makes
27 Own result · 22 Own interpretation · 20 Not checked yet · 14 Own method · 2 No basis given · 1 Partly supported · accuracy not yet measured
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In the paper
“CXR was selected as the imaging input because it is widely available and commonly indicated by guidelines”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Abstract
What the sources say
- Not checked yetcites [22] American College of Radiology. CR recommendations for the use of chest radiography and computed tomography (CT) for suspected COVID-19 infection. https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection (2020).
- Partly supportedcites [23] Rubin, G. D. et al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society. Radiology 296, 172–180 (2020). sourceExperimental — accuracy not yet measured
The Essentials and Scenario 2 sections recommend chest imaging for certain COVID-19 presentations, supporting the claim that imaging is indicated by guidance. But the Methods section says the recommendations do not specify CXR over CT and leave modality choice to clinical teams. The paper notes CXR’s portability, not that it is widely available. (LLM, conf=0.84)
- Not checked yetcites [24] World Health Organization. Use of chest imaging in COVID-19. https://www.who.int/publications/i/item/use-of-chest-imaging-in-covid-19 (2020).
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In the paper
“Known issues of Batch Normalization (BN) in FL58 motivated us to fix our base model for image feature extraction49 in order to reduce the divergence between unbalanced client-sites.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
What the sources say
- Not checked yetcites [58] Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proc. 32nd International Conf. Machine Learning, PMLR 37, 448–456 (2015).
- Could not verifycites [49] Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305–311 (2020). sourceExperimental — accuracy not yet measured
The 'Decentralized data and federated machine learning' section discusses challenges in updating a central model, but does not address batch normalization. Searches for batch normalization and heterogeneous client data found no passage linking BN to divergence between client sites. The decision to fix the feature-extraction model is presented as the citing authors' own response, not as a finding attributed to this source. (LLM, conf=0.72)
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In the paper
“In the case of client sites with relatively small datasets, the best FL model markedly outperformed the ‘local’ model as well as models trained on larger datasets from 5 client-sites in the Boston area”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Federating The Model
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In the paper
“It also resulted in a 38% generalizability improvement (as defined by the average-AUC when running the model on all test sets; from 0.667 to 0.920 or 25.3 percentage points) of the best global model for predicting 24h oxygen treatment compared to models trained only on a site’s own data”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Federating The Model
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In the paper
“For MV at 72h, EXAM had a low false-negative rate of 7.1% at CDH.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Validation At Independent Sites
plainn = 2- Asserted: passes — The authors put it forward as true.
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In the paper
“NCH did not have any cases with MV / death at 24h.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Validation At Independent Sites
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“EXAM achieved an average area under the curve (AUC) greater than 0.92 for predicting outcomes at 24 and 72h from the time of initial presentation to the ER”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Abstract
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In the paper
“Training the model through FL resulted in a significant performance improvement (p<<1e-3, Wilcoxon signed-rank test)”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Federating The Model
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In the paper
“As for 72h MV prediction, EXAM achieved a sensitivity of 0.929 and specificity of 0.880 at CDH”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Validation At Independent Sites
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In the paper
“Patient age and EMR feature distributions varied greatly between sites, as expected due to the differing demographics between globally distributed hospitals”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Data Collection
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 31
- Probably not a substantial claim. 20
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The FL global trained model, EXAM, achieved an average AUC of 0.944 and 0.924 for 24/72h prediction tasks respectively (Table 2 b), which exceeded the average performance among sites used in training EXAM.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Validation At Independent Sites
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In the paper
“showing two false-negative cases from CDH where one case had many missing EMR data features, and the other case had a CXR with motion artifact and some missing EMR features.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Validation At Independent Sites
plaincorrelationaln = 2- Asserted: passes — The authors put it forward as true.
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In the paper
“For the prediction results of 72h oxygen treatments, the best global model training resulted in an average performance improvement of 18% compared to locally trained models, while generalizability of the global model improved on average by 34%”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Federating The Model
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The FL model achieved a higher true positive rate for the two positive (severe) cases and a markedly lower false positive rate compared to the local model”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Federating The Model
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We conducted a Wilcoxon signed-rank test to confirm the significance of the observed improvement in performance between the locally trained model and the FL model for the 24 and 72 hr time point (see Fig. 2 and Extended Data Fig. 1). The null hypothesis was rejected with a one-sided p-value << 1e-3 in both cases.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Statistical Analysis
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In the paper
“Our results show that the F-values of 5 different local sites are 245.7, 253.4, 342.3, 389.8, while the F-value of the FL model is 843.5.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Statistical Analysis
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In the paper
“Histogram of CORISK results at MGB, with an illustration of how the score can be used for patient triage, in which ‘A’ is an example threshold for safe discharge that has 99.5% negative predictive value, and ‘B’ is an example threshold for Intensive Care Unit (ICU) admission that has 50.3% positive predictive value.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Extended Data
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In the paper
“The average performance improved by 18% (from 0.760 to 0.899 or 13.9 percentage points) compared to locally trained models alone, while average generalizability of the global model improved by 34% (from 0.669 to 0.899 or 23.0 percentage points).”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Extended Data
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In the paper
“For both the 24h and 72h outcome prediction models, the random splits for each of the three repeated local and FL training and evaluation experiments were independently generated.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Model input
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In the paper
“Each client-site was asked to randomly split its dataset into 3 parts, 70% for training, 10% for validation, and 20% for testing.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Model input
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In the paper
“The model was not re-trained at these sites, and it was only used for validation purposes.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Validation At Independent Sites
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“we limited the number of negative patients included to, at –most, 95% of the total cases at each client-site.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Data Collection
plainn = 16,148- Asserted: passes — The authors put it forward as true.
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In the paper
“All procedures were conducted in accordance with principles for human experimentation as defined in the Declaration of Helsinki and International Conference on Harmonization Good Clinical Practice guidelines and approved by the relevant institutional review boards”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Ethics Approval
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In the paper
“Data between sites was not harmonized prior to being extracted, and in light of real-life clinical informatics circumstances, a meticulous harmonization of the data input was not conducted by the authors”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Federating The Model
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In the paper
“MI-CLAIM guidelines for reporting of clinical AI models were followed”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Ethics Approval
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In the paper
“The model’s design was constrained to objective predictors, unlike many published studies that leveraged subjective clinical impressions.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
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None of the sources cited for this claim have been checked yet.
In the paper
“FL has already shown promise in recent medical imaging applications40–43, including in COVID-19 analysis8,44,45.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Abstract
What the sources say
- Not checked yetcites [40] Roth, H. R. et al. Federated learning for breast density classification: a real-world implementation. In Proc. Second MICCAI Workshop, DART 2020 and First MICCAI Workshop, DCL 2020, Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning (eds. Albarqouni, S. et al.) Vol. 12,444, 181–191 (Springer International Publishing, 2020).
- Not checked yetcites [41] Sheller, M. J. et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10, 12598 (2020). source
- Not checked yetcites [42] Remedios, S. W., Butman, J. A., Landman, B. A. & Pham, D. L. in Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers (eds Remedios, S. W. et al.) (Springer, 2020). source
- Not checked yetcites [43] Xu, Y. et al. A collaborative online AI engine for CT-based COVID-19 diagnosis. Preprint at https://www.medrxiv.org/content/10.1101/2020.05.10.20096073v2 (2020).
- Not checked yetcites [8] Yang, D. et al. Federated semi-supervised learning for Covid region segmentation in chest ct using multi-national data from China, Italy, Japan. Med. Image Anal. 70, 101992 (2021). source
- Not checked yetcites [44] Raisaro, J. L. et al. SCOR: A secure international informatics infrastructure to investigate COVID-19. J. Am. Med. Inform. Assoc. 27, 1721–1726 (2020). source
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In the paper
“The validation results confirmed that the global model is robust, supporting our hypothesis that FL trained models are generalisable across healthcare systems.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The EXAM score can be used in the same way.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Extended Data
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The CORISK thresholds derived at MGB are applicable to other populations.
None of the sources cited for this claim have been checked yet.
In the paper
“Governance of data for FL is maintained locally, alleviating privacy concerns, with only model weights or gradients communicated between the client-sites and the federated server38,39.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Abstract
What the sources say
- Not checked yetcites [38] Ma, C. et al. On safeguarding privacy and security in the framework of federated learning. IEEE Netw. 34, 242–248 (2020). source
- Not checked yetcites [39] Brisimi, T. S. et al. Federated learning of predictive models from federated Electronic Health Records. Int. J. Med. Inform. 112, 59–67 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The global FL model, EXAM, proved to be more robust and achieved better results on individual sites than any model that was trained only on local data.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“We also used cases with unknown SARS-COV-2 status, and so the model could provide input to the physician ahead of receiving an RT-PCR test result, making it useful for a real-life clinical setting.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Our study involved multiple sites across four continents and under the oversight of different regulatory bodies, and thus holds the promise of being provided to different regulated markets in an expedited way.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Similar to other machine learning models, EXAM is limited by the quality of the training data.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In contrast, a patient who is at a lower risk of requiring invasive oxygen therapy may be placed in a less intensive care setting such as a regular ward or even released from the ED for continued self-monitoring at home65.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > EXAM Model Development
What the sources say
- Not checked yetcites [65] Kilaru, A. S. et al. Return hospital admissions among 1419 COVID-19 patients discharged from five U.S. emergency departments. Acad. Emerg. Med. 27, 1039–1042 (2020). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“We believe that due to the diversity across 20 sites, this risk may have been mitigated.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Of note, the model is not approved by any regulatory agency at this time, and it should only be used for research purposes.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > EXAM Model Development
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The encryption algorithms, such as Homomorphic Encryption60 shall maintain the performance but may significantly increase the message sizes and training time.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
What the sources say
- Not checked yetcites [60] Zhang, C. et al. BatchCrypt: efficient homomorphic encryption for cross-silo federated learning. In Proc. 2020 USENIX Annual Technical Conference, ATC 2020, 493–506 (2020).
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“To date, most AI models, including the aforementioned CDS model, have been trained and validated on ‘narrow’ data that often lacks in diversity31,32, potentially resulting in over-fitting and lower generalizability.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Abstract
What the sources say
- Not checked yetcites [31] Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019). source
- Not checked yetcites [32] Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S. & Rubin, D. L. Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digit. Med. 2, 78 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The data collected reflects varied incidence rates, and thus the ‘population momentum’ we encountered is more diverse.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The dataset from the 20 institutes that participated in this study remains under their custody. This data was used for training at each of the local sites and was not shared with any of the other participant institutions or with the Federated Server, and it is not publicly available.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Data availability
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Arguably, the most established form of FL is implementing the Federated Averaging algorithm proposed by McMahan et al72, or variations thereof.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Federated Learning Details
What the sources say
- Not checked yetcites McMahan et al72
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The output of the CDS model was a score, termed ‘CORISK’27 that corresponded to oxygen support requirements, and that could aid in triaging patients by front-line clinicians28+30.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Abstract
What the sources say
- Not checked yetcites [27] Buch, V. et al. Development and validation of a deep learning model for prediction of severe outcomes in suspected COVID-19 Infection. Preprint at https://arxiv.org/abs/2103.11269 (2021).
- Not checked yetcites 28+30
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“While differential privacy algorithms36,48,49 show good protection, they may weaken the model’s performance.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
What the sources say
- Not checked yetcites [36] Rieke, N. et al. The future of digital health with federated learning. NPJ Dig. Med. 3, 119 (2020). source
- Not checked yetcites [48] Zhu, L., Liu, Z. & Han, S. in Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 14774–14784 (Curran Associates, Inc., 2019).
- Not checked yetcites [49] Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305–311 (2020). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Each client site learns locally, and shares model weight updates with a central server that aggregates contributions using secure SSL encryption and communication protocols.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Federated Learning Details
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“All code and software used in this study are publicly available on NGC.”
- Also stated in: § Code availability · plain
“The trained models, data preparation guidelines, code for training, validating testing the model, readme file, installation guideline, and license files are publicly available at NVIDIA NGC61: https://ngc.nvidia.com/catalog/models/nvidia:med:clara_train_covid19_exam_ehr_xray”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Code availability
What the sources say
- Not checked yetcites [61] Nvidia NGC Catalog: COVID-19 Related Models. https://ngc.nvidia.com/catalog/models?orderBy=scoreDESC&pageNumber=0&query=covid&quickFilter=models&filters (2020).
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Also stated in: § Code availability · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The finding, that these two approaches were outperformed on all three prediction tasks by the global FL model, indicate that the benefit for client-sites with small datasets arising from participation in FL collaborations is substantial.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We believe that consistent improvement was achieved not only due to larger, but also a more diverse data set, the use of data inputs that can be standardized and avoidance of clinical impressions / reported symptoms.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unlike the majority of the publications, focused on diagnosis of COVID-19 or predicting mortality, we predicted oxygen requirements that have implications for patient management.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As data was not centralized, it is not readily accessible. Given that, any future analysis of the results, beyond what was derived and collected, is limited.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The model’s imaging input is used in common practice, in contrast with models that use chest Computed Tomography (CT), a non-consensual diagnostic modality.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“A critical decision made during the evaluation of a patient at the initial point of care or the ED, is whether the patient is likely to require more invasive or resource-limited countermeasures or interventions”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > EXAM Model Development
What the sources say
- Not checked yetcites [64] Galloway, J. B. et al. A clinical risk score to identify patients with COVID-19 at high risk of critical care admission or death: an observational cohort study. J. Infect. 81, 282–288 (2020). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“For FL, there remains the potential risk of model ‘inversion’47 or even the reconstruction of training images from the model gradients themselves48.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > Use Of Differential Privacy
What the sources say
- Not checked yetcites [47] Fredrikson, M., Jha, S. & Ristenpart, T. Model inversion attacks that exploit confidence information and basic countermeasures. In Proc. 22nd ACM SIGSAC Conference on Computer and Communications Security 1322–1333, https://doi.org/10.1145/2810103.2813677 (2015). source
- Not checked yetcites [48] Zhu, L., Liu, Z. & Han, S. in Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 14774–14784 (Curran Associates, Inc., 2019).
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is likely a reflection of FL’s ability to capture more diversity than local training, and to mitigate the bias present in models trained on a homogenous population.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
Oxygen delivered to a patient is equivalent to the patient's oxygen need.
“It is assumed that oxygen delivered to the patient equates with the oxygen need.”
The paper relies on this without support.
In the early phase of the COVID-19 pandemic, many patients received high-flow oxygen prophylactically regardless of their oxygen need.
“However, in the early phase of the COVID-19 pandemic, many patients were provided high flow oxygen prophylactically, regardless of their oxygen need.”
The paper relies on this without support.
The clinical labels for oxygen support requirements accurately reflect the patients' true clinical states.
“The final output was a continuous value in the range of 0 – 1 for both the 24 hour and 72-hour predictions, corresponding to the labels described above”
The paper relies on this without support.
The test datasets at each site are independent and representative of the target populations.
“After the FL training finishes, the best local models and the best global model are automatically shared with all client-sites and evaluated on their local test data.”
The paper relies on this without support.
The CORISK thresholds derived at MGB are applicable to other populations.
“The EXAM score can be used in the same way.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Because federated learning uses several datasets reflecting worldwide diversity in demographics, imaging modalities, and disease presentations, it is particularly helpful in osteoarthritis imaging [180].”
From Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease (2025) · cites this paper as [180]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The authors in [56] discuss the development of a federated learning model called EXAM, which predicts the oxygen requirements of COVID-19 patients using data from 20 institutions worldwide.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [56]
In the citing paper
“Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [16]
In the citing paper
“The extended global footprint and the task complexity are what sets this study apart from current literature, since it dealt with a multi-parametric multi-class problem with reference standards that require expert clinicians following an involved manual annotation protocol, rather than simply recording a categorical entry from medical records16,32.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [16]
In the citing paper
“These insights differ from previous literature that describes studies that were smaller in scale and involved simpler tasks16,32.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [16]
In the citing paper
“demonstrating an improved average AUC and a broader applicability in contrast to models trained at only one site.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [56]
In the citing paper
“EXAM achieved a mean AUC (Area Under The Curve) of 0.92 in predicting outcomes within 24 and 72 h, demonstrating an improved average AUC and a broader applicability in contrast to models trained at only one site.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [56]
In the citing paper
“FL facilitated rapid collaboration without data sharing and enabled a model that could be generalized across heterogeneous datasets, demonstrating the potential of FL in healthcare.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [56]
In the citing paper
“The first way is to directly use databases from different medical sites/centers [48,100].”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [100]
In the citing paper
“Dayan et al. [100] build a large-scale federation through international cooperation.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [100]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Results > The EXAM Model Architecture
“In total, 20 features (19 from the EMR and a CXR) were used as input to the model.”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Model input
“In total, 21 EMR features were used as input to the model.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Statistical Analysis
“5 different local sites”
§ Federated learning for predicting clinical outcomes in patients with COVID-19 > Methods > Statistical Analysis
“245.7, 253.4, 342.3, 389.8”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1×
“9a, the model fuses information from both EMR and CXR features (based on a modified ResNet34 with spatial attention 66 pretrained on the CheXpert dataset) 67 and the Deep & Cross network 68 .”
MethodBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · cited 1×
“Arguably the most established form of FL is implemention of the federated averaging algorithm as proposed by McMahan et al. 72 , or variations thereof.”
MethodpROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · cited 1×
“The computation of P values was conducted in R with the pROC library 74 .”
MethodFederated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
MissForest—Non-parametric missing value imputation for mixed-type data
Stekhoven, D. J., & Bühlmann, P. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr597 · Cited by 6,156 · cited 1×
“A MissForest algorithm 71 was used to impute EMR features, based on the local training dataset.”
MethodA guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · cited 1×
“To date most AI models, including the aforementioned CDS model, have been trained and validated on ‘narrow’ data that often lack diversity 31,32 , potentially resulting in overfitting and lower generalizability.”
MethodThe future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015 · cited 4×
“This is conducted as an iterative process until training is complete 36 .”
BackgroundMethodInfluentialSelf-citationWynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · cited 2×
“For FL, there remains the potential risk of model ‘inversion’ 47 or even the reconstruction of training images from the model gradients themselves 48 .”
Background
Research works printed but not linked: 3
- Zhu, L., Liu, Z. & Han, S. in Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 14774–14784 (Curran Associates, Inc., 2019).
- Abadi, M. et al. TensorFlow: asystem for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), USENIX Association 265–283 (2016).
- Hsieh, K., Phanishayee, A., Mutlu, O. & Gibbons, P. B. The non-IID data quagmire of decentralized machine learning. In Proc. 37th International Conf. Machine Learning PMLR 119 (2020).
Cited by
Sorted by citations
Items 301–400 of 810
Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease
Kumar, R., Sporn, K., Borole, A., Khanna, A., Gowda, C., Paladugu, P., Ngo, A., Jagadeesan, R., Zaman, N., & Tavakkoli, A. (2025) · Diagnostics · doi:10.3390/diagnostics15111418 · Cited by 10
“Because federated learning uses several datasets reflecting worldwide diversity in demographics, imaging modalities, and disease presentations, it is particularly helpful in osteoarthritis imaging [180].”
Kanthavel, R., & Dhaya, R. (2025) · Innovation and Emerging Technologies · doi:10.1142/s2737599425500252 · Cited by 10
Liu, Y., & Bi, D. (2023) · Risk Analysis · doi:10.1111/risa.14124 · Cited by 8
FeLebrities: A User-Centric Assessment of Federated Learning Frameworks
Riviera, W., Galazzo, I. B., & Menegaz, G. (2023) · IEEE Access · doi:10.1109/access.2023.3312579 · Cited by 10
Frameworks for Privacy-Preserving Federated Learning
Phong, L. T., Phuong, T. T., Wang, L., & Ozawa, S. (2023) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2023mui0001 · Cited by 9
Schmidt, K., Bearce, B., Chang, K., Coombs, L. P., Farahani, K., Elbatel, M., Mouheb, K., Martí, R., Zhang, R., Zhang, Y., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103206 · Cited by 9
Self-citationMessinis, S., Protonotarios, N. Ε., & Doulamis, N. D. (2024) · Sensors · doi:10.3390/s24165142 · Cited by 11
Zhou, H.-Y., Li, Y., Li, J., Meng, J., & Wu, A. (2024) · The Innovation Medicine · doi:10.59717/j.xinn-med.2024.100091 · Cited by 10
Medical Federated Model With Mixture of Personalized and Shared Components
Zhao, Y., Liu, Q., Liu, P., Liu, X., & He, K. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3470072 · Cited by 17
Non-IID Medical Imaging Data on COVID-19 in the Federated Learning Framework: Impact and Directions
Al-Hafiz, F., & Basuhail, A. A. (2024) · COVID · doi:10.3390/covid4120140 · Cited by 11
LMSA: A Lightweight Multi-Key Secure Aggregation Framework for Privacy-Preserving Healthcare AIoT
Park, H., & Lee, J. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.061178 · Cited by 12
Addressing data heterogeneity in distributed medical imaging with heterosync learning
Hu, H.-T., Li, M., Lin, X.-X., Cai, M.-Y., Liu, S., Wu, S., Tong, W., Ye, F.-Y., Hu, J., Ke, W.-P., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64459-y · Cited by 13
Self-citationWalston, S. L., Matsumoto, T., Miki, Y., & Ueda, D. (2022) · The British Journal of Radiology · doi:10.1259/bjr.20220058 · Cited by 9
Federated learning framework integrating REFINED CNN and Deep Regression Forests
Nolte, D., Bazgir, O., Ghosh, S., & Pal, R. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad036 · Cited by 9
Photonic optical accelerators: The future engine for the era of modern AI?
Makarenko, M. O., Wang, Q., Lopez, A. B., & Fratalocchi, A. (2023) · APL Photonics · doi:10.1063/5.0174044 · Cited by 10
Tan, X., Chang, C. H., Zhou, L., & Tang, L. (2021) · PubMed · doi:10.48550/arxiv.2103.06261 · Cited by 13
GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging
Pennisi, M., Salanitri, F. P., Palazzo, S., Pino, C., Rundo, F., Giordano, D., & Spampinato, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18523-6_7 · Cited by 8
Federated Discriminative Representation Learning for Image Classification
Zhang, Y., Wang, Y., Li, Y., Xu, Y., Wei, S., Liu, S., & Shang, X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3336957 · Cited by 12
Dynamic selectout and voting-based federated learning for enhanced medical image analysis
Iqbal, S., Qureshi, A. N., Alhussein, M. A., Aurangzeb, K., Mahmood, A., & Azzuhri, S. R. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ada0a6 · Cited by 8
Loftus, T. J., Haider, A. H., & Upchurch, G. R. (2025) · JAMA Surgery · doi:10.1001/jamasurg.2024.6025 · Cited by 9
Williams, M. C., MacArthur, J. A. L., Forsyth, R., & Petersen, S. E. (2025) · European Heart Journal - Imaging Methods and Practice · doi:10.1093/ehjimp/qyaf012 · Cited by 9
Automation in sensor network metrology: An overview of methods and their implementations
Vedurmudi, A. P., Miličević, K., Kok, G., Yong, B. X., Xu, L., Ge, Z., Brintrup, A. M., Gruber, M., Tabandeh, S., Zaidan, M. A., et al. (2025) · Measurement Sensors · doi:10.1016/j.measen.2024.101799 · Cited by 8
Medical imaging-based artificial intelligence in pneumonia: A narrative review
Yang, Y., Xing, W., Liu, Y., Li, Y., Ta, D., Song, Y., & Hou, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129731 · Cited by 9
Li, S., Yan, M., Yuan, R., Liu, M., Liu, N., & Hong, C. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104780 · Cited by 9
Federated Learning for Predicting Major Postoperative Complications
Ren, Y., Park, Y., Shickel, B., Guan, Z., Patel, A. K., Ma, Y., Hu, Z., Balch, J. A., Loftus, T. J., Rashidi, P., et al. (2025) · Annals of Surgery Open · doi:10.1097/as9.0000000000000573 · Cited by 9
Ghorbian, M., Ghorbian, ., & Ghobaei‐Arani, M. (2025) · Clinical and Experimental Medicine · doi:10.1007/s10238-025-01753-5 · Cited by 8
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Chanci, D., Grunwell, J. R., Rafiei, A., et al. (2023) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000003410 · Cited by 8
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Durga, S., Daniel, E., Seetha, S., Reshma, V. K., & Sachnev, V. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1633916 · Cited by 10
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Zhang, R., Chen, Y., Yue, W., Zhang, Y., Li, X., Feng, S., Yuan, F., & Luo, M. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2025.1736272 · Cited by 15
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Farjana, A., & Makkar, A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23599-3_9 · Cited by 7
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Nath, S., Rahimy, E., Kras, A., & Korot, E. (2023) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000986 · Cited by 7
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He, H., Wang, L., Wang, X., & Zhang, M. (2023) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2023.2274325 · Cited by 8
Pais, V., Rao, S., Muniyal, B., & Yun, S. (2024) · Cogent Engineering · doi:10.1080/23311916.2023.2301150 · Cited by 11
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Zhao, S., Zhu, Z., Li, X., & Chen, Y. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3433419 · Cited by 7
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Zhang, A. S., & Li, N. F. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-18461-1_13 · Cited by 10
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Shen, C., Wang, P., Yang, D., Xu, D., Oda, M., Chen, P., Liu, K., Liao, W.-C., Fuh, C., Mori, K., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18523-6_6 · Cited by 9
Self-citationGuan, X., Yu, G., Zhang, W., Wen, R., Wei, R., Jiao, S., Zhao, Q., Lou, Z., Hao, L., Liu, E., et al. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000067 · Cited by 7
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Truhn, D., Arasteh, S. T., Saldanha, O. L., et al. (2022) · medRxiv · doi:10.1101/2022.07.28.22277288 · Cited by 7
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Balelli, I., Sportisse, A., Cremonesi, F., Mattei, P., & Lorenzi, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4732357 · Cited by 13
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Macedo, D., Santos, D. F. S., Perkusich, Â., & Valadares, D. C. G. (2023) · Sensors · doi:10.3390/s23146286 · Cited by 8
Zhu, K., Lu, M., Li, H., Xiong, N. N., & He, W. (2025) · Digital Communications and Networks · doi:10.1016/j.dcan.2025.04.011 · Cited by 7
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Zhou, Z., Luo, G., Chen, M., Weng, Z., & Zhu, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3631706 · Cited by 27
Sun, J.-R., Sun, X.-N., Lu, B.-J., & Deng, B. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i46.111176 · Cited by 12
Bibi, M., Ahmad, R., Rizwan, A., Khan, A. N., Khan, Q. W., & Kim, D.-H. (2026) · IEEE Access · doi:10.1109/access.2025.3650610 · Cited by 9
Alves, R., Costa, B., Martins, H., & Faria, N. (2026) · Pharmacophore · doi:10.51847/f2rnaaswj2 · Cited by 7
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Li, Z., Yan, C., Zhang, X., Gharibi, G., Yin, Z., Jiang, X., & Malin, B. (2023) · PubMed Central · doi:10.48550/arxiv.2308.11027 · Cited by 11
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Zhang, M., Lin, F., Tian, L., Jia, R., Zheng, Z., & Li, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3389593 · Cited by 7
Li, X., Jones, P., & Zhao, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-61851-4 · Cited by 7
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Huang, Y., Bert, C., Gomaa, A., Fietkau, R., Maier, A., & Putz, F. (2024) · IEEE Access · doi:10.1109/access.2024.3431885 · Cited by 10
Oyekan, B. (2024) · Journal of Knowledge Learning and Science Technology · doi:10.60087/jklst.vol3.n3.p139-164 · Cited by 6
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Nguyen, T. V., Dakka, M. A., Diakiw, S. M., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1371143/v1 · Cited by 6
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Qureshi, R., Irfan, M., Ali, H., Khan, A., Ali, S., shah, Z., Gondal, T. M., Sadak, F., Shah, Z., Hadi, M. U., et al. (2023) · doi:10.36227/techrxiv.22293442.v2 · Cited by 205
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Mali, B., Singh, P. K., & Mazumdar, N. (2024) · Security and Privacy · doi:10.1002/spy2.403 · Cited by 7
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Wu, C., Pham, B.-T., Wang, J., et al. (2022) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2022.09.014 · Cited by 5
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Metadata
- Authors
- Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., Wang, C.-H., Hsu, C.-N., Lee, C. K., Ruan, P., Xu, D., Wu, D., Huang, E., Kitamura, F. C., Lacey, G., de Antônio Corradi, G. C., Nino, G., Shin, H.-H., Obinata, H., Ren, H., Crane, J. C., Tetreault, J., Guan, J., Garrett, J. W., Kaggie, J. D., Park, J. G., Dreyer, K., Juluru, K., Kersten, K., Rockenbach, M. A. B. C., Linguraru, M. G., Haider, M. A., AbdelMaseeh, M., Rieke, N., Damasceno, P. F., e Silva, P. M. C., Wang, P., Xu, S., Kawano, S., Sriswasdi, S., Park, S. Y., Grist, T. M., Buch, V., Jantarabenjakul, W., Wang, W., Tak, W. Y., Li, X., Lin, X., Kwon, Y. J., Quraini, A., Feng, A., Priest, A. N., Turkbey, B., Glicksberg, B., Bizzo, B., Kim, B. S., Tor-Díez, C., Lee, C.-C., Hsu, C.-J., Lin, C., Lai, C.-L., Hess, C. P., Compas, C., Bhatia, D., Oermann, E. K., Leibovitz, E., Sasaki, H., Mori, H., Yang, I., Sohn, J. H., Murthy, K. N. K., Fu, L.-C., de Mendonça, M. R. F., Fralick, M., Kang, M. K., Adil, M., Gangai, N., Vateekul, P., Elnajjar, P., Hickman, S., Majumdar, S., McLeod, S. L., Reed, S., Gräf, S., Harmon, S., Kodama, T., Puthanakit, T., Mazzulli, T., de Lavor, V. L., Rakvongthai, Y., Lee, Y. R., Wen, Y., Gilbert, F. J., Flores, M. G., & Li, Q.
- Affiliations
- Harvard University (US)
- Nvidia (United States) (US)
- VA San Diego Healthcare System (US)
- Icahn School of Medicine at Mount Sinai (US)
- National Institutes of Health (US)
- Tri-Service General Hospital (TW)
- DASA (Brazil) (BR)
- Memorial Sloan Kettering Cancer Center (US)
- Self-Defense Forces Central Hospital (JP)
- University of California, San Francisco (US)
- University of Wisconsin–Madison (US)
- University of Cambridge (GB)
- Yeungnam University College (KR)
- Children's National (US)
- University of Toronto (CA)
- Lunenfeld-Tanenbaum Research Institute (CA)
- Chulalongkorn University (TH)
- Kyungpook National University (KR)
- Mass General Brigham (US)
- National Taiwan University (TW)
- Daegu Catholic University (KR)
- National Defense Medical Center (TW)
- National Health Insurance Administration (TW)
- New York University (US)
- Sinai Health System (CA)
- University Health Network (CA)
- Corresponding author
- Flores, Mona G.
- ORCID
- Dayan, Ittai
- Roth, Holger R.
- Zhong, Aoxiao
- Gentili, Amilcare
- Abidin, Anas Z.
- Costa, Anthony Beardsworth
- Wood, Bradford J.
- Tsai, Chien-Sung
- Wang, Chih-Hung
- Hsu, Chun-Nan
- Xu, Daguang
- Wu, Dufan
- Kitamura, Felipe Campos
- Shin, Hao-Hsin
- Obinata, Hirofumi
- Ren, Hui
- Crane, Jason C.
- Tetreault, Jesse
- Guan, Jiahui
- Garrett, John W.
- Kaggie, Joshua D.
- Park, Jung Gil
- Dreyer, Keith
- Juluru, Krishna
- Rockenbach, Marcio Aloisio Bezerra Cavalcanti
- Linguraru, Marius George
- Haider, Masoom A.
- AbdelMaseeh, Meena
- Rieke, Nicola
- Damasceno, Pablo F.
- Wang, Pochuan
- Xu, Sheng
- Sriswasdi, Sira
- Park, Soo Young
- Grist, Thomas M.
- Buch, Varun
- Jantarabenjakul, Watsamon
- Wang, Weichung
- Tak, Won Young
- Li, Xiang
- Lin, Xihong
- Kwon, Young Joon
- Feng, Andrew
- Priest, Andrew N.
- Turkbey, Baris
- Glicksberg, Benjamin
- Bizzo, Bernardo
- Kim, Byung Seok
- Tor-Díez, Carlos
- Lee, Chia-Cheng
- Hsu, Chia-Jung
- Lin, Chin
- Hess, Christopher P.
- Oermann, Eric K.
- Sasaki, Hisashi
- Yang, Isaac
- Sohn, Jae Ho
- Murthy, Krishna Nand Keshava
- Fu, Li-Chen
- de Mendonça, Matheus Ribeiro Furtado
- Fralick, Mike
- Kang, Min Kyu
- Gangai, Natalie
- Vateekul, Peerapon
- Elnajjar, Pierre
- Hickman, Sarah
- Majumdar, Sharmila
- McLeod, Shelley L.
- Reed, Sheridan
- Gräf, Stefan
- Harmon, Stephanie
- Kodama, Tatsuya
- Puthanakit, Thanyawee
- Mazzulli, Tony
- Rakvongthai, Yothin
- Lee, Yu Rim
- Wen, Yuhong
- Gilbert, Fiona J.
- Flores, Mona G.
- Li, Quanzheng
- Venue
- Nature Medicine
- Published
- 2021-09-15
- Volume
- 27
- Issue
- 10
- Pages
- 1735-1743
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC9157510
- OpenAlex
- W3200840849
- Semantic Scholar
- 99b056a04fd4f98cad8b9987ff8d205fe62d786e
- Cited by
- 842
- References
- 68
- Citations by year
- 2026: 194 · 2025: 166 · 2024: 150 · 2023: 197 · 2022: 122 · 2021: 10
- FWCI
- 42.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion, other-oa)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Europe PMC (PubMed Central) (submittedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Foundation for the National Institutes of Health
- Doris Duke Charitable Foundation
- Colgate-Palmolive Company
- Brigham and Women's Hospital
- Massachusetts General Hospital
- American Association for Dental, Oral, and Craniofacial Research
- Nvidia
- National Center for Theoretical Sciences
- Children's National Hospital
- Lunit
- Cancer Research UK (26884)
- National Institute for Health and Care Research (NF-SI-0515-10067)
- Department of Health and Social Care
- University of Cambridge
- Chulalongkorn University
- Cambridge University Hospitals
- Ministry of Science and Technology, Taiwan
- Centre d'Imagerie BioMédicale
- National Taiwan University
- National Institutes of Health (COVID-19)
- Genentech
- University of California, San Francisco
- National Health Insurance Administration
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Engineering and Physical Sciences Research Council (EP/N014588/1)
- Faculty of Medicine, Chulalongkorn University
- NIHR Cambridge Biomedical Research Centre
- National Heart, Lung, and Blood Institute (R01HL141237, R42HL145669)
- National Cancer Institute (P30CA008748, ZIDBC011242)
- National Institute of Allergy and Infectious Diseases (COVID-19)
- U.S. National Library of Medicine (R01LM013151)
- NIH Clinical Center (ZIACL040015)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, COVID-19 pandemic, clinical outcome prediction, chest radiography, electronic health records, mechanical ventilation prediction, mortality prediction, model generalization, vital signs, laboratory findings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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