The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index; 4 of the 9 sources its claims cite were checked for support.
Federated learning improves site performance in multicenter deep learning without data sharing
Open accessAbstract
Objective: To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL). Materials and Methods: Deep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions. Results: We found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset. Discussion: The power of FL was successfully demonstrated across 3 academic institutions while avoiding the privacy risk associated with the transfer and pooling of patient data. Conclusion: Federated learning is an effective methodology that merits further study to enable accelerated development of models across institutions, enabling greater generalizability in clinical use.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
In a 3-institution study using real prostate MRI, federated learning produced a single model without sharing patient data. The FL model outperformed each institution’s private model on held-out institutional test sets and an external challenge dataset.
Key findings
- The FL model achieved an overall private-test-set mean Dice of 0.895 ± 0.036, significantly better than each private model (P < .001); private model overall Dice values were 0.833 ± 0.131 (NCI), 0.854 ± 0.074 (SUNY), and 0.745 ± 0.178 (UCLA).
- On the external PROSTATEx challenge dataset (n = 343), the FL model achieved 0.889 ± 0.036, significantly better than the private models: NCI 0.872 ± 0.062, SUNY 0.838 ± 0.043, UCLA 0.812 ± 0.136 (P < .001 for all comparisons).
- Private models performed well on their own institutional test sets (Dice range 0.883–0.925) but had diminished performance on other institutions’ test sets (Dice range 0.575–0.887).
Methods
- Retrospective multicenter study at UCLA, SUNY Upstate, and NCI: each institution collected 1 prostate MRI from 100 patients, with axial T2W images at 3T and expert whole-prostate segmentations; each private dataset was split into 80 training and 20 held-out test images. An external challenge dataset of 343 axial T2W prostate images from SPIE-AAPM-NCI PROSTATEx was also used.
- The model was a 3D Anisotropic Hybrid Network trained with soft Dice loss and Adam; each private training set was split into 5 sets of 16 images to train 5 submodels combined into an ensemble. All models were trained for 300 epochs.
- Federated learning used a cloud-based aggregation server; after each epoch, model weights and validation metrics were sent from each institution, combined by a weighted average based on validation mean Dice, and redistributed. Evaluation used Dice coefficient and 2-sided paired t-tests comparing private models to the FL model on the combined private test set and the challenge dataset.
Limitations
- Stated: the authors did not address the potential for an inside actor to recover underlying patient data through a model inversion attack on shared weights; they suggest calibrated distortion to shared weights as a possible future enhancement.
- Stated: the task (prostate segmentation on T2-weighted MRI) is relatively simple, and all private models achieved high performance on their own institutional datasets, so the study could not demonstrate that an FL-trained model would significantly outperform a single-site-trained model on that single site’s data.
- Stated: because private datasets were similarly sized, the authors did not explore varying the federated aggregation methodology; adding more institutions may introduce challenges in imaging data quality heterogeneity, governance, intellectual property, and model generalizability, possibly requiring private fine-tuning. Evident from the text: only 3 academic institutions and one segmentation task were tested, and no formal privacy attack evaluation was performed.
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Claims the paper makes
8 Own interpretation · 4 Own method · 4 Own result · 3 Partly supported · 2 No basis given · 1 Sources not found · 1 No full text of the sources · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, works that examine the practical application of FL in radiological applications are still limited.20,21”
§ Federated learning improves site performance in multicenter deep learning without data sharing > INTRODUCTION
What the sources say
- Not matchedcites [20] Roth
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites [21] Sheller, 2020, Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data, Sci Rep sourceExperimental — accuracy not yet measured
The Methods > Data and Discussion passages describe this paper’s evaluation of federated learning on radiographic brain imaging, so it is an example of the kind of work the citing sentence identifies. They do not establish that such works are limited in number. No page numbers are available in our copy. (LLM, conf=0.81)
plain- Asserted: passes — The authors put it forward as true.
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- Not matched
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 private models exhibited varied performance on the challenge dataset (Dice coefficient range: 0.812–0.872).”
§ Federated learning improves site performance in multicenter deep learning without data sharing > RESULTS
plain- 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.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“we were unable to demonstrate the expected benefit that an FL-trained model would significantly outperform a single-site-trained model on that single site’s data.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > 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.
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
“At no point during this study were private data transferred or shared across institutions.”
- Also stated in: § DISCUSSION · plain
“Since no transfer of protected health information (or even deidentified health information) was required, we were able to address the privacy and data governance limitations inherent to multicenter studies through the use of simplified 2-way collaboration agreements, rather than requiring the negotiation of a complex 4-way collaboration and material transfer agreement that would have been required”
§ Federated learning improves site performance in multicenter deep learning without data sharing > MATERIALS AND METHODS > Study overview
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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- Also stated in: § DISCUSSION · 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 power of federated learning was successfully demonstrated across 3 academic institutions using real clinical prostate imaging data.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > CONCLUSION
boosteddirect- 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.
In the paper
“A major limitation of the DL approach is the need for a large volume of training data that captures the full breadth of inputs on which the model is likely to be subsequently used.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [13] Russakovsky, 2015, ImageNet large scale visual recognition challenge, Int J Comput Vis 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.
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 the method we demonstrate in this paper significantly better protects the privacy of patients than the current standard of direct sharing of data.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > 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.
- Rests on an assumption: The practical risk of model inversion by an inside actor outside crafted malintent is low due to the weight averaging scheme.
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 suggests that the FL model was able to benefit from the advantage of learning important institution-specific knowledge through the FL aggregation paradigm, without requiring any individual training site to “see” the full breadth of inputs.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > 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.
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
“participating institutions intentionally made no effort to harmonize either the T2W acquisition protocol or segmentation methodologies.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > MATERIALS AND METHODS > Datasets and preprocessing
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.
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
“suggesting that the model also attained the expected advantages inherent in training with more data through the FL aggregation method, even though the full dataset was not seen at any single training site.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > 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.
- Probably not a substantial claim. 6
- It may state two separate claims as one. 8
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Previous work has demonstrated the potential utility of FL for model training, generally using publicly available data to simulate multi-institutional training.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > INTRODUCTION
What the sources say
- Not matchedcites [20] Roth
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites [21] Sheller, 2020, Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data, Sci Rep source
The Discussion reports that FL trained models across multiple institutional datasets performed comparably to centralized training. The Methods (Data; page unavailable) say the authors used the publicly available BraTS training dataset and divided it to match the ten institutions that contributed the scans, supporting the description of simulated multi-institutional training. The division reflected actual institutional origins rather than arbitrary hypothetical institutions. (LLM, conf=0.88)
boosted- 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.
- Not matched
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Such models often suffer from poor performance when transferred to other institutions with differing protocols, equipment, or patient populations.14,15”
§ Federated learning improves site performance in multicenter deep learning without data sharing > INTRODUCTION
What the sources say
- Supportedcites [14] Yasaka, 2018, Deep learning and artificial intelligence in radiology: current applications and future directions, PLOS Med source
The paper’s discussion of model transportability says deep learning models do not necessarily perform consistently across hospitals and identifies differences in imaging vendors, scan conditions, and patient populations as relevant. It also reports poorer performance on external hospital data than on data from the training sites. (LLM, conf=0.91)
- Partly supportedcites [15] De Fauw, 2018, Clinically applicable deep learning for diagnosis and referral in retinal disease, Nat Med sourceExperimental — accuracy not yet measured
The Introduction (p. 1) identifies generalization to new populations and devices as a challenge. In “Generalization to a New Scanning Device Type” (p. 7), the authors report a 46.6% referral-error rate when their model was applied to scans from a different device without retraining. That supports the equipment-transfer point, but the study does not establish that models often perform poorly across institutions or that differing protocols or patient populations caused such declines. (LLM, conf=0.88)
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.
- Supported
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 private models performed well on their own private test sets (Dice coefficient range: 0.883–0.925) but had diminished performance on the other private test sets (Dice coefficient range: 0.575–0.887). This led to overall mean Dice coefficients between 0.745 and 0.833 for the private models.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > RESULTS
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 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 generic FL model outperformed each of the private models, with an overall mean Dice coefficient of 0.889. The statistical analysis again demonstrated that the FL model was significantly superior to any of the private models (P < .001).”
§ Federated learning improves site performance in multicenter deep learning without data sharing > RESULTS
boosteddirectn = 343 ·- 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 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 model exhibited private test set mean Dice coefficients between 0.880 and 0.920, yielding an overall result of 0.895. The statistical analysis using 2-sided paired t-tests demonstrated that the FL model was significantly superior to any of the private models (P < .001 for all comparisons).”
- Also stated in: § DISCUSSION · plain
“The FL model that we trained performed well across all of the private datasets, yielding an overall performance level that was significantly better than that of any of the private models alone.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > RESULTS
boosteddirectn = 60 ·- 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.
- Also stated in: § DISCUSSION · 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 federated model demonstrated improved performance across both held-out test sets from each institution and an external test set, validating the FL paradigm.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > CONCLUSION
boosteddirect- 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
“in the worst case, our method is no less secure as only model weights are ever transmitted.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > 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
“All 3 academic institutions had IRB approval for review and image analysis, with written informed patient consent or waiver of patient consent.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > MATERIALS AND METHODS > Data governance
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. 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
“we did not attempt to address the potential for an inside actor (ie, 1 of the participating institutions) to attempt to recover the underlying patient data through a model inversion attack on the trained weights shared during federated learning.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > 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: 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 could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“the sharing of trained model weights is an accepted practice within healthcare,28,29”
§ Federated learning improves site performance in multicenter deep learning without data sharing > DISCUSSION
What the sources say
- Not matchedcites [28] Soni
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [29] Liu, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- Not matched
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
“Our work shows that FL can be reduced to practice using real-world private clinical data across multiple institutions, and that this approach creates a model that demonstrates improved generalizability both within the participating institutions and with outside data.”
- Also stated in: § Results · plain
“We found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset.”
- Also stated in: § CONCLUSION · boosted
“The federated model demonstrated improved performance across both held-out test sets from each institution and an external test set, validating the FL paradigm.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > INTRODUCTION
boosteddirect- 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.
- Also stated in: § Results · 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
“though model inversion is a technical risk that cannot be ruled out”
§ Federated learning improves site performance in multicenter deep learning without data sharing > 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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, a limitation of this approach is that no single model ever “sees” a complete picture of the universe of potential inputs during the training phase, thus placing pressure on the federated aggregation function to adequately distribute knowledge from each site into the model.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > INTRODUCTION
plaindirect- 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.
The paper relies on this without support.
The practical risk of model inversion by an inside actor outside crafted malintent is low due to the weight averaging scheme.
“we empirically believe that the practical risk of inversion outside of crafted malintent on the part of study designers to be low due to the weight averaging scheme in place.”
The paper relies on this without support.
Medical imaging data in most cases is siloed within provider institutions, and assembling large-scale datasets traditionally requires transfer of data between silos.
“However, medical imaging data in most cases is siloed within provider institutions, and, as a result, assembling large-scale datasets traditionally requires the transfer of data between these silos.”
The paper relies on this without support.
Very few public large-scale pooled medical image datasets exist.
“As a result, very few public large-scale pooled medical image datasets exist.”
The paper relies on this without support.
Deep learning models are primarily interpolators, not extrapolators—they perform best when presented with inputs similar to training data.
“This large volume is required because deep learning models are primarily interpolators, not extrapolators—that is, they perform best when presented with inputs that are similar to the data that those models were trained on.”
The paper relies on this without support.
The task of prostate segmentation on T2-weighted MRI is relatively simple.
“the task we used (prostate segmentation on T2-weighted MRI) is relatively simple”
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?
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 [19]
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 improves site performance in multicenter deep learning without data sharing > RESULTS
“This led to overall mean Dice coefficients between 0.745 and 0.833 for the private models.”
§ Federated learning improves site performance in multicenter deep learning without data sharing > RESULTS
“| Private models | SUNY | 0.887 ± 0.027 | 0.906 ± 0.018 | 0.768 ± 0.064 | 0.854 ± 0.074* |”
Citations
Cites
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · cited 1×
“13 This large volume is required because deep learning models are primarily interpolators , not extrapolators— that is, they perform best when presented with inputs that are similar to the data that those models were trained on.”
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · cited 1×
“DL has enabled a wave of innovation in imaging decision support, with recent major results in the fields of ophthalmology, 2–4 dermatology, 5–7 pathology, 8–10 and radiology.”
Li, Z., & Hoiem, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2773081 · Cited by 4,202
The Future of Digital Health with Federated Learning
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., et al. (2020) · arXiv · doi:10.48550/arxiv.2003.08119 · Cited by 3,495
Self-citationEhteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · cited 1×
“DL has enabled a wave of innovation in imaging decision support, with recent major results in the fields of ophthalmology, dermatology, pathology, and radiology.(11,12) A major limitation of the DL approach is the need for a large volume of training data that captures the full breadth of inputs on which the model is l…”
BackgroundCoudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639 · cited 1×
“14,15 As a result, there is a need for methods to enable the development of general models for clinical use, without requiring the creation of pooled datasets.”
BackgroundSecure, privacy-preserving and federated machine learning in medical imaging
Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,676
Cited by
Sorted by citations
Algorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750
“…multi-institutional collaboration to aid the validation of AI algorithms for prostate segmentation, brain-cancer detection, the monitoring of the progression of Alzheimer’s disease using magnetic resonance imaging (MRI) and the classification of paediatric chest X-ray images 211,214,222–224 .”
BackgroundShifting machine learning for healthcare from development to deployment and from models to data
Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00898-y · Cited by 420
“…learning were found to achieve 99% of the performance of traditional centralized training even with imbalanced datasets or with relatively few samples per institution, thus showing that federated learning can be realistically implemented without sacrificing performance or generalization 77,78 .”
BackgroundFederated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394
“The exact 49 site IDs are: 1, 2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 49, 50, 52, 53, 54, 56, 59, 60.”
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315
“In [153], the efficacy of FL has been exemplified through a multi-institutional collaboration involving three esteemed academic establishments: 1) the University of California at Los Angeles (UCLA), USA; 2) the State University of New York (SUNY) Upstate Medical University, USA; and 3) the National…”
BackgroundFederated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228
Prayitno, Shyu, C.-R., Putra, K. T., Chen, H.-C., Tsai, Y.-Y., Hossain, K. S. M. T., Jiang, W., & Shae, Z.-Y. (2021) · Applied Sciences · doi:10.3390/app112311191 · Cited by 210
“There are several published FL studies in medical image segmentation, namely brain tumor disease [38], COVID-19 region [53], and prostate cancer region [50].”
InfluentialFederated Learning for Healthcare Domain - Pipeline, Applications and Challenges
Joshi, M., Pal, A., & Sankarasubbu, M. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3533708 · Cited by 218
“Each hospital distributes the local model that has been trained with patient data and acquires the global model for future diagnosis [97].”
BackgroundMethodInfluentialApplications of Federated Learning; Taxonomy, Challenges, and Research Trends
Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157
Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-07186-4 · Cited by 130
“Privacy preserving federated learning systems for medical image analysis have been mainly explored in the context of segmentation for brain 5–8 , prostate 9 and COVID-19 affected regions 10–12 .”
BackgroundDo Gradient Inversion Attacks Make Federated Learning Unsafe?
Hatamizadeh, A., Yin, H., Molchanov, P. A., Myronenko, A., Li, W., Dogra, P., Feng, A., Flores, M. G., Kautz, J., Xu, D., et al. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3239391 · Cited by 144
“in scanner equipment and acquisition protocols, often spanning different countries, continents, and patient populations [3], [4], [5], [6], [7], [8].”
BackgroundSelf-citationIslam, M., Reza, M. T., Kaosar, M. G., & Parvez, M. Z. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11014-1 · Cited by 120
“Federated Learning (FL) is a recently proposed machine learning technique, which tries to address the problem of strict regulations regarding data secrecy and limited supply of available data sets [11, 33, 34].”
BackgroundReviewing Federated Machine Learning and Its Use in Diseases Prediction
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114
“Cancer Yes No Yes No [132] Same No Yes No [134] Same No Yes No [135] Same No Yes No [136] Same No Yes No [138] Yes No Yes No [139] Yes No Yes No [140] Not available No Yes No [141] Yes No Yes No [142] Not available No Yes No [143] No No Yes No [144] No No Yes No [145] Not available No Yes No”
BackgroundResultInfluentialGenomics and multiomics in the age of precision medicine
Mani, S., Lalani, S. R., & Pammi, M. (2025) · Pediatric Research · doi:10.1038/s41390-025-04021-0 · Cited by 136
“81 Application programming interface (API) and related Substitutable Medical Apps, Reusable Technologies (SMART ® ) platform are showing promise in this process of integration.”
Federated learning: Applications, challenges and future directions
Bharati, S., Mondal, M. R. H., Podder, P., & Prasath, V. B. S. (2022) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-220006 · Cited by 126
“For example, the classification tasks of COVID-19 detections [71–84], cancer diagnoses [85–94] and autism spectrum disorder (ASD) [84,95–97] are considered in a FL setting in healthcare.”
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., Bouridane, A., & Abdulkadir, A. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123123 · Cited by 114
“This concept of data-private collaborative learning [51], that Source data”
BackgroundEdge AI and Blockchain for Smart Sustainable Cities: Promise and Potential
Badidi, E. (2022) · Sustainability · doi:10.3390/su14137609 · Cited by 100
The importance of resource awareness in artificial intelligence for healthcare
Jia, Z., Chen, J., Xu, X., Kheir, J. N., Hu, J., Xiao, H., Peng, S., Hu, X. S., Chen, D., & Shi, Y. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00670-0 · Cited by 93
Application of artificial intelligence in cataract management: current and future directions
Gutiérrez, L., Lim, J. S., Foo, L. L., Ng, W. Y., Yip, M. Y. T., Lim, G. Y. S., Wong, M. M., Fong, A., Rosman, M., Mehta, J. S., et al. (2022) · Eye and Vision · doi:10.1186/s40662-021-00273-z · Cited by 89
“This is a privacy-preserving technique that exposes the model to heterogenous non-independent and identically distributed data [66].”
MethodMedical Imaging Applications of Federated Learning
Sandhu, S. S., Gorji, H. T., Tavakolian, P., Tavakolian, K., & Akhbardeh, A. (2023) · Diagnostics · doi:10.3390/diagnostics13193140 · Cited by 100
“Additionally, Sarma et al. conducted a case study demonstrating their ability to utilize FL to train a deep learning model across three academic institutions while preserving patients’ privacy [92].”
Nguyen, T., Dakka, M. A., Diakiw, S. M., VerMilyea, M. D., Perugini, M., Hall, J. M. M., & Perugini, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-12833-x · Cited by 86
“The field of federated learning has rapidly expanded into the area of healthcare 17–21 , in medical applications in particular 22–25 bringing a wide range of methods for AI training across distributed devices or data silos (horizontal or sample-based federated learning), data centers within an…”
BackgroundSunoqrot, M. R. S., Saha, A., Hosseinzadeh, M., Elschot, M., & Huisman, H. (2022) · European Radiology Experimental · doi:10.1186/s41747-022-00288-8 · Cited by 76
“A successful example of federated training of a prostate segmentation algorithm is reported by Sarma et al. [58].”
MethodMachine learning in prostate MRI for prostate cancer: current status and future opportunities
Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W., & Tan, C. H. (2022) · Diagnostics · doi:10.3390/diagnostics12020289 · Cited by 74
Federated disentangled representation learning for unsupervised brain anomaly detection
Bercea, C. I., Wiestler, B., Rueckert, D., & Albarqouni, S. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00515-2 · Cited by 130
“Second, we augment the original images with up to 3 rectangles of random size s ∈ [ 6 , 500 ] with the width w ∈ [ 3 , 20 ] and height h ∈ [ w − w / 3 , w + w / 3 ] .”
MethodOvercoming data scarcity in biomedical imaging with a foundational multi-task model
Schäfer, R., Nicke, T., Höfener, H., Lange, A., Merhof, D., Feuerhake, F., Schulz, V., Lotz, J., & Kiessling, F. M. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00662-z · Cited by 104
“Differences in imaging modalities, scanners, annotations or patient populations can make models fail when applied to the data of different clinical centers 35 .”
Deep Learning for Cardiovascular Imaging
Wehbe, R. M., Katsaggelos, A. K., Hammond, K. J., Hong, H., Ahmad, F. S., Ouyang, D., Shah, S. J., McCarthy, P. M., & Thomas, J. D. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.3142 · Cited by 75
The Internet of Federated Things (IoFT)
Kontar, R. A., Shi, N., Yue, X., Chung, S., Byon, E., Chowdhury, M., Jin, J. J., Kontar, W., Masoud, N., Nouiehed, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3127448 · Cited by 72
“More recently, federated ML has been applied to the development of multi-hospital models in healthcare [261, 266, 126].”
MethodDeep learning-based artificial intelligence applications in prostate MRI: brief summary
Türkbey, B., & Haider, M. A. (2021) · British Journal of Radiology · doi:10.1259/bjr.20210563 · Cited by 66
Self-citationA Review of Medical Federated Learning: Applications in Oncology and Cancer Research
Chowdhury, A., Kassem, H., Padoy, N., Umeton, R., & Karargyris, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08999-2_1 · Cited by 72
“[ 46] performed prostate image segmentation in a federated setting.”
BackgroundMethodProportionally Fair Hospital Collaborations in Federated Learning of Histopathology Images
Hosseini, S. M., Sikaroudi, M., Babaie, M., & Tizhoosh, H. R. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3234450 · Cited by 64
“The authors in [14] used private prostate MRI images from three institutions and showed the superiority of federated learning in prostate MRI segmentation.”
BackgroundBlika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 82
“Reference [138] demonstrate the use of FL at three different institutions on the medical image analysis of entire prostate segmentation, which is a first step towards magnetic resonance imaging (MRI)-guided cancer diagnosis and fusion-guided therapies.”
In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare
Topaloglu, M. Y., Morrell, E. M., Rajendran, S., & Topaloĝlu, Ü. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.746497 · Cited by 50
“Given the large volume and diverse data needed for model training, Federated Learning (FL) approaches may provide a novel opportunity for the future of ML applications (Rajendran et al., 2021; Sarma et al., 2021).”
BackgroundRong, Y., Chen, Q., Fu, Y., Yang, X., Al‐Hallaq, H., Wu, Q., Yuan, L., Xiao, Y., Cai, B., Latifi, K., et al. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.10.033 · Cited by 58
Ho, T.-T., Tran, K.-D., & Huang, Y. (2022) · Sensors · doi:10.3390/s22103728 · Cited by 44
Privacy-preserving blockchain-based federated learning for brain tumor segmentation
Kumar, R., Bernard, C. M., Ullah, A., Khan, R. U., Kumar, J., Kulevome, D. K. B., Rao, Y., & Zeng, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108646 · Cited by 45
Gupta, S., Kumar, S., Chang, K., Lu, C., Singh, P., & Kalpathy–Cramer, J. (2023) · Radiographics · doi:10.1148/rg.220107 · Cited by 53
Transparency of medical artificial intelligence systems
Kim, C., Gadgil, S. U., & Lee, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00363-w · Cited by 60
The Federated Tumor Segmentation (FeTS) ChallengePreprint
Pati, S., Baid, U., Zenk, M., Edwards, B., Sheller, M., Reina, G. A., Foley, P., Gruzdev, A., Martin, J., Albarqouni, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05874 · Cited by 132
Choudhury, A., Volmer, L., Martin, F. J., Fijten, R., Wee, L., Dekker, A. L. A. J., & van Soest, J. (2024) · JMIR AI · doi:10.2196/60847 · Cited by 44
“Existing literature has shown that FL models can achieve performance comparable to centrally trained models [38,41,45-47].”
FedPerl: Semi-supervised Peer Learning for Skin Lesion Classification
Bdair, T. M., Navab, N., & Albarqouni, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_32 · Cited by 49
Prediction across healthcare settings: a case study in predicting emergency department disposition
Barak‐Corren, Y., Chaudhari, P. P., Perniciaro, J. L., Waltzman, M. L., Fine, A. M., & Reis, B. Y. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00537-x · Cited by 42
Augmenting digital twins with federated learning in medicine
Nagaraj, D., Khandelwal, P., Steyaert, S., & Gevaert, O. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00044-4 · Cited by 41
Mazher, M., Razzak, I., Qayyum, A., Tanveer, M., Beier, S., Khan, T. M., & Niederer, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102256 · Cited by 41
Artificial Intelligence for Dermatopathology: Current Trends and the Road Ahead
Chen, S. B., & Novoa, R. A. (2022) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2022.01.003 · Cited by 37
Federated Multi-Organ Segmentation With Inconsistent Labels
Xu, X., Deng, H., Gateño, J., & Yan, P. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3270140 · Cited by 37
“…barrier of data privacy and realize large-scale DL on isolated data, federated learning (FL) [1], an emerging decentralized learning paradigm, has been adopted in the medical image analysis, solving various clinical problems such as prostate segmentation [2], [3] and COVID-19 diagnosis [4], [5].”
Kumar, R., Waisberg, E., Ong, J., Paladugu, P., Amiri, D., Saintyl, J., Yelamanchi, J., Nahouraii, R., Jagadeesan, R., & Tavakkoli, A. (2024) · Brain Sciences · doi:10.3390/brainsci14121266 · Cited by 52
Advancing breast, lung and prostate cancer research with federated learning. A systematic review
Ankolekar, A., Boie, S. D., Abdollahyan, M., Gadaleta, E., Hasheminasab, S. A., Yang, G., Beauville, C., Δικαίος, Ν., Kastis, G. A., Bussmann, M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01591-5 · Cited by 44
“HorizontalFLwasusedmainlyforclassi fi cation tasks 17,27,29,36,38 , followed by detection 20,23,25 , segmentation 25,26 and regression 29 .”
Baek, S., Jung, E.-Y., Pyo, J., Pachepsky, Y., Son, H., & Cho, K. (2022) · Water Research · doi:10.1016/j.watres.2022.118494 · Cited by 27
Federated Partially Supervised Learning With Limited Decentralized Medical Images
Dong, N., Kampffmeyer, M., Voiculescu, I. D., & Xing, E. P. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3231017 · Cited by 31
“FL is a learning paradigm that aims to utilize decentralized data stored separately in different places and has become a topic of active research in medical image analysis [14], [15], [16], [17], [18], [19].”
BackgroundMulti-institutional PET/CT image segmentation using federated deep transformer learning
Shiri, I., Razeghi, B., Sadr, A. V., Amini, M., Salimi, Y., Ferdowsi, S., Boor, P., Gündüz, D., Voloshynovskiy, S., & Zaidi, H. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107706 · Cited by 35
Developing and deploying deep learning models in brain magnetic resonance imaging: A review
Aggarwal, K., Jimeno, M. M., Ravi, K. S., González, R. G., & Geethanath, S. (2023) · NMR in Biomedicine · doi:10.1002/nbm.5014 · Cited by 36
“53,54 When dealing with multi-institutional data, protocol harmonization can avoid model bias that may arise from differences in image contrast, intensity, or noise distribution.”
BackgroundPrivacy-Enhancing Technologies in Biomedical Data Science
Cho, H., Froelicher, D., Dokmai, N., Nandi, A., Sadhuka, S., Hong, M. M., & Berger, B. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-120423-120107 · Cited by 39
An improved federated deep learning for plant leaf disease detection
Hari, P., Singh, M. P., & Singh, A. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18867-9 · Cited by 29
“Sarma et al. [10] demonstrate FL without centralizing or sharing the physical data of multi-institutions.”
Abbas, S., Asif, M. W., Rehman, A., Alharbi, M., Khan, M. A., & Elmitwally, N. S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36743 · Cited by 34
“Integrating federated learning with emerging technologies such as artificial intelligence, deep learning, and computer vision further enhances the precision and personalization of cancer diagnostics [74].”
BackgroundRetractedNovel technical and privacy-preserving technology for artificial intelligence in ophthalmology
Lim, J. S., Hong, M., Lam, W. S. T., Zhang, Z., Teo, Z. L., Liu, Y., Ng, W. Y., Foo, L. L., & Ting, D. S. W. (2022) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000846 · Cited by 31
“…EMR for COVID-19 mortality prediction [140], and genomics data for the construction of type 2 diabetes polygenic risk scores [ outperformedtraditionalsingle-institutionMLarchi-tectures [148,151–156] and may be comparable to models built via central learning (centralized database) [137,138,143–150].”
MethodPreparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance
Yang, Y., Sun, K., Gao, Y., Wang, K., & Yu, G. (2023) · Diagnostics · doi:10.3390/diagnostics13193115 · Cited by 35
“The National Management Measures for Health Care Big Data Standards, Security and Services (Trial) [50], Health Insurance Portability and Accountability Act (HIPAA) [51] and other related laws should be complied strictly in data collection, storage, usage and disclosure.”
BackgroundRajagopal, A., Redekop, E., Kemisetti, A., Kulkarni, R., Raman, S. S., Sarma, K. V., Magudia, K. A., Arnold, C. W., & Larson, P. E. Z. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.02.012 · Cited by 28
Self-citationApplying Deep Learning-Based Human Motion Recognition System in Sports Competition
Zhang, L. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.860981 · Cited by 26
“Thus, CNN has exerted excellent performance in visual target recognition (Sarma K. V. et al., 2021).”
BackgroundFed2A: Federated Learning Mechanism in Asynchronous and Adaptive Modes
Liu, S., Chen, Q., & You, L. (2022) · Electronics · doi:10.3390/electronics11091393 · Cited by 24
COMMUTE: Communication-efficient transfer learning for multi-site risk prediction
Gu, T., Lee, P. H., & Duan, R. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104243 · Cited by 29
Bi, X., Gupta, A., & Yang, M. (2023) · Management Science · doi:10.1287/mnsc.2023.00611 · Cited by 25
Survey of Medical Applications of Federated Learning
Choi, G., Cha, W. C., Lee, S. U., & Shin, S.-Y. (2024) · Healthcare Informatics Research · doi:10.4258/hir.2024.30.1.3 · Cited by 33
“The remaining five studies explored a variety of radiology images, including mammography [24,47], prostate MRI [23], cardiac MRI [52], and pancreatic CT [25].”
BackgroundReal-world federated learning in radiology: hurdles to overcome and benefits to gain
Bujotzek, M., Akünal, Ü., Denner, S., Neher, P., Zenk, M., Frodl, E., Jaiswal, A., Kim, M., Krekiehn, N. R., Nickel, M., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae259 · Cited by 29
“…legal framework across diverse legal jurisdictions ( ★ ,[13]) →S1: Draft individual legal contracts between the FL initiative and each site ( ★ , [12] ) Step 2: Develop legal framework for sharing of DL models within the FL initiative ( ★ ) - H2: Determine if DL models constitute patient-related…”
BackgroundThe performance of artificial intelligence in prostate magnetic resonance imaging screening
Owida, H. A., Hassan, M. R., Ali, A. M., Alnaimat, F., Sharah, A. A., Abuowaida, S. F. A., & Alshdaifat, N. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i2.pp2234-2241 · Cited by 30
“In order to speed up the construction of models between institutions and provide higher generalizability in clinical application without actual data exchange, alternatives such as federated learning can be highly effective [54].”
BackgroundA Federated Learning Framework for Brain Tumor Segmentation Without Sharing Patient Data
Zhang, W., Jin, W., Rho, S., Jiang, F., & Yang, C. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23147 · Cited by 22
Depression clinical detection model based on social media: a federated deep learning approach
Liu, Y. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05754-7 · Cited by 24
Ali, A., Huang, J., & Jabbar, A. (2025) · IEEE Access · doi:10.1109/access.2025.3562128 · Cited by 21
Insights into artificial intelligence in myopia management: from a data perspective
Zhang, J., & Zou, H. (2023) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-023-06101-5 · Cited by 18
“Federated learning [108] and swarm learning [109] have emerged as potential methods to cope with privacy problems, providing a decentralized and secure method of data management.”
Lopetuso, L. R., Deleu, S., Puca, P., Abreu, M. T., Armuzzi, A., Barbara, G., Caprioli, F., Ng, S. C., Costello, S. P., Damiani, A., et al. (2025) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaf013 · Cited by 23
“37,38 Additionally, AI-driven microbiota analysis may enable predictive modeling to identify biomarkers of treatment response, streamline donor matching, and finally improve therapeutic outcomes.”
Federated Learning for Medical Imaging Segmentation via Dynamic Aggregation on Non-IID Data Silos
Yang, L., He, J., Fu, Y., & Luo, Z. (2023) · Electronics · doi:10.3390/electronics12071687 · Cited by 24
Federated Learning for Diabetic Retinopathy Detection in a Multi-center Fundus Screening Network
Matta, S. C., Hassine, M. B., Lecat, C., Borderie, L., Guilcher, A. L., Massin, P., Cochener, B., Lamard, M., & Quellec, G. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340772 · Cited by 23
Machine learning in metastatic cancer research: Potentials, possibilities, and prospects
Petinrin, O. O., Saeed, F., Toseef, M., Liu, Z., Basurra, S. S., Muyide, I. O., Li, X., Lin, Q., & Wong, K. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.03.046 · Cited by 23
“It has also shown superior performance compared to data from single institution [172].”
ResultExploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture
Abbas, Z., Ahmad, S. F., Syed, M. H., Anjum, A., & Rehman, S. (2023) · IEEE Access · doi:10.1109/access.2023.3344029 · Cited by 23
“Real clinical rectal image data were used to effectively illustrate the capability of FL across three academic institutions in [61].”
MethodTrustworthy machine learning in the context of security and privacy
Upreti, R., Lind, P. G., Elmokashfi, A. M., & Yazidi, A. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00813-3 · Cited by 25
“Similarly, in [105], researchers try to resolve a real-world problem.”
Artificial intelligence in dermatopathology: A systematic review
Lalmalani, R. M., Lim, C. X. Y., & Oh, C. C. (2025) · Clinical and Experimental Dermatology · doi:10.1093/ced/llae361 · Cited by 19
Weimann, K., & Conrad, T. O. F. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3427787 · Cited by 19
“Federated learning has found a variety of applications in medical research: brain tumor segmentation [20], [21], breast cancer classification [22], whole prostate segmentation [23], lung cancer detection [24] and ECG interpretation [25], [26], [27], [28], [29], [30], [31].”
MethodArtificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review
Leone, D. M., O’Sullivan, D. M., & Jaimes, K. B. (2024) · Children · doi:10.3390/children12010025 · Cited by 24
“However, to ensure equitable outcomes, it is essential that AI model training and validation datasets include diverse populations to prevent the reinforcement or exacerbation of racial, ethnic, and geographic disparities in healthcare.”
Chen, Z., Wang, C., & Wang, F. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i24.108021 · Cited by 25
MERGE: A model for multi-input biomedical federated learning
Casella, B., Riviera, W., Aldinucci, M., & Menegaz, G. (2023) · Patterns · doi:10.1016/j.patter.2023.100856 · Cited by 20
“31,32 Due to the high sensitivity of the data, many other works have been performed to explore FL in the biomedical field.”
BackgroundLiu, Y., Huang, J., Chen, J., Chen, W., Pan, Y., & Qiu, J. (2024) · BMC Cancer · doi:10.1186/s12885-024-12456-7 · Cited by 21
“Meanwhile, Federated Learning (FL) [12, 13] has garnered substantial interest within the medical domain as an emerging machine learning technique [14].”
BackgroundPan, J., Fan, Z., Smith, G. E., Guo, Y., Bian, J., & Xu, J. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70128 · Cited by 19
“57 Given that EHRs include patient treatment data In addition, our demographic analyses uncovered regional varia-tionsthatarecrucialforinterpretingthestudyresults.”
Privacy-preserving collaborative machine learning in biomedical applications
Kim, W., & Seok, J. (2022) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic54071.2022.9722703 · Cited by 18
Federated Learning for Medical Imaging: An Updated State of the Art
Mouhni, N., Elkalay, A., Chakraoui, M., Abdali, A., Ammoumou, A., & Amalou, I. (2022) · Ingénierie des systèmes d information · doi:10.18280/isi.270117 · Cited by 20
“Recent works have demonstrated that FL model performance metrics like accuracy, recall and F1-score, are equal or sometimes even better than a single institution trained model [29, 39, 40, 49, 50].”
BackgroundZenk, M., Baid, U., Pati, S., Linardos, A., Edwards, B., Sheller, M., Foley, P., Aristizábal, A., Zimmerer, D., Gruzdev, A. D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60466-1 · Cited by 29
Differentially private federated deep learning for multi-site medical image segmentationPreprint
Ziller, A., Usynin, D., Remerscheid, N. W., Knolle, M. A., Braren, R., Rueckert, D., & Kaissis, G. A. (2022) · Research Square · doi:10.21203/rs.3.rs-1478332/v1 · Cited by 27
Federated learning using model projection for multi-center disease diagnosis with non-IID data
Du, J., Li, W., Liu, P., Vong, C., You, Y., Lei, B., & Wang, T. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106409 · Cited by 18
Federated Learning in Healthcare: From Research to Real-World Deployment
Bakas, S., Li, X., Shah, P., & Roth, H. R. (2026) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-080125-041414 · Cited by 28
Self-citationWang, D., Wang, D., Hu, Q., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123869 · Cited by 15
Provenzano, D., Melnyk, O., Imtiaz, D., McSweeney, B., Nemirovsky, D., Wynne, M., Whalen, M. J., Rao, Y. J., Loew, M. H., & Haji-Momenian, S. (2023) · Applied Sciences · doi:10.3390/app13021088 · Cited by 13
Machine Learning for Hepatocellular Carcinoma Segmentation at MRI:RadiologyIn Training
Raman, A. G., Jones, C. K., & Weiss, C. R. (2022) · Radiology · doi:10.1148/radiol.212386 · Cited by 12
Self-citationContinual learning framework for a multicenter study with an application to electrocardiogram
Kim, J., Lim, M. H., Kim, K., & Yoon, H. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02464-9 · Cited by 13
“In federated learning, models are distributed to be trained, aggregated periodically, and distributed again [5].”
Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare
Liu, C., Fan, H., Kim, M., Zhou, T., Yang, P., Zhao, L., Wang, Y., Che, Z., Liu, C., Li, B., et al. (2026) · Advanced Science · doi:10.1002/advs.202522900 · Cited by 20
Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment
Yuan, S., Guo, L., & Xu, F. (2025) · International Urology and Nephrology · doi:10.1007/s11255-025-04878-4 · Cited by 16
Jaswanth, P. K., Chowdary, P. Y., & Ramprasad, M. V. S. (2023) · Measurement Sensors · doi:10.1016/j.measen.2023.100868 · Cited by 10
Vo, V. T.-T., Shin, T.-H., Yang, H.-J., Kang, S., & Kim, S.-H. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108104 · Cited by 11
Towards practical federated learning and evaluation for medical prediction models
Kazlouski, A., Perez, I. M., Noor, F. A., Högerman, M., Ettala, O. O., Pahikkala, T., & Airola, A. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106046 · Cited by 11
Tsui, J. M. G., Kehayias, C. E., Leeman, J. E., Nguyen, P. L., Peng, L. C., Yang, D. D., Moningi, S., Martin, N. E., Orio, P. F., D'Amico, A. V., et al. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.07.029 · Cited by 9
Sakhawat, A. R., Fatima, A., Abbas, S., Ahmad, M., & Khan, M. A. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-0839-4.ch012 · Cited by 10
Escribà-Montagut, X., Marcon, Y., Anguita‐Ruiz, A., Avraam, D., Urquiza, J., Morgan, A. S., Wilson, B., Burton, P. R., & González, J. R. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012626 · Cited by 10
Harnessing clinical annotations to improve deep learning performance in prostate segmentation
Sarma, K. V., Raman, A. G., Dhinagar, N. J., Priester, A. M., Harmon, S. A., Sanford, T., Mehralivand, S., Türkbey, B., Marks, L. S., Raman, S. S., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253829 · Cited by 10
Self-citationWang, X., He, J., Ding, G., Tang, Y., & Wang, Q. (2026) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s572089 · Cited by 11
“119,120 In pharmacogenomics, federated AI has already demonstrated the feasibility of discovering chemotherapy-response biomarkers across hospitals without centra-lizing raw patient data.”
Metadata
- Authors
- Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., Wood, B. J., Choyke, P. L., Priester, A. M., Marks, L. S., Raman, S. S., Enzmann, D., Turkbey, B., Speier, W., & Arnold, C. W.
- Affiliations
- University of California, Los Angeles (US)
- National Institutes of Health (US)
- SUNY Upstate Medical University (US)
- Nvidia (United States) (US)
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
- Corresponding author
- Arnold, Corey W
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-02-04
- Volume
- 28
- Issue
- 6
- Pages
- 1259-1264
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC8200268
- OpenAlex
- W3128304793
- Semantic Scholar
- ef5abdbf50baa6f719338ffa8a4fe1e41fed0352
- Cited by
- 242
- References
- 25
- Citations by year
- 2026: 36 · 2025: 54 · 2024: 55 · 2023: 49 · 2022: 34 · 2021: 13
- FWCI
- 8.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- NIH NCI (F30CA210329, R21CA220352, P50CA092131, ZIDBC011242, Z1ACL040015, HHSN261200800001E)
- NIH NIGMS (GM08042)
- NIH
- AMA Foundation
- NVIDIA Corporation Academic Hardware Grant
- NIH Center for Interventional Oncology
- Intramural Research Program of the NIH, and a cooperative research and development agreement (CRADA) between NIH and nVIDIA
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, data privacy, data sharing, model generalization, clinical data
- MeSH terms
- Deep Learning, Humans, Privacy, Information Dissemination
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.
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