Pith. sign in

REVIEW 3 major objections 8 minor 65 references

A Language-Signal-Vision Multimodal Framework for Multitask Cardiac Analysis

T0 review · 3 major / 8 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that fusing laboratory tests, ECGs, and echocardiograms in one framework improves cardiac diagnosis and risk prediction, and it releases a patient-aligned multimodal cohort to test this.

desk verdict A serious multimodal cardiac engineering paper with a genuinely useful new dataset, but the headline gains may be inflated by same-admission drug history leaking the HF label; referee it, require a leak-control ablation. read the letter →

arxiv 2508.13072 v1 pith:RRCH6LHF submitted 2025-08-18 cs.AI

Yuting Zhang , Tiantian Geng , Luoying Hao , Xinxing Cheng , Alexander Thorley , Xiaoxia Wang , Wenqi Lu , Sandeep S Hothi
show 4 more authors
Lei Wei Zhaowen Qiu Dipak Kotecha Jinming Duan
This is my paper · ORCID
classification cs.AI
keywords multimodallearningmultitaskheartfailurediagnosisriskstratificationelectrocardiogramechocardiographylaboratorytestsmodelexplainability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a single multimodal architecture can improve cardiac diagnosis and prognosis by fusing laboratory text, 12-lead ECGs, and echocardiogram video, rather than relying on one data type or on coarse feature concatenation. To test this, the authors build HFTri-MIMIC, a patient- and time-aligned cohort of 1,524 MIMIC-IV patients with all three modalities and follow-up for 455 patients. Their TGMM framework uses pretrained modality backbones, a flexible fusion module that gates both shared and modality-specific features, and task-specific text guidance. The core claim is that trimodal fusion raises heart-failure diagnosis to AUC 0.91 and risk-stratification C-index to 0.61, beating single-modality and prior-fusion baselines, and that the same design transfers to a myocardial infarction task. The reader should care because it proposes a reusable data resource and a concrete recipe for how complementary cardiac signals could be integrated in one system.

What carries the argument

The load-bearing mechanism is the MedFlexFusion module: it computes query/key/value projections for each modality, lets one modality query all others, and combines the local fused features with a sigmoid global gate per modality, so the model can decide how much each source contributes. A textual guidance module adds semi-soft textual content, a task-specific human-written query concatenated with learnable tokens, encoded by a one-layer transformer, and uses that content to gate the concatenated specific-plus-shared features. The response module then turns prediction into a comparison of candidate answers with a small transformer decoder. The argument that this machinery works is carried by ablations showing that removing the learned text guidance or replacing the response module with a linear probe degrades performance.

What would settle it

Mask out every medication-history and medical-history token from the Labs text and retrain the model on the same split; if the trimodal AUC falls from 0.91 toward the 0.85 ECG+ECHO baseline, the complementarity claim is an artifact of label leakage.

Watch

Extended reading notes

Core claim

The central discovery, as the paper states it, is that trimodal integration of laboratory test values, ECGs, and echocardiograms improves heart failure diagnosis and risk prediction by up to 10% and 8%, respectively, over single modalities, with the full TGMM model reaching an AUC of 0.91 (95% CI 0.87–0.94), accuracy 0.86, and a C-index of 0.61 ± 0.05 on the HFTri-MIMIC cohort. The paper attributes this gain to a fusion design that takes complementarity seriously: the MedFlexFusion module gives local cross-modal attention plus a global gating signal, so shared and modality-specific features are both retained, and a textual guidance module filters those features toward the current task using a mix of human-written and learned prompts. It further claims that the same framework improves myocardial infarction diagnosis by up to 5% when labs and ECGs are fused, and that the trimodal model remains stable across missing-modality scenarios. Wilcoxon signed-rank tests are reported to support the significance of the modality-combination differences.

Load-bearing premise

The heart-failure label for training comes from ICD codes, and the lab-text input includes the patient's medication and medical history from the same admission; the whole trimodal advantage assumes that this history does not already reveal which patients carry the heart-failure diagnosis.

Editorial extensions

If this is right

  • If TGMM generalizes as reported, a single modular network can cover diagnosis and prognosis with whatever subset of labs, ECG, and echo is available, matching clinical settings where not every test is done for every patient.
  • The ECG+ECHO combination being weaker than other pairs suggests that merely adding modalities is not enough; the fusion mechanism must exploit complementary portions, so future systems should expect pair-specific gains.
  • Because the risk-stratification C-index (0.61) exceeds all compared baselines, the textual-guidance design is a viable direction for prognosis even if absolute predictive strength remains moderate.
  • On the external MI dataset, the same architecture makes lab+ECG diagnosis nearly match ECG-alone performance, implying ECG is the dominant signal for MI while labs add little.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, a direct next test is to run the same fusion on an independent multi-centre cohort with outcomes defined prospectively rather than by ICD codes, which would separate true fusion gains from coding artifacts.
  • Beyond the paper, the explanation figures suggest the model attends to shared Lab-ECHO features most; one testable extension is to ablate only the shared features and measure how much of the 0.91 AUC comes from cross-modal agreement rather than unique signal.
  • Beyond the paper, a deployment-oriented extension would calibrate the risk scores and set a decision threshold, since a C-index of 0.61 is unlikely to change clinical management without calibration.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 8 minor

Summary. The paper introduces HFTri-MIMIC, a curated multimodal dataset from MIMIC-IV containing same-admission laboratory results, 12-lead ECGs, and echocardiograms for 1,524 patients, with a follow-up subset of 455 patients for risk stratification. It then proposes TGMM, an end-to-end framework combining modality-specific foundation encoders, the MedFlexFusion module, a textual guidance module, and a transformer-decoder response module, supporting unimodal, bimodal, and trimodal inputs for heart failure diagnosis, risk prediction, and cross-modal retrieval. The experiments claim that trimodal fusion improves HF diagnosis and risk prediction by up to 10% and 8% over single modalities, that labs plus ECG improves MI diagnosis by up to 5%, and that ablation and explainability analyses support the contribution of each component.

Significance. If the leakage concern is controlled, the paper would make a useful contribution: HFTri-MIMIC is a patient- and time-aligned multimodal resource that appears genuinely absent from the public literature; TGMM is flexible over modality subsets and tasks; the fusion baselines share the same encoders; and external validation on an MI dataset is included. The explainability analyses (attention matrices, SHAP, Grad-CAM++) are also a strength. However, the headline quantitative claims currently rest on an unaddressed label-leakage risk and on significance statements that are internally inconsistent with the reported confidence intervals, so the evidentiary value of the results is substantially weaker than the text asserts.

major comments (3)
  1. [§3.2–3.3, §4.2.1, Fig. 9(a)] The Labs modality includes 'medical history' and 'drug history' from the same admission (§3.3), and §4.2.1 textualizes every column as '<column name> of the <object> is <value>', so these fields are fed directly into Bio-Clinical BERT. Since the diagnosis label is prevalent HF defined by same-admission ICD codes (§3.2), a patient's medication list and medical history are consequences of the target label. The SHAP analysis in Fig. 9(a) indeed shows tokens such as 'antithrombotics' and 'antiplatelets' driving predictions. Without an ablation that removes or permutes these columns, the unimodal Labs AUC of 0.82 and the trimodal AUC of 0.91 in Table 2 cannot be interpreted as evidence of complementary multimodal physiology; the central claim of synergistic fusion may reduce to label leakage. Please rerun the diagnosis experiments (and ideally the risk experiments) excluding medical and drug history, report the resulting metrics, and add an explicit discussion of this risk in the Limitation section.
  2. [§5.3, Table 6] The note 'All results differ significantly under the same modality setting (p<0.05)' is internally contradicted by the table: in the Lab ACC column, the no-TGM linear-response row and the full TGMM row both report 0.77 (0.72, 0.82), so a paired test cannot be significant on those values. More generally, many rows in Table 6 have overlapping 95% confidence intervals, and Section 5.2 reports Wilcoxon signed-rank tests without specifying the pairing units, the number of comparisons, or any multiple-testing correction. Please provide per-comparison p-values, confidence intervals for differences, and a precise description of the test procedure; otherwise the component-wise ablation claims are not supported.
  3. [§5.2, Tables 2, 4, and 5] The prognosis results rest on only 130 incident events (Table 1) with a best C-index of 0.61±0.05, and several reported 'significant' differences (e.g., TGMM trimodal 0.61 vs Janus-Pro 0.54; TGMM Lab+ECHO 0.59 vs Meancat 0.53) have overlapping standard deviations under the repeated two-fold procedure. The paper does not state whether Wilcoxon tests were applied to fold-level metrics, patient-level scores, or repeated runs, nor does it report C-index confidence intervals or event counts by fold. Please specify the exact test procedure and report uncertainty measures compatible with the paired design.
minor comments (8)
  1. [Abstract] The phrase 'relatives weightings' contains a typo and should read 'relative weightings'.
  2. [§5.1] The statement 'all compared approaches employed the same foundation models to ensure a fair comparison' is inaccurate for Qwen2.5-VL and Janus-Pro, which are large multimodal models with their own encoders; please clarify that the claim applies to the fusion baselines that use the same modality encoders as TGMM.
  3. [Table 6] The row labels use checkmarks without an explicit legend, making it hard to tell which ablation configuration each row represents; please add a clear key or describe each row configuration in the caption.
  4. [§4.2.2, Eqs. (3)–(5)] The gating weights αk1, αv1, αk2, αv2 are defined through sums of projections; please specify the dimensions of the projection outputs and how the sums are computed if the K and V projections have different shapes.
  5. [§5.1] C-index values are reported with standard deviations but no confidence intervals; bootstrap confidence intervals would better convey the uncertainty given the small number of incident events.
  6. [§4.2.4] The term 'adversarial comparison' is a suggestive name for comparing candidate likelihoods; please clarify whether any min-max or adversarial training is actually used, since none is described in the loss formulation.
  7. [§3.1–3.2] The phrase 'patient- and time-aligned' should clarify whether each patient contributes exactly one admission or whether multiple admissions per patient are possible, since the exclusion criteria are only summarized in Appendix A.
  8. [§6] The Discussion mentions 'AUC 0.97 vs. 0.96, p<0.05' for Qwen2.5-VL without specifying which modality setting or task this refers to; please tie this statement to Table 4 or state the comparison explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivation chain is self-contained and no prediction reduces to a fitted value or to a load-bearing self-citation.

full rationale

The paper's central claims are empirical evaluations of a proposed fusion architecture against external foundation models (Bio-Clinical BERT, ST-MEM, EchoPrime) and external baselines, all using the same encoders, so the comparisons are not constructed from the paper's own fitted values. The loss functions in Section 4.3 are standard cross-entropy, Cox partial likelihood, margin ranking, and contrastive losses, and the reported hyperparameter analysis in Section 5.4 is a sensitivity check, not a post-hoc fit renamed as a prediction. No uniqueness theorem is imported from the authors' prior work, and the few self-citations (e.g., Zhang et al. 2024b, Gill et al. 2023) appear only as related-work context and are not load-bearing for the framework's claimed contributions. The skeptic's concern that the Labs text modality includes same-admission medical and drug history (Sections 3.3 and 4.2.1), which could leak the prevalent-HF label, is a data-validity and outcome-definition threat rather than a circular derivation: it does not reduce any equation to its own input, and the paper's Limitation section omits this risk, but omitting a limitation is not circularity. Accordingly, the appropriate finding is no significant circularity.

Assumptions & free parameters 7 free parameters · 6 assumptions · 2 invented entities

The central claims rest on ICD-coded labels, same-admission history features, temporal alignment, and the validity of three external encoders and a view classifier. These are all borrowed or assumed rather than demonstrated in this paper. No physics-like constants or invented physical entities are involved.

free parameters (7)
  • margin (margin ranking loss) = not stated
    Appears in Lm (Eq. 15); value not stated in the visible text; it shapes the C-index ranking and is a hand-chosen hyperparameter.
  • loss weights (lambda_lm, lambda_mc, lambda_unlikely, lambda_dig, lambda_r, lambda_m) = not stated
    Weights in Eqs. 10 and 13; values not reported in the main text; chosen by hand.
  • temperature tau (contrastive retrieval loss) = not stated
    Appears in Eq. 16; value not stated.
  • insertion position of Ch within Cl = not stated
    Explicitly called a tunable hyperparameter (Section 4.2.3); Fig. 7 shows robustness to it.
  • number of unfrozen foundation-model layers = 2
    Selected via parameter analysis on the trimodal HF diagnosis setting (Section 5.4, Fig. 7).
  • stochastic depth drop probability p_drop = not stated
    Used in Eq. 9; value not stated.
  • learnable modality gating weights (alpha_k1, alpha_v1, alpha_k2, alpha_v2, Gi) = trained
    Learned during training (Eqs. 4, 5, 6); not hand-set, so these are ordinary trainable parameters rather than experimenter-chosen constants.
assumptions (6)
  • domain assumption ICD-9/10 codes accurately identify prevalent and incident heart failure
    Section 3.2 labels patients 'classified based on ICD-9/10 codes'; the paper itself notes in Section 2.1 that administrative-code outcomes require expert interpretation, yet no clinician adjudication is described.
  • domain assumption Same-admission medical and drug history do not leak the prevalent-HF label
    Section 3.3 includes medical history and drug history in the Labs text input; because the diagnosis label refers to the same admission, these features can be consequences of the label. No leakage test is reported.
  • domain assumption Records from the same hospital stay are temporally aligned and clinically contemporaneous
    Section 3.2 selects records from the same admission 'therefore ensuring temporal alignment'; labs, ECG, and echo can occur days apart within an admission, and the paper does not report the time gaps.
  • domain assumption Foundation encoders (Bio-Clinical BERT, ST-MEM, EchoPrime) give valid representations for these inputs
    Section 4.2.1 adopts three pretrained encoders; the paper relies on their generalization to MIMIC-derived data without a fine-tuning or domain-shift analysis.
  • domain assumption The EchoCLIP view classifier correctly identifies the 11 echo views
    Section 3.3 uses 'a view classifier Christensen et al. (2024)' to select views from raw DICOM; view misclassification would alter the echo features and the reported missingness pattern.
  • standard math Standard ML loss objects are valid here (cross-entropy, Cox partial likelihood, InfoNCE)
    Eqs. 11, 14, 16 assume standard loss formulations from Katzman et al. and contrastive learning; these are unproved background results treated as given.
invented entities (2)
  • Semi-Soft Textual Content (SSTC) with learnable tokens (Cl)
    purpose: Task-specific conditioning combining human-defined prompts with trainable tokens in the textual guidance module
    A new model component whose benefit is shown only by in-paper ablations (Table 6); it has no external falsifiable handle, so independent_evidence is False.
  • Adversarial comparison response decoding
    purpose: Reformulates classification as comparing likelihoods of candidate answers in a decoder
    A design choice (Section 4.2.4); its superiority is asserted via ablation against a linear prober, with no external validation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Language-Signal-Vision Multimodal Framework for Multitask Cardiac Analysis." pith.science (2026). https://pith.science/paper/RRCH6LHF

@misc{pith2026250813072,
  author       = {Pith},
  title        = {Pith review of: A Language-Signal-Vision Multimodal Framework for Multitask Cardiac Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RRCH6LHF}},
  note         = {Machine review of arXiv:2508.13072}
}
read the original abstract

Contemporary cardiovascular management involves complex consideration and integration of multimodal cardiac datasets, where each modality provides distinct but complementary physiological characteristics. While the effective integration of multiple modalities could yield a holistic clinical profile that accurately models the true clinical situation with respect to data modalities and their relatives weightings, current methodologies remain limited by: 1) the scarcity of patient- and time-aligned multimodal data; 2) reliance on isolated single-modality or rigid multimodal input combinations; 3) alignment strategies that prioritize cross-modal similarity over complementarity; and 4) a narrow single-task focus. In response to these limitations, a comprehensive multimodal dataset was curated for immediate application, integrating laboratory test results, electrocardiograms, and echocardiograms with clinical outcomes. Subsequently, a unified framework, Textual Guidance Multimodal fusion for Multiple cardiac tasks (TGMM), was proposed. TGMM incorporated three key components: 1) a MedFlexFusion module designed to capture the unique and complementary characteristics of medical modalities and dynamically integrate data from diverse cardiac sources and their combinations; 2) a textual guidance module to derive task-relevant representations tailored to diverse clinical objectives, including heart disease diagnosis, risk stratification and information retrieval; and 3) a response module to produce final decisions for all these tasks. Furthermore, this study systematically explored key features across multiple modalities and elucidated their synergistic contributions in clinical decision-making. Extensive experiments showed that TGMM outperformed state-of-the-art methods across multiple clinical tasks, with additional validation confirming its robustness on another public dataset.

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

65 extracted references · 16 canonical work pages

  1. [1]

    , author Falcone, G.J

    author Acosta, J.N. , author Falcone, G.J. , author Rajpurkar, P. , author Topol, E.J. , year 2022 . title Multimodal biomedical ai . journal Nature medicine. volume 28 , pages 1773--1784 . :https://doi.org/10.1038/s41591-022-01981-2

    doi:10.1038/s41591-022-01981-2 2022

  2. [2]

    , author Al-Roub, N

    author Akerman, A.P. , author Al-Roub, N. , author Angell-James, C. , author Cassidy, M.A. , author Thompson, R. , author Bosque, L. , author Rainer, K. , author Hawkes, W. , author Piotrowska, H. , author Leeson, P. , et al., year 2025 . title External validation of artificial intelligence for detection of heart failure with preserved ejection fraction ....

    work page doi:10.1038/s41467-025-58283-7 2025

  3. [3]

    , author Donahue, J

    author Alayrac, J.B. , author Donahue, J. , author Luc, P. , author Miech, A. , author Barr, I. , author Hasson, Y. , author Lenc, K. , author Mensch, A. , author Millican, K. , author Reynolds, M. , et al., year 2022 . title Flamingo: a visual language model for few-shot learning . journal Advances in neural information processing systems. volume 35 , pa...

    arXiv doi:10.48550/arxiv.2204.14198 2022

  4. [4]

    , author Murphy, J

    author Alsentzer, E. , author Murphy, J. , author Boag, W. , author Weng, W.H. , author Jindi, D. , author Naumann, T. , author McDermott, M. , year 2019 . title Publicly available clinical bert embeddings , in: booktitle Proceedings of the 2nd Clinical Natural Language Processing Workshop. , pp. pages 72--78 . :https://doi.org/10.48550/arXiv.1904.03323

    arXiv doi:10.48550/arxiv.1904.03323 2019

  5. [5]

    , author Dashtban, A

    author Banerjee, A. , author Dashtban, A. , author Chen, S. , author Pasea, L. , author Thygesen, J.H. , author Fatemifar, G. , author Tyl, B. , author Dyszynski, T. , author Asselbergs, F.W. , author Lund, L.H. , et al., year 2023 . title Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external,...

    work page doi:10.1016/s2589-750 2023

  6. [7]

    , author Li, L

    author Chen, C. , author Li, L. , author Beetz, M. , author Banerjee, A. , author Gupta, R. , author Grau, V. , year 2025 a. title Large language model-informed ecg dual attention network for heart failure risk prediction . journal IEEE Transactions on Big Data. volume 11 , pages 948--960 . :https://doi.org/10.1109/TBDATA.2025.3536922

    work page arXiv 2025

  7. [8]

    , author Wu, Z

    author Chen, X. , author Wu, Z. , author Liu, X. , author Pan, Z. , author Liu, W. , author Xie, Z. , author Yu, X. , author Ruan, C. , year 2025 b. title Janus-pro: Unified multimodal understanding and generation with data and model scaling . journal arXiv preprint arXiv:2501.17811. :https://doi.org/10.48550/arXiv.2501.17811

    arXiv doi:10.48550/arxiv.2501.17811 2025

  8. [9]

    , author Zou, B

    author Chen, Z. , author Zou, B. , author Dai, Y. , author Zhu, C. , author Kong, G. , author Zhang, W. , year 2023 . title Medical visual question answering with symmetric interaction attention and cross-modal gating . journal Biomedical Signal Processing and Control. volume 85 , pages 105049 . :https://doi.org/10.1016/j.bspc.2023.105049

    arXiv 2023

Show all 65 references
  1. [10]

    , author Vukadinovic, M

    author Christensen, M. , author Vukadinovic, M. , author Yuan, N. , author Ouyang, D. , year 2024 . title Vision--language foundation model for echocardiogram interpretation . journal Nature Medicine. volume 30 , pages 1481--1488 . :https://doi.org/10.1038/s41591-024-02959-y

    2024 doi

  2. [11]

    , author Xia, F

    author Driess, D. , author Xia, F. , author Sajjadi, M.S. , author Lynch, C. , author Chowdhery, A. , author Wahid, A. , author Tompson, J. , author Vuong, Q. , author Yu, T. , author Huang, W. , et al., year 2023 . title Palm-e: An embodied multimodal language model . journal...

    2023 doi arXiv

  3. [12]

    , author Liu, H

    author Geng, X. , author Liu, H. , author Lee, L. , author Schuurmans, D. , author Levine, S. , author Abbeel, P. , year 2022 . title Multimodal masked autoencoders learn transferable representations , in: booktitle First Workshop on Pre-training: Perspectives, Pitfalls, and P...

    2022 doi arXiv

  4. [13]

    , author Ouyang, D

    author Ghorbani, A. , author Ouyang, D. , author Abid, A. , author He, B. , author Chen, J.H. , author Harrington, R.A. , author Liang, D.H. , author Ashley, E.A. , author Zou, J.Y. , year 2020 . title Deep learning interpretation of echocardiograms . journal NPJ digital medic...

    2020 doi

  5. [14]

    , author Karwath, A

    author Gill, S.K. , author Karwath, A. , author Uh, H.W. , author Cardoso, V.R. , author Gu, Z. , author Barsky, A. , author Slater, L. , author Acharjee, A. , author Duan, J. , author Dall'Olio, L. , et al., year 2023 . title Artificial intelligence to enhance clinical value ...

    2023 doi

  6. [15]

    , author Tinn, R

    author Gu, Y. , author Tinn, R. , author Cheng, H. , author Lucas, M. , author Usuyama, N. , author Liu, X. , author Naumann, T. , author Gao, J. , author Poon, H. , year 2022 . title Domain-specific language model pretraining for biomedical natural language processing . journ...

    2022 doi

  7. [16]

    , author Califf, R.M

    author Harrell, F.E. , author Califf, R.M. , author Pryor, D.B. , author Lee, K.L. , author Rosati, R.A. , year 1982 . title Evaluating the yield of medical tests . journal Jama. volume 247 , pages 2543--2546 . :https://doi.org/10.1001/jama.247.18.2543

    1982 doi

  8. [17]

    , author Fan, H

    author He, K. , author Fan, H. , author Wu, Y. , author Xie, S. , author Girshick, R. , year 2020 . title Momentum contrast for unsupervised visual representation learning , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. , pp. ...

    2020

  9. [18]

    , author Buendia, A

    author Hegselmann, S. , author Buendia, A. , author Lang, H. , author Agrawal, M. , author Jiang, X. , author Sontag, D. , year 2023 . title Tabllm: Few-shot classification of tabular data with large language models , in: booktitle International Conference on Artificial Intell...

    2023 doi arXiv

  10. [19]

    , author Bozkurt, B

    author Heidenreich, P.A. , author Bozkurt, B. , author Aguilar, D. , author Allen, L.A. , author Byun, J.J. , author Colvin, M.M. , author Deswal, A. , author Drazner, M.H. , author Dunlay, S.M. , author Evers, L.R. , et al., year 2022 a. title 2022 aha/acc/hfsa guideline for ...

    2022

  11. [20]

    , author Bozkurt, B

    author Heidenreich, P.A. , author Bozkurt, B. , author Aguilar, D. , author Allen, L.A. , author Byun, J.J. , author Colvin, M.M. , author Deswal, A. , author Drazner, M.H. , author Dunlay, S.M. , author Evers, L.R. , et al., year 2022 b. title 2022 aha/acc/hfsa guideline for ...

    2022

  12. [21]

    , author Zeng, M.L

    author Hong, Y. , author Zeng, M.L. , year 2023 . title International classification of diseases (icd) . journal KO Knowledge Organization. volume 49 , pages 496--528 . :https://doi.org/10.5771/0943-7444-2022-7-496

    2023 doi

  13. [22]

    , author Li, C

    author Huang, W. , author Li, C. , author Zhou, H.Y. , author Yang, H. , author Liu, J. , author Liang, Y. , author Zheng, H. , author Zhang, S. , author Wang, S. , year 2024 . title Enhancing representation in radiography-reports foundation model: A granular alignment algorit...

    2024 doi

  14. [23]

    , author Bianchi, F

    author Huang, Z. , author Bianchi, F. , author Yuksekgonul, M. , author Montine, T.J. , author Zou, J. , year 2023 . title A visual--language foundation model for pathology image analysis using medical twitter . journal Nature medicine. volume 29 , pages 2307--2316 . :https://...

    2023 doi

  15. [24]

    , author Kim, W

    author Jin, Q. , author Kim, W. , author Chen, Q. , author Comeau, D.C. , author Yeganova, L. , author Wilbur, W.J. , author Lu, Z. , year 2023 . title Medcpt: Contrastive pre-trained transformers with large-scale pubmed search logs for zero-shot biomedical information retriev...

    2023 doi

  16. [25]

    , author Bulgarelli, L

    author Johnson, A. , author Bulgarelli, L. , author Pollard, T. , author Gow, B. , author Moody, B. , author Horng, S. , author Celi, L. , author Mark, R. , year 2024 . title Mimic-iv (version 3.1). physionet . :https://doi.org/10.13026/kpb9-mt58

    2024 doi

  17. [26]

    , author Bulgarelli, L

    author Johnson, A.E. , author Bulgarelli, L. , author Shen, L. , author Gayles, A. , author Shammout, A. , author Horng, S. , author Pollard, T.J. , author Hao, S. , author Moody, B. , author Gow, B. , et al., year 2023 . title Mimic-iv, a freely accessible electronic health r...

    2023 doi

  18. [27]

    , author Shaham, U

    author Katzman, J.L. , author Shaham, U. , author Cloninger, A. , author Bates, J. , author Jiang, T. , author Kluger, Y. , year 2018 . title Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network . journal BMC medical research...

    2018 doi

  19. [28]

    , author Smistad, E

    author Leclerc, S. , author Smistad, E. , author Pedrosa, J. , author stvik, A. , author Cervenansky, F. , author Espinosa, F. , author Espeland, T. , author Berg, E.A.R. , author Jodoin, P.M. , author Grenier, T. , et al., year 2019 . title Deep learning for segmentation usin...

    2019

  20. [29]

    , author Selvaraju, R

    author Li, J. , author Selvaraju, R. , author Gotmare, A. , author Joty, S. , author Xiong, C. , author Hoi, S.C.H. , year 2021 . title Align before fuse: Vision and language representation learning with momentum distillation . journal Advances in neural information processing...

    2021 doi arXiv

  21. [30]

    , author Holliday, J

    author Littlejohns, T.J. , author Holliday, J. , author Gibson, L.M. , author Garratt, S. , author Oesingmann, N. , author Alfaro-Almagro, F. , author Bell, J.D. , author Boultwood, C. , author Collins, R. , author Conroy, M.C. , et al., year 2020 . title The uk biobank imagin...

    2020 doi

  22. [31]

    , author Cheng, S

    author Liu, C. , author Cheng, S. , author Chen, C. , author Qiao, M. , author Zhang, W. , author Shah, A. , author Bai, W. , author Arcucci, R. , year 2023 . title M-flag: Medical vision-language pre-training with frozen language models and latent space geometry optimization ...

    2023 doi

  23. [32]

    , author Fan, Y

    author Liu, X. , author Fan, Y. , author Li, S. , author Chen, M. , author Li, M. , author Hau, W.K. , author Zhang, H. , author Xu, L. , author Lee, A.P.W. , year 2021 . title Deep learning-based automated left ventricular ejection fraction assessment using 2-d echocardiograp...

    2021

  24. [33]

    , author Chen, B

    author Lu, M.Y. , author Chen, B. , author Williamson, D.F. , author Chen, R.J. , author Liang, I. , author Ding, T. , author Jaume, G. , author Odintsov, I. , author Le, L.P. , author Gerber, G. , et al., year 2024 . title A visual-language foundation model for computational ...

    2024 doi

  25. [34]

    , author Lee, S.I

    author Lundberg, S.M. , author Lee, S.I. , year 2017 . title A unified approach to interpreting model predictions . journal Advances in neural information processing systems. volume 30 . :https://doi.org/10.48550/arXiv.1705.07874

    2017 doi arXiv

  26. [36]

    , author Mark, R.G

    author Moody, G.B. , author Mark, R.G. , year 2001 . title The impact of the mit-bih arrhythmia database . journal IEEE engineering in medicine and biology magazine. volume 20 , pages 45--50 . :https://doi.org/10.1109/51.932724

    2001 doi

  27. [37]

    , author Kaissis, G

    author M \"u ller, P. , author Kaissis, G. , author Zou, C. , author Rueckert, D. , year 2022 . title Joint learning of localized representations from medical images and reports , in: booktitle European conference on computer vision. , pp. pages 685--701 . :https://doi.org/10....

    2022 doi

  28. [38]

    , author Park, M

    author Na, Y. , author Park, M. , author Tae, Y. , author Joo, S. , year 2024 . title Guiding masked representation learning to capture spatio-temporal relationship of electrocardiogram , in: booktitle The Twelfth International Conference on Learning Representations. https://o...

    2024

  29. [39]

    , year 2024

    author Organization., W.H. , year 2024 . title Ethics and governance of artificial intelligence for health: large multi-modal models. WHO guidance . :https://coilink.org/20.500.12592/3xsj8mz

    2024

  30. [40]

    , author He, B

    author Ouyang, D. , author He, B. , author Ghorbani, A. , author Yuan, N. , author Ebinger, J. , author Langlotz, C.P. , author Heidenreich, P.A. , author Harrington, R.A. , author Liang, D.H. , author Ashley, E.A. , et al., year 2020 . title Video-based ai for beat-to-beat as...

    2020 doi

  31. [41]

    , author Bontempi, D

    author Pai, S. , author Bontempi, D. , author Hadzic, I. , author Prudente, V. , author Soka c , M. , author Chaunzwa, T.L. , author Bernatz, S. , author Hosny, A. , author Mak, R.H. , author Birkbak, N.J. , et al., year 2024 . title Foundation model for cancer imaging biomark...

    2024 doi

  32. [42]

    , author Shazeer, N

    author Raffel, C. , author Shazeer, N. , author Roberts, A. , author Lee, K. , author Narang, S. , author Matena, M. , author Zhou, Y. , author Li, W. , author Liu, P.J. , year 2020 . title Exploring the limits of transfer learning with a unified text-to-text transformer . jou...

    2020 doi arXiv

  33. [43]

    , author Lopez, L

    author Reddy, C.D. , author Lopez, L. , author Ouyang, D. , author Zou, J.Y. , author He, B. , year 2023 . title Video-based deep learning for automated assessment of left ventricular ejection fraction in pediatric patients . journal Journal of the American Society of Echocard...

    2023 doi

  34. [44]

    , author Cogswell, M

    author Selvaraju, R.R. , author Cogswell, M. , author Das, A. , author Vedantam, R. , author Parikh, D. , author Batra, D. , year 2017 . title Grad-cam: Visual explanations from deep networks via gradient-based localization , in: booktitle Proceedings of the IEEE international...

    2017 doi

  35. [46]

    , author Mathivanan, G

    author Sherly, S.I. , author Mathivanan, G. , year 2023 . title An efficient honey badger based faster region cnn for chronc heart failure prediction . journal Biomedical Signal Processing and Control. volume 79 , pages 104165 . :https://doi.org/10.1016/j.bspc.2022.104165

    2023

  36. [47]

    , author Qiu, Y.L

    author Steyaert, S. , author Qiu, Y.L. , author Zheng, Y. , author Mukherjee, P. , author Vogel, H. , author Gevaert, O. , year 2023 . title Multimodal deep learning to predict prognosis in adult and pediatric brain tumors . journal Communications Medicine. volume 3 , pages 44...

    2023 doi

  37. [48]

    , author Kajdanowicz, T

    author Szyma \'n ski, P. , author Kajdanowicz, T. , year 2017 . title A network perspective on stratification of multi-label data , in: booktitle First International Workshop on Learning with Imbalanced Domains: Theory and Applications. , pp. pages 22--35 . :https://doi.org/10...

    2017 doi arXiv

  38. [49]

    , author Guan, C

    author Tjoa, E. , author Guan, C. , year 2020 . title A survey on explainable artificial intelligence (xai): Toward medical xai . journal IEEE transactions on neural networks and learning systems. volume 32 , pages 4793--4813 . :https://doi.org/10.1109/TNNLS.2020.3027314

    2020

  39. [50]

    , year 2019

    author Topol, E.J. , year 2019 . title High-performance medicine: the convergence of human and artificial intelligence . journal Nature medicine. volume 25 , pages 44--56 . :https://doi.org/10.1038/s41591-018-0300-7

    2019 doi

  40. [51]

    , author Rohr, K

    author Vale-Silva, L.A. , author Rohr, K. , year 2021 . title Long-term cancer survival prediction using multimodal deep learning . journal Scientific Reports. volume 11 , pages 13505 . :https://doi.org/10.1038/s41598-021-92799-4

    2021 doi

  41. [52]

    , author Bozkurt, A

    author Vorontsov, E. , author Bozkurt, A. , author Casson, A. , author Shaikovski, G. , author Zelechowski, M. , author Severson, K. , author Zimmermann, E. , author Hall, J. , author Tenenholtz, N. , author Fusi, N. , et al., year 2024 . title A foundation model for clinical-...

    2024 doi

  42. [53]

    , author Tang, X

    author Vukadinovic, M. , author Tang, X. , author Yuan, N. , author Cheng, P. , author Li, D. , author Cheng, S. , author He, B. , author Ouyang, D. , year 2024 . title Echoprime: A multi-video view-informed vision-language model for comprehensive echocardiography interpretati...

    2024 doi arXiv

  43. [54]

    , author Strodthoff, N

    author Wagner, P. , author Strodthoff, N. , author Bousseljot, R.D. , author Kreiseler, D. , author Lunze, F.I. , author Samek, W. , author Schaeffter, T. , year 2020 . title Ptb-xl, a large publicly available electrocardiography dataset . journal Scientific data. volume 7 , p...

    2020 doi

  44. [55]

    , author Bai, S

    author Wang, P. , author Bai, S. , author Tan, S. , author Wang, S. , author Fan, Z. , author Bai, J. , author Chen, K. , author Liu, X. , author Wang, J. , author Ge, W. , et al., year 2024 a. title Qwen2-vl: Enhancing vision-language model's perception of the world at any re...

    2024 doi arXiv

  45. [56]

    , author Zhao, J

    author Wang, X. , author Zhao, J. , author Marostica, E. , author Yuan, W. , author Jin, J. , author Zhang, J. , author Li, R. , author Tang, H. , author Wang, K. , author Li, Y. , et al., year 2024 b. title A pathology foundation model for cancer diagnosis and prognosis predi...

    2024 doi

  46. [57]

    , year 2019

    author Weintraub, W.S. , year 2019 . title Role of big data in cardiovascular research . journal Journal of the American Heart Association. volume 8 , pages e012791 . :https://doi.org/10.1161/JAHA.119.012791

    2019 doi

  47. [58]

    , author P \"o lsterl, S

    author Wolf, T.N. , author P \"o lsterl, S. , author Wachinger, C. , author Initiative, A.D.N. , et al., year 2022 . title Daft: A universal module to interweave tabular data and 3d images in cnns . journal NeuroImage. volume 260 , pages 119505 . :https://doi.org/10.1016/j.neu...

    2022

  48. [59]

    , author Grant, I

    author Woodfield, R. , author Grant, I. , author Group, U.B.S.O. , author Follow-Up, U.B. , author Group, O.W. , author Sudlow, C.L. , year 2015 . title Accuracy of electronic health record data for identifying stroke cases in large-scale epidemiological studies: a systematic ...

    2015 doi

  49. [60]

    , author Wu, C.H

    author Xie, T. , author Wu, C.H. , author Shi, P. , author Zhong, R. , author Scholak, T. , author Yasunaga, M. , author Wu, C.S. , author Zhong, M. , author Yin, P. , author Wang, S.I. , et al., year 2022 . title Unifiedskg: Unifying and multi-tasking structured knowledge gro...

    2022 doi

  50. [61]

    , author Usuyama, N

    author Xu, H. , author Usuyama, N. , author Bagga, J. , author Zhang, S. , author Rao, R. , author Naumann, T. , author Wong, C. , author Gero, Z. , author Gonz \'a lez, J. , author Gu, Y. , et al., year 2024 . title A whole-slide foundation model for digital pathology from re...

    2024 doi

  51. [62]

    , author Pei, M

    author Yan, B. , author Pei, M. , year 2022 . title Clinical-bert: Vision-language pre-training for radiograph diagnosis and reports generation , in: booktitle Proceedings of the AAAI Conference on Artificial Intelligence. , pp. pages 2982--2990 . :https://doi.org/10.1609/aaai...

    2022 doi

  52. [63]

    , author Axmed, M

    author Ye, Q. , author Axmed, M. , author Pryzant, R. , author Khani, F. , year 2024 . title Prompt engineering a prompt engineer . journal the Findings of the Association for Computational Linguistics (ACL). :https://arxiv.org/abs/2311.05661

    2024 arXiv

  53. [64]

    , author Xu, H

    author Yi, X. , author Xu, H. , author Zhang, H. , author Tang, L. , author Ma, J. , year 2024 . title Text-if: Leveraging semantic text guidance for degradation-aware and interactive image fusion , in: booktitle Proceedings of the IEEE/CVF Conference on Computer Vision and Pa...

    2024

  54. [65]

    , author Deng, J

    author Yu, H. , author Deng, J. , author Nathan, R. , author Kr \"o schel, M. , author Pekarsky, S. , author Li, G. , author Klaassen, M. , year 2021 . title An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers . journal Movemen...

    2021 doi

  55. [66]

    , author Zhou, R

    author Zhang, K. , author Zhou, R. , author Adhikarla, E. , author Yan, Z. , author Liu, Y. , author Yu, J. , author Liu, Z. , author Chen, X. , author Davison, B.D. , author Ren, H. , et al., year 2024 a. title A generalist vision--language foundation model for diverse biomed...

    2024 doi

  56. [67]

    , author Liu, B

    author Zhang, Y. , author Liu, B. , author Bunting, K.V. , author Brind, D. , author Thorley, A. , author Karwath, A. , author Lu, W. , author Zhou, D. , author Wang, X. , author Mobley, A.R. , et al., year 2024 b. title Development of automated neural network prediction for e...

    2024

  57. [68]

    , author Chen, H

    author Zhou, F. , author Chen, H. , year 2023 . title Cross-modal translation and alignment for survival analysis , in: booktitle Proceedings of the IEEE/CVF International Conference on Computer Vision. , pp. pages 21485--21494 . :https://doi.org/10.1109/ICCV51070.2023.01964

    2023

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.