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REVIEW 5 major objections 6 minor 27 references

Architecting Clinical Collaboration: Multi-Agent Reasoning Systems for Multimodal Medical VQA

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper argues that emulating clinical consultation and reference-checking in software preserves accuracy across distribution shift, reaching about 70% on unseen data where the best single model fell to 37%.

desk verdict Systematic comparison with a useful fine-tuning negative result, but the central architectural claim is confounded by a missing Gemini-only control. read the letter →

arxiv 2507.05520 v3 pith:4YUAHFPO submitted 2025-07-07 cs.AI

Karishma Thakrar , Shreyas Basavatia , Akshay Daftardar This is my paper
classification cs.AI
keywords medicalvisualquestionansweringmulti-agentreasoningretrieval-augmentedgenerationclinicalworkflowemulationdermatologyvision-languagemodelsfine-tuningvsarchitecturetelemedicine
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 is trying to show that the standard recipe for medical AI—fine-tuning a vision-language model on domain data—is neither necessary nor sufficient, and that an alternative recipe works better: assemble several pre-trained models and route their answers through a structured reasoning process that mimics how clinicians consult colleagues and check references. On a noisy telemedicine dermatology benchmark, the authors report that the best single model fell from 67% validation accuracy to 37% on unseen test data, while their Reasoning Layer held 70.62% and their Agentic RAG system 69.24%, with no fine-tuning at all. Fine-tuning made four of seven models worse, by an average of 30%. If these results hold, medical AI deployment could shift from expensive domain adaptation to inference-time architectures that are transparent, updateable, and robust to messy real-world data.

What carries the argument

The Reasoning Layer is a three-stage peer-consultation emulator run by an instruction-following language model: it first extracts standardized visual features across ten clinical dimensions from each image, then parses patient descriptions into thirteen structured categories, and finally synthesizes those with the seven model predictions, treating the predictions as advisory rather than votes. The Agentic RAG system is a reference-checking pipeline with specialized agents for image analysis, clinical context, diagnosis extraction, knowledge retrieval, evidence integration, decision synthesis, self-reflection, and re-analysis; a hybrid BM25 plus semantic-search retrieval over an 800-entry dermatology knowledge base, reranked by a cross-encoder, supplies literature grounding, and a self-reflection step is triggered when confidence falls below 0.75. The load-bearing mechanism in both is the same: diverse, imperfect model predictions are combined by a separate reasoning model rather than averaged, and that combination is guided by explicit clinical structure. Pairwise agreement among the seven models ranged from 17.5% to 51.6%, which the paper identifies as the diversity that lets errors cancel instead of compounding.

What would settle it

Run the staged image-analysis, context-extraction, and evidence-synthesis prompts with Gemini 2.5 Flash directly on DermaVQA-DAS test encounters, omitting the seven model predictions and all retrieval; if that single-model run scores near the 70% achieved by the Reasoning Layer, the architectures' advantage over a well-prompted model collapses.

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Extended reading notes

Core claim

The authors' central claim is that an AI system for medical visual question answering can be made robust to distribution shift by wiring together pre-trained models the way a clinical team works, rather than by fine-tuning on domain data. On the DermaVQA-DAS dataset, the best baseline vision-language model (Qwen2.5-VL-7B) scored 67.21% on validation but collapsed to 37.43% on the unseen test split. Both clinical-inspired architectures held steady: the Reasoning Layer scored 70.62% test accuracy and the Agentic RAG system 69.24%, each using the same seven models' predictions as advisory inputs and requiring no additional training. Fine-tuning by contrast degraded four of the seven models, with an average 30% decrease, and feeding those degraded predictions into the architectures slightly hurt their performance. Accuracy is scored with partial credit for overlapping answers, $\frac{|P\cap G|}{\max(|P|,|G|)}$, reflecting the multi-label nature of dermatology questions. The paper reads these results as evidence that reconstructing collaborative, evidence-checked clinical reasoning in software is a stronger direction than parameter adaptation for medical VQA.

Load-bearing premise

The claim hinges on the multi-agent structure, not the underlying Gemini 2.5 Flash model, being what produces the 70% accuracy; the paper reports no Gemini-only run with the same staged prompts and no retrieval, so that attribution is untested.

Editorial extensions

If this is right

  • Deploying pre-trained open vision-language models inside these two wrappers can reach about 70% test accuracy on this benchmark without spending 70 GPU-hours on fine-tuning.
  • Because the architecture treats model outputs as advisory inputs, individual models can be added, removed, or upgraded without retraining the ensemble.
  • The knowledge base can be refreshed or swapped, so new dermatological guidance can enter the system at inference time rather than through a retraining cycle.
  • Feeding fine-tuned predictions into the architectures lowered accuracy slightly, so the benefit of fine-tuning disappears once a reasoning layer is present.
  • Both architectures produce confidence scores and literature-grounded explanations, which the paper argues is the property that makes a medical AI system usable in a clinical workflow.

Reading between the lines

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

  • An extension the paper leaves untested: give Gemini 2.5 Flash the same staged image-analysis and context-extraction prompts with the seven model predictions and retrieval removed; if it already scores near 70%, the architecture's contribution is prompt structure rather than collaboration.
  • The partial-ensemble ablations (58.9–59.9% for two to four models versus 69–70% for all seven) suggest an ensemble-size curve the paper does not characterize, so measuring accuracy as a function of the number of constituent models would show whether more models keep helping or plateau.
  • The gap between objective questions (84–87% for itching, location, and onset) and subjective visual questions (47–56% for extent, morphology, and color) points to the remaining bottleneck being perceptual discrimination rather than reasoning, which implies stronger vision encoders may help more than additional reasoning agents.
  • The authors report that retrieval did not always improve answer accuracy but did improve explanation quality; an evaluation framework that ignores explanation usefulness would therefore undervalue the agentic RAG system's main advantage.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The manuscript proposes that clinical-inspired multi-agent architectures, rather than fine-tuning, improve medical visual question answering under distribution shift. Seven open-source VLMs are evaluated in six configurations: baseline, LoRA fine-tuned, and each augmented with a multi-model Reasoning Layer or an Agentic RAG system that uses gemini-2.5-flash-preview for staged image analysis, context extraction, evidence synthesis, and optional retrieval from a curated dermatology knowledge base. On the DermaVQA-DAS dataset, the Reasoning Layer achieves 70.62% test accuracy and Agentic RAG achieves 69.24%, versus 37.43% for the single best baseline model. The authors conclude that architectural emulation of collaborative clinical reasoning is a robust alternative to fine-tuning. The paper also reports ablation results for ensemble size, self-reflection, and confidence thresholds, and provides detailed qualitative traces of the reasoning process.

Significance. If the central claim were supported, the result would be practically significant: it would offer a fine-tuning-free path to robust medical VQA with explainable, literature-grounded outputs, and it would connect AI system design to clinical collaboration literature. The manuscript has notable strengths: the code is publicly available, the evaluation covers multiple open models and multiple configurations, the partial-credit metric for multi-label questions is clearly defined, and the appendix traces show a nontrivial reasoning and self-correction process. However, the central attribution of the 70% accuracy to the multi-agent architecture is not established by the current experimental design, because the architecture's reasoning is entirely performed by a proprietary frontier model that is never evaluated alone with the same staged prompts. The significance is therefore conditional on a control experiment that the paper does not provide.

major comments (5)
  1. [§V, first paragraph] The central claim is unsupported because no control evaluates gemini-2.5-flash-preview in the same staged prompting pipeline (Stage 1 image analysis, Stage 2 context extraction, Stage 3 evidence synthesis) without the seven model predictions and without retrieval. The only evidence about Gemini alone is two anecdotal contradictory outputs (ENC00858, ENC00868), not a measured accuracy. Since the Reasoning Layer and Agentic RAG are implemented entirely with Gemini 2.5 Flash, the 70.62% and 69.24% test accuracies could reflect Gemini's base capability or the prompts rather than the multi-agent structure. Add a Gemini-only control with identical prompts and report per-question accuracy; without it, the paper's main conclusion does not follow.
  2. [§V, Table I] The statement that 'baseline models collapsed on test data' is supported only by Qwen2.5-VL-7B, which drops from 67.21% validation accuracy to 37.43% test accuracy. Table III reports validation accuracy for all seven baselines, but no test accuracy is reported for the other six. The Abstract and Discussion compare the architectures against 'the best baseline model's test performance' using a single model. Report test accuracy for all seven baseline models, or restrict the collapse claim to the one measured model.
  3. [Table II footnote] The exclusion of fine-tuned Qwen2.5-VL-7B from test inference is post hoc. The footnote says it was excluded 'due to overfitting (see Table IV)', but Table IV shows validation accuracy dropping from 67.21% to 38.12%—a validation-set degradation, not a measured test-set result. Removing the strongest baseline model from the fine-tuned-prediction ensemble makes Table II more favorable to the architectures by construction. Provide a pre-specified exclusion criterion, or include the model's test result and discuss it.
  4. [§V.A] The confidence threshold of 0.75 for self-reflection was selected after testing thresholds 0.6–0.9, and the accuracy at the chosen threshold (69.2%) is then reported as validating the architectural choice. Selecting a hyperparameter on the test set inflates the reported performance and makes this ablation circular. Use a held-out validation split for threshold selection, or present the full threshold sweep as a sensitivity analysis with a corrected accounting of the selection procedure.
  5. [§IV.C.1 and Reference [23]] The knowledge base is described as a 'curated medical knowledge base containing 800+ dermatology entries from the American Academy of Dermatology', but reference [23] is 'brucewayne0459, Skin diseases and care, 2024', which is not an American Academy of Dermatology publication. The provenance of the retrieval corpus is load-bearing for the Agentic RAG results and for the claim of literature-grounded outputs. Correct the citation or describe the actual source of the knowledge base entries.
minor comments (6)
  1. [Abstract and §V] The abstract's 'average 30% decrease' is ambiguous: Table I shows a 29.78 percentage-point drop for Qwen2.5-VL-7B, which is a 44.3% relative decrease. Please specify which quantity is meant.
  2. [§III] The train/validation/test split sizes and the split criterion for the 300 patient encounters in DermaVQA-DAS are not stated. This information is needed to interpret the distribution-shift results.
  3. [Throughout] No error bars, confidence intervals, or significance tests are reported, and all results appear to be single-run evaluations. Given the small dataset and the large reported differences, at least a bootstrap interval would strengthen the comparison.
  4. [Figure 3 and Figure 6] The computation of pairwise agreement rates involving 'augmented architectures' is not defined in the text or captions. It is unclear whether the agreement is between the architecture's final answer and ground truth, or between the architecture's answer and each model's prediction.
  5. [Related Work] There is a typo in Section II: 'these reamin fragmented' should be 'these remain fragmented'.
  6. [Appendix, Tables V and VI] The qualitative traces in Tables V and VI are informative, but the main text does not state whether these examples were selected as representative or were chosen because they demonstrate the desired behavior.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the reported accuracies are empirical measurements, and no load-bearing claim reduces to its own inputs by construction.

full rationale

Walking the paper's derivation chain, the only formal derivations are the standard autoregressive LoRA loss (Section IV.A) and the Jaccard-style partial-credit metric (Section IV.D); these are definitions, not predictions. The central result, Reasoning Layer 70.62% and Agentic RAG 69.24% test accuracy versus 37.43% for the best open-source baseline, is an empirical measurement on held-out test data, not a quantity forced by the inputs. The architectures use Gemini 2.5 Flash as an aggregator, but the paper does not define 'clinical-inspired architecture' in terms of the outcome, and no self-citation, uniqueness theorem, or ansatz-citation is load-bearing. The main interpretive weakness is the absence of a Gemini-2.5-Flash-only control with the same staged prompts, which is a confounding and attribution concern rather than circularity. Likewise, tuning the self-reflection threshold to 0.75 on validation data weakens the ablation claim but does not make the reported test accuracy a tautology. No fitted parameter is renamed as a prediction, and no result is equivalent to its inputs by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities or theoretical constructs. Its free parameters are hyperparameters and unspecified adaptive weights. The main unstated assumptions are about dataset label quality, the evaluation metric, and the unmeasured contribution of the proprietary Gemini model in the proposed architectures.

free parameters (3)
  • Confidence threshold for self-reflection = 0.75
    Chosen after experiments over the range 0.6 to 0.9 showed lower performance at other values (Section V.A). This is a hand-tuned hyperparameter that affects when the self-reflection agent triggers.
  • Adaptive task-specific weights in Evidence Integration Agent
    Section IV.C.2 says the system uses adaptive task-specific weights to emphasize visual cues, clinical history, or literature depending on question type, but the values or the adaptation procedure are not specified. This is an unstated free parameter that could influence results.
  • LoRA and training hyperparameters = rank=8, alpha=16, dropout=0.05, epochs=3, learning rate=1e-4
    These are hand-chosen standard values (Section IV.A). They are not fitted to the target accuracy but directly affect the fine-tuning outcomes, which are a key comparison in the paper.
assumptions (4)
  • domain assumption DermaVQA-DAS ground-truth labels are correct and complete
    The entire evaluation depends on the challenge dataset's labels as the reference standard. The paper does not provide clinician verification or error analysis of the labels.
  • domain assumption The Jaccard-based partial-credit metric is an appropriate measure of clinical accuracy
    The paper defines accuracy as |P intersect G| / max(|P|, |G|) in Section IV.D and treats this as a meaningful clinical performance measure, which is a modeling choice that may overstate agreement.
  • domain assumption Gemini 2.5 Flash is a reliable reasoner for dermatology images and clinical text
    Both proposed architectures rely on Gemini for image analysis, context extraction, and synthesis (Sections IV.B-C), but the paper does not validate Gemini's outputs independently or compare with a Gemini-only baseline.
  • domain assumption The curated medical knowledge base accurately represents American Academy of Dermatology content
    Section IV.C.1 claims the knowledge base contains 800+ entries from the AAD, but reference [23] is a user handle 'brucewayne0459' rather than an official AAD source, so the provenance and accuracy of the medical content are unsupported.

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Cite this review

Pith. "Pith review of Architecting Clinical Collaboration: Multi-Agent Reasoning Systems for Multimodal Medical VQA." pith.science (2026). https://pith.science/paper/4YUAHFPO

@misc{pith2026250705520,
  author       = {Pith},
  title        = {Pith review of: Architecting Clinical Collaboration: Multi-Agent Reasoning Systems for Multimodal Medical VQA},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4YUAHFPO}},
  note         = {Machine review of arXiv:2507.05520}
}
read the original abstract

Dermatological care via telemedicine often lacks the rich context of in-person visits. Clinicians must make diagnoses based on a handful of images and brief descriptions, without the benefit of physical exams, second opinions, or reference materials. While many medical AI systems attempt to bridge these gaps with domain-specific fine-tuning, this work hypothesized that mimicking clinical reasoning processes could offer a more effective path forward. This study tested seven vision-language models on medical visual question answering across six configurations: baseline models, fine-tuned variants, and both augmented with either reasoning layers that combine multiple model perspectives, analogous to peer consultation, or retrieval-augmented generation that incorporates medical literature at inference time, serving a role similar to reference-checking. While fine-tuning degraded performance in four of seven models with an average 30% decrease, baseline models collapsed on test data. Clinical-inspired architectures, meanwhile, achieved up to 70% accuracy, maintaining performance on unseen data while generating explainable, literature-grounded outputs critical for clinical adoption. These findings demonstrate that medical AI succeeds by reconstructing the collaborative and evidence-based practices fundamental to clinical diagnosis.

Figures

Figures reproduced from arXiv: 2507.05520 by the authors.

Figure 1
Figure 1. Example from DermaVQA-DAS dataset (ENC00006, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Agentic RAG architecture featuring a multi-stage pipeline with agents that process queries, encounter data, and model [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Pairwise agreement rates (%) among baseline model [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Average model performance versus architectural enhancements on validation data. Radar plots show mean accuracy [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Individual model performance on validation data before (left) and after (right) fine-tuning. Radar plots reveal mixed [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Pairwise agreement rates (%) among finetuned model predictions, augmented architectures, and ground truth labels. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

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Works this paper leans on

27 extracted references · 20 canonical work pages

  1. [23]

    Accessed: May 25, 2025

    brucewayne0459, Skin diseases and care, 2024. Accessed: May 25, 2025

    work page 2024

  2. [1]

    A Comprehensive Review on Synergy of Multi- Modal Data and AI Technologies in Medical Diagnosis

    Xu, X.; Li, J.; Zhu, Z.; Zhao, L.; Wang, H.; Song, C.; Chen, Y .; Zhao, Q.; Yang, J.; Pei, Y . A Comprehensive Review on Synergy of Multi- Modal Data and AI Technologies in Medical Diagnosis. Bioengineering 2024, 11(3), 219

    work page 2024

  3. [2]

    Chiou, A.S., Omiye, J.A., Gui, H., Swetter, S.M., Ko, J.M., Gastman, B., Arbesman, J., & Novoa, R.A. (2025). Multimodal Image Dataset for AI-Based Skin Cancer (MIDAS) Benchmarking. NEJM AI , 2(6)

    work page 2025

  4. [3]

    Investigating the catastrophic forgetting in multimodal large language models,

    Y . Zhai, S. Tong, X. Li, M. Cai, Q. Qu, Y . J. Lee, and Y . Ma, “Investigating the catastrophic forgetting in multimodal large language models,” arXiv preprint arXiv:2309.10313 , 2023

    arXiv 2023

  5. [4]

    L., Boddupalli, D., Nundy, S., et al

    Barnett, M. L., Boddupalli, D., Nundy, S., et al. (2019). Comparative Accuracy of Diagnosis by Collective Intelligence of Multiple Physicians vs Individual Physicians. JAMA Network Open , 2(3), e190096

    work page 2019

  6. [5]

    Col- laborative clinical reasoning: a scoping review

    Lee, C.-Y ., Lai, H.-Y ., Lee, C.-H., Chen, M.-M., and Yau, S.-Y . Col- laborative clinical reasoning: a scoping review. PeerJ, 12:e17042, 2024. PMID: 38464754, PMCID: PMC10924455

    work page 2024

  7. [6]

    K. D. Mashoudy, S. M. Perez, and K. Nouri, From diagnosis to intervention: a review of telemedicine’s role in skin cancer care,Archives of Dermatological Research , vol. 316, article no. 139, 2024

    work page 2024

  8. [7]

    C., Okobi, O

    Ezeamii, V . C., Okobi, O. E., Wambai-Sani, H., Perera, G. S., Zaynieva, S., Okonkwo, C. C., Ohaiba, M. M., William-Enemali, P. C., Obodo, O. R., and Obiefuna, N. G. Revolutionizing healthcare: How telemedicine is improving patient outcomes and expanding access to care. Cureus, 16(7):e63881, July 2024. PMID: 39099901, PMCID: PMC11298029

    work page 2024

Show all 27 references
  1. [8]

    Anderson, C

    J. Anderson, C. Bice, and L. Manzione, Message Burden 2025: Striking a Balance Between Physician Accessibility & Message Burden, KLAS Arch Collaborative Report , KLAS Research, Mar. 2025

    2025

  2. [9]

    It’s time to bench the medical exam benchmark,

    I. D. Raji, R. Daneshjou, and E. Alsentzer, “It’s time to bench the medical exam benchmark,” NEJM AI , vol. 2, no. 2, p. AIe2401235, 2025

    2025

  3. [10]

    Primiero, Gisele Gargantini Rezze, Liam J

    Clare A. Primiero, Gisele Gargantini Rezze, Liam J. Caffery, Cristina Carrera, Sebastian Podlipnik, Natalia Espinosa, Susana Puig, Monika Janda, H. Peter Soyer, and Josep Malvehy. A narrative review: Op- portunities and challenges in artificial intelligence skin image analyses...

    2024

  4. [11]

    Advancing multimodal medical capabilities of Gemini,

    L. Yang, S. Xu, A. Sellergren, et al., “Advancing multimodal medical capabilities of Gemini,” arXiv preprint arXiv:2405.03162 , 2024

    2024 arXiv

  5. [12]

    SkinGPT-4: An interactive dermatology diagnostic system with visual large language model,

    J. Zhou, X. He, L. Sun, J. Xu, X. Chen, Y . Chu, L. Zhou, X. Liao, B. Zhang, and X. Gao, “SkinGPT-4: An interactive dermatology diagnostic system with visual large language model,” arXiv preprint arXiv:2304.10691, 2023

    2023 arXiv

  6. [13]

    MedCoT: Medical chain of thought via hierarchical expert,

    J. Liu, Y . Wang, J. Du, J. T. Zhou, and Z. Liu, “MedCoT: Medical chain of thought via hierarchical expert,” arXiv preprint arXiv:2412.13736 ,

    arXiv

  7. [14]

    Karthikesalingam and V

    A. Karthikesalingam and V . Natarajan, AMIE: A research AI system for diagnostic medical reasoning and conversations, Google Research Blog, Jan. 2024

    2024

  8. [15]

    Analyzing diagnostic reasoning of vision–language models via zero-shot chain-of-thought prompting in medical visual question answering,

    F. T. J. Faria, L. H. Baniata, A. Choi, and S. Kang, “Analyzing diagnostic reasoning of vision–language models via zero-shot chain-of-thought prompting in medical visual question answering,” Mathematics, vol. 13, no. 14, p. 2322, 2025

    2025

  9. [16]

    Diagnostic reasoning prompts reveal the potential for large language model inter- pretability in medicine,

    T. Savage, A. Nayak, R. Gallo, E. Rangan, and J. H. Chen, “Diagnostic reasoning prompts reveal the potential for large language model inter- pretability in medicine,” npj Digital Med. , vol. 7, p. 20, 2024

    2024

  10. [17]

    Benchmarking retrieval- augmented generation for medicine,

    G. Xiong, Q. Jin, Z. Lu, and A. Zhang, “Benchmarking retrieval- augmented generation for medicine,” arXiv preprint arXiv:2402.13178 , 2024

    2024 arXiv

  11. [18]

    Rationale-guided retrieval aug- mented generation for medical question answering,

    J. Sohn, Y . Park, C. Yoon, et al., “Rationale-guided retrieval aug- mented generation for medical question answering,” arXiv preprint arXiv:2411.00300, 2024

    2024

  12. [19]

    Med- FrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning

    Yu, S.; Wang, H.; Wu, J.; Xie, C.; Zhou, Y . Med- FrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning. arXiv preprint arXiv:2505.16964 , 2025. Project: https://ucsc-vlaa.github.io/MedFrameQA/. Dataset: https://huggingface.co/datasets/SuhaoYu1020/MedFrameQA

    2025

  13. [20]

    Sushil, M.; Zack, T.; Mandair, D.; Zheng, Z.; Wali, A.; Yu, Y .-N.; Quan, Y .; Butte, A. J. A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification. Res Sq [Preprint], Feb. 6, 2024. Version 1. PMID...

    2024

  14. [21]

    DermaVQA-DAS: Dermatology assessment schema (DAS) and datasets for closed-ended question answering and segmentation in patient-generated dermatology images,

    W.-w. Yim, Y . Fu, A. Ben Abacha, M. Yetisgen, N. Codella, R. A. Novoa, and J. Malvehy, “DermaVQA-DAS: Dermatology assessment schema (DAS) and datasets for closed-ended question answering and segmentation in patient-generated dermatology images,” CoRR, 2025

    2025

  15. [22]

    Accessed: May 26, 2025

    Google DeepMind, Gemini, 2024. Accessed: May 26, 2025

    2024

  16. [24]

    Evidence extraction to validate medical claims in fake news detection,

    P. Deka, A. Jurek-Loughrey, et al., “Evidence extraction to validate medical claims in fake news detection,” in Int. Conf. Health Inf. Sci. , 2022, pp. 3–15

    2022

  17. [25]

    Reimers and I

    N. Reimers and I. Gurevych, Cross-encoder for MS Marco: MiniLM- L6-v2, 2021. Accessed: May 25, 2025. APPENDIX Fig. 4: Average model performance versus architectural enhancements on validation data. Radar plots show mean accuracy across all baseline models (left) and all fine-t...

    2021

  18. [27]

    Initial Reasoning The question asks for the best label(s) to describe the affected area based on the provided evidence

    highlight the importance of breaking the itch-scratch cycle, which appears crucial based on the visual findings of excoriation. Initial Reasoning The question asks for the best label(s) to describe the affected area based on the provided evidence. I need to review the ‘SKIN DE...

  19. [2024]

    Presented at EMNLP 2024

    2024

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Reviewed August 6, 2026 · model on record in the stance chip above.