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arxiv: 2606.08982 · v1 · pith:2LGLIX2Vnew · submitted 2026-06-08 · 💻 cs.AI

Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care

Aiyuan Yang , Chengfeng Dou , Da Pan , Dian Wang , Fan Yang , Fei Deng , Fei Li , Guangwei Ai
show 19 more authors
Hui Liu Hongda Zhang Jinyang Tai Kai Lu Lijun Liu Linwei Chen Linyu Li Meiqing Guo Peidong Guo Qiang Ju Rihui Xin Shuai Wang XinKai Ma Xudong Chen Yichuan Mo Canbin Piao Leyi Pan Yihe Luo Zian Wang
This is my paper

Pith reviewed 2026-06-27 16:57 UTC · model grok-4.3

classification 💻 cs.AI
keywords medical agent systemcontinuous carereinforcement learninghallucination reductionmultimodal medical AIclinical toolslong-term memory
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The pith

Baichuan-M4 coordinates agents, memory, and tools to deliver continuous medical care with a 3.3% hallucination rate.

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

The paper presents Baichuan-M4 as a medical large model built for ongoing patient care rather than isolated questions. It organizes the system around a runtime harness that aligns training with deployment, a reasoning model trained via continuous-care reinforcement learning, and a layer of clinical tools for memory, evidence retrieval, and image analysis. Evaluations across knowledge tests, simulated consultations, long-context memory, document processing, and multimodal understanding show leading performance. A sympathetic reader would care because most current medical AIs stop at single-turn answers and lack mechanisms for sustained clinical tracking.

Core claim

Baichuan-M4 is built as a coordinated medical agent system around three pillars: Baichuan-Harness, a unified runtime that keeps reinforcement-learning training and real-world deployment consistent while enforcing action constraints, tool use, long-term patient memory, and multi-agent coordination; a core reasoning model trained with a continuous-care reinforcement-learning framework that integrates span-level reward modeling, reasoning-path compression, curriculum learning, and stabilized policy optimization; and a clinical tool layer for patient-memory management, authoritative evidence-based retrieval, and multimodal medical perception across documents, X-rays, and dermatology.

What carries the argument

Baichuan-Harness, a unified runtime that aligns reinforcement-learning training with deployment while enforcing constraints, tool use, long-term memory, and multi-agent coordination.

If this is right

  • Leads in static medical knowledge and safety benchmarks.
  • Outperforms on dynamic OSCE-style consultations.
  • Maintains accurate long-context clinical memory across interactions.
  • Achieves strong results in evidence-based retrieval and multimodal image understanding.
  • Reduces hallucination rate to 3.3% on the reported suite.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same agent structure could support tracking of chronic conditions across repeated encounters without resetting context each time.
  • If the memory and coordination mechanisms hold, the system might reduce reliance on separate human review for routine follow-ups.
  • The training approach of combining span-level rewards with policy stabilization could be tested on non-medical domains that also require long-horizon agent behavior.

Load-bearing premise

The cross-dimensional evaluation suite and reported hallucination rate accurately measure real-world clinical safety and performance without post-hoc selection or undisclosed test-set leakage.

What would settle it

An independent clinical deployment study that records actual hallucination rates and decision errors when the system manages real patient cases over multiple visits.

Figures

Figures reproduced from arXiv: 2606.08982 by Aiyuan Yang, Canbin Piao, Chengfeng Dou, Da Pan, Dian Wang, Fan Yang, Fei Deng, Fei Li, Guangwei Ai, Hongda Zhang, Hui Liu, Jinyang Tai, Kai Lu, Leyi Pan, Lijun Liu, Linwei Chen, Linyu Li, Meiqing Guo, Peidong Guo, Qiang Ju, Rihui Xin, Shuai Wang, Xinkai Ma, Xudong Chen, Yichuan Mo, Yihe Luo, Zian Wang.

Figure 1
Figure 1. Figure 1: Baichuan-Harness, the unified runtime foundation for both training and deployment. Baichuan-M4 is not only a language model, but also a highly coordinated medical agent system [4–6]. As shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The three-layer nested adaptive control system, where continuous improvement is driven by real-world feedback. and correctly connect patient context. The model can integrate the current input with the patient’s prior disease course. For example, when a patient first reports “cough and low￾grade fever,” then uploads a complete blood count and provides medication feedback several days later, the model can re… view at source ↗
Figure 3
Figure 3. Figure 3: Static medical knowledge and safety metrics across the four models, shown in Baichuan’s brand palette. Higher is better for the three HealthBench panels, while lower is better for the hallucination rate; Baichuan-M4 leads every knowledge metric and attains the lowest hallucination rate (3.3%). • Clear lead in complex decision-making: On the HealthBench Hard subset [21], which evaluates complex reasoning in… view at source ↗
Figure 4
Figure 4. Figure 4: Dynamic clinical consultation and long-context memory capacity across models (higher is better). Baichuan-M4 leads on both Scan-Bench V1 and V2 and shows its widest margin on long-context clinical memory, reaching 86.9 versus 79.4–81.7 for the other models. • Sustained leadership in dynamic consultation: In Scan-Bench V1/V2 scenarios, where the model must actively ask questions and perform step-by-step dif… view at source ↗
Figure 5
Figure 5. Figure 5: reports the evidence-based medicine results on Baichuan-EBM. 55 60 65 70 75 80 85 Core Score (higher is better) 40 50 60 70 80 90 Citation Precision (higher is better) Qwen-3.5 Plus (60.4, 43.8) H + (71.5, 52.6) OpenEvidence (74.1, 55.9) GPT-5.5 (79.2, 54.7) Baichuan-M4 (Ours) (83.1, 90.0) Evidence-Based Medicine on Baichuan-EBM [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Baichuan-M4 is Baichuan Intelligence's clinical-grade medical large model, designed for \emph{continuous care} rather than single-turn medical question answering. It is built as a coordinated medical agent system around three pillars: \textbf{Baichuan-Harness}, a unified runtime that keeps reinforcement-learning training and real-world deployment consistent while enforcing action constraints, tool use, long-term patient memory, and multi-agent coordination; a \textbf{core reasoning model} trained with a continuous-care reinforcement-learning framework that integrates span-level reward modeling (SPAR++), reasoning-path compression, curriculum learning, and stabilized policy optimization; and a \textbf{clinical tool layer} for patient-memory management, authoritative evidence-based retrieval, and multimodal medical perception across documents, X-rays, and dermatology. On a cross-dimensional medical evaluation suite, Baichuan-M4 attains leading results in static medical knowledge and safety, dynamic OSCE-style consultation, long-context clinical memory, evidence-based retrieval, medical document OCR, and multimodal image understanding, while lowering the hallucination rate to 3.3\%.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript presents Baichuan-M4 as a clinical-grade medical agent system for continuous care, built around three pillars: Baichuan-Harness (a unified runtime enforcing constraints, tool use, and multi-agent coordination), a core reasoning model trained via continuous-care RL with SPAR++ span-level rewards, reasoning-path compression, curriculum learning, and stabilized policy optimization, and a clinical tool layer for patient memory, evidence retrieval, and multimodal perception (documents, X-rays, dermatology). It claims leading performance on a cross-dimensional evaluation suite covering static medical knowledge/safety, dynamic OSCE-style consultations, long-context clinical memory, evidence-based retrieval, medical document OCR, and multimodal image understanding, while achieving a 3.3% hallucination rate.

Significance. If the reported results prove robust under independent scrutiny, the integration of RL training with long-term memory and constrained tool use could represent a meaningful step toward deployable medical agents that handle ongoing patient care rather than isolated queries. The emphasis on consistency between training and deployment via the harness is a positive design choice. However, the current lack of verifiable evaluation details prevents any assessment of whether these advances translate to genuine improvements in clinical safety or reliability.

major comments (3)
  1. [Abstract] Abstract: The central claims of 'leading results' across six evaluation dimensions and a 3.3% hallucination rate are presented without any baselines, comparison systems, dataset descriptions, sample sizes, error bars, or statistical tests. This absence directly undermines the ability to evaluate the reported superiority or the reliability of the hallucination figure.
  2. [Abstract] Abstract: The cross-dimensional medical evaluation suite is invoked as the basis for all performance claims, yet no information is supplied on its construction, data sources, train/test splits, overlap checks with training corpora, exclusion criteria, or adjudication protocol for the hallucination rate (e.g., span-level vs. response-level, automated vs. human). These omissions are load-bearing because the weakest assumption identified is precisely that the suite measures real-world clinical performance without leakage or post-hoc selection.
  3. [Abstract] Abstract: The description of the core reasoning model references SPAR++, curriculum learning, and stabilized policy optimization, but supplies no equations, hyperparameter values, or ablation results showing how these components contribute to the reported outcomes. Without such grounding, the training framework cannot be assessed as a substantive advance.
minor comments (1)
  1. [Abstract] The abstract employs boldface for component names but does not define acronyms such as SPAR++ or OSCE on first use; the full manuscript should ensure consistent first-use definitions and a glossary if these terms are non-standard.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful reading and valuable feedback. The comments correctly identify that the abstract, as currently written, lacks sufficient supporting details to allow independent evaluation of the claims. We will revise the abstract to incorporate key baselines, evaluation-suite characteristics, and references to the technical sections. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claims of 'leading results' across six evaluation dimensions and a 3.3% hallucination rate are presented without any baselines, comparison systems, dataset descriptions, sample sizes, error bars, or statistical tests. This absence directly undermines the ability to evaluate the reported superiority or the reliability of the hallucination figure.

    Authors: We agree that the abstract must supply enough context for the performance claims to be assessed. The full baselines, comparison systems, dataset sizes, and statistical tests appear in Sections 4 and 5. We will revise the abstract to name the primary baselines, report sample sizes for each dimension, and note that all improvements are statistically significant (p < 0.01) under the tests described in the paper. revision: yes

  2. Referee: [Abstract] Abstract: The cross-dimensional medical evaluation suite is invoked as the basis for all performance claims, yet no information is supplied on its construction, data sources, train/test splits, overlap checks with training corpora, exclusion criteria, or adjudication protocol for the hallucination rate (e.g., span-level vs. response-level, automated vs. human). These omissions are load-bearing because the weakest assumption identified is precisely that the suite measures real-world clinical performance without leakage or post-hoc selection.

    Authors: We accept the point. Section 3.2 contains the full construction protocol, data sources, splits, overlap verification (no training-data leakage), exclusion criteria, and the span-level human adjudication procedure used for the 3.3% hallucination figure. We will add a concise sentence to the abstract that summarizes these safeguards and explicitly directs readers to Section 3.2. revision: yes

  3. Referee: [Abstract] Abstract: The description of the core reasoning model references SPAR++, curriculum learning, and stabilized policy optimization, but supplies no equations, hyperparameter values, or ablation results showing how these components contribute to the reported outcomes. Without such grounding, the training framework cannot be assessed as a substantive advance.

    Authors: The equations for SPAR++, the hyperparameter schedule, curriculum stages, and ablation results are provided in Sections 2.3 and 4.3. Because abstracts are length-limited, we will revise the abstract to include a single sentence that highlights the novel elements (span-level reward modeling and reasoning-path compression) and refers readers to the technical sections for equations and ablations. revision: yes

Circularity Check

0 steps flagged

No circularity: paper reports empirical results without derivations or self-referential predictions

full rationale

The paper describes an agent system architecture and reports performance numbers on an evaluation suite, but contains no equations, first-principles derivations, fitted parameters presented as predictions, or load-bearing self-citations that reduce claims to inputs by construction. The listed circularity patterns (self-definitional, fitted-input-called-prediction, uniqueness theorems, ansatz smuggling, renaming) do not apply because no mathematical chain exists to inspect. The 3.3% hallucination figure is presented as a measured outcome rather than a derived result that collapses to its own inputs. This is the normal case of an empirical systems paper whose central claims rest on external benchmark performance rather than internal definitional equivalence.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no mathematical derivations, fitted constants, or postulated entities; the ledger is therefore empty.

pith-pipeline@v0.9.1-grok · 5821 in / 1228 out tokens · 23156 ms · 2026-06-27T16:57:19.149099+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

34 extracted references · 16 canonical work pages · 6 internal anchors

  1. [1]

    Baichuan-M1: Pushing the Medical Capability of Large Language Models

    Bingning Wang, Haizhou Zhao, Huozhi Zhou, et al. “Baichuan-M1: Pushing the Medical Capability of Large Language Models”. In:CoRRabs/2502.12671 (2025).doi:10.48550/ARXIV.2502.12671. arXiv:2502.12671.url: https://doi.org/10.48550/arXiv.2502.12671

    work page doi:10.48550/arxiv.2502.12671 2025

  2. [2]

    Arsene Fansi Tchango, Rishab Goel, Zhi Wen, Julien Martel, and Joumana Ghosn

    Chengfeng Dou, Chong Liu, Fan Yang, et al. “Baichuan-M2: Scaling Medical Capability with Large Verifier System”. In:CoRRabs/2509.02208 (2025).doi:10.48550/ARXIV.2509.02208. arXiv:2509.02208.url: https://doi.org/10.48550/arXiv.2509.02208

    work page doi:10.48550/arxiv.2509.02208 2025

  3. [3]

    M3 Team, Chengfeng Dou, Fan Yang, et al.Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making. 2026. arXiv:2602.06570 [cs.CL].url:https://arxiv.org/abs/2602.06570

    arXiv 2026

  4. [4]

    Towards Conversational Diagnostic AI

    Tao Tu, Anil Palepu, Mike Schaekermann, et al. “Towards Conversational Diagnostic AI”. In:CoRR abs/2401.05654 (2024).doi:10.48550/ARXIV.2401.05654. arXiv:2401.05654.url: https://doi.org/10.48550/arXiv.2401.05654

    work page doi:10.48550/arxiv.2401.05654 2024

  5. [5]

    Towards physician-centered oversight of conversational diagnostic AI

    Elahe Vedadi, David G. T. Barrett, Natalie Harris, et al. “Towards physician-centered oversight of conversational diagnostic AI”. In:CoRRabs/2507.15743 (2025).doi:10.48550/ARXIV.2507.15743. arXiv:2507.15743.url: https://doi.org/10.48550/arXiv.2507.15743

    work page doi:10.48550/arxiv.2507.15743 2025

  6. [6]

    Jie Liu, Wenxuan Wang, Zizhan Ma, et al.Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence. 2025. arXiv:2412.01605 [cs.CL].url:https://arxiv.org/abs/2412.01605

    arXiv 2025

  7. [7]

    Preprint, arXiv:2508.12790

    Zenan Huang, Yihong Zhuang, Guoshan Lu, et al. “Reinforcement Learning with Rubric Anchors”. In:CoRR abs/2508.12790 (2025).doi:10.48550/ARXIV.2508.12790. arXiv:2508.12790.url: https://doi.org/10.48550/arXiv.2508.12790. 15 Baichuan-M4 Model Card

    work page doi:10.48550/arxiv.2508.12790 2025

  8. [8]

    Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains

    Anisha Gunjal, Anthony Wang, Elaine Lau, Vaskar Nath, Bing Liu, and Sean Hendryx. “Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains”. In:CoRRabs/2507.17746 (2025).doi: 10.48550/ARXIV.2507.17746. arXiv:2507.17746.url:https://doi.org/10.48550/arXiv.2507.17746

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2507.17746 2025

  9. [9]

    Curriculum learning

    Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. “Curriculum learning”. In:Proceedings of the 26th annual international conference on machine learning. 2009, pp. 41–48

    2009

  10. [10]

    Dcpo: Dynamic clipping policy optimization.arXiv preprint arXiv:2509.02333, 2025

    Shihui Yang, Chengfeng Dou, Peidong Guo, et al. “DCPO: Dynamic Clipping Policy Optimization”. In:CoRR abs/2509.02333 (2025).doi:10.48550/ARXIV.2509.02333. arXiv:2509.02333.url: https://doi.org/10.48550/arXiv.2509.02333

    work page doi:10.48550/arxiv.2509.02333 2025

  11. [11]

    Group Sequence Policy Optimization

    Chujie Zheng, Shixuan Liu, Mingze Li, et al. “Group Sequence Policy Optimization”. In:CoRRabs/2507.18071 (2025).doi:10.48550/ARXIV.2507.18071. arXiv:2507.18071.url: https://doi.org/10.48550/arXiv.2507.18071

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2507.18071 2025

  12. [12]

    DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning

    Daya Guo, Dejian Yang, Haowei Zhang, et al. “DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning”. In:Nat.645.8081 (2025), pp. 633–638.doi:10.1038/S41586-025-09422-Z.url: https://doi.org/10.1038/s41586-025-09422-z

    work page doi:10.1038/s41586-025-09422-z.url: 2025

  13. [13]

    Database resources of the national center for biotechnology information

    Eric W Sayers, Jeffrey Beck, Evan E Bolton, et al. “Database resources of the national center for biotechnology information”. In:Nucleic acids research49.D1 (2021), pp. D10–D17

    2021

  14. [14]

    Utilization of the PICO framework to improve searching PubMed for clinical questions

    Connie Schardt, Martha B Adams, Thomas Owens, Sheri Keitz, and Paul Fontelo. “Utilization of the PICO framework to improve searching PubMed for clinical questions”. In:BMC medical informatics and decision making 7.1 (2007), p. 16

    2007

  15. [15]

    Retrieval-augmented generation for knowledge-intensive nlp tasks

    Patrick Lewis, Ethan Perez, Aleksandra Piktus, et al. “Retrieval-augmented generation for knowledge-intensive nlp tasks”. In:Advances in neural information processing systems33 (2020), pp. 9459–9474

    2020

  16. [16]

    MedGemma Technical Report

    Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, et al. “MedGemma Technical Report”. In:CoRR abs/2507.05201 (2025).doi:10.48550/ARXIV.2507.05201. arXiv:2507.05201.url: https://doi.org/10.48550/arXiv.2507.05201

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2507.05201 2025

  17. [17]

    Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning

    LASA Team, Weiwen Xu, Hou Pong Chan, et al. “Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning”. In:CoRRabs/2506.07044 (2025).doi: 10.48550/ARXIV.2506.07044. arXiv:2506.07044.url:https://doi.org/10.48550/arXiv.2506.07044

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2506.07044 2025

  18. [18]

    Hulu-med: A transparent generalist model towards holistic medical vision-language understanding.arXiv preprint arXiv:2510.08668, 2025

    Songtao Jiang, Yuan Wang, Sibo Song, et al. “Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding”. In:CoRRabs/2510.08668 (2025).doi:10.48550/ARXIV.2510.08668. arXiv:2510.08668.url:https://doi.org/10.48550/arXiv.2510.08668

    work page doi:10.48550/arxiv.2510.08668 2025

  19. [19]

    Medical Hallucinations in Foundation Models and Their Impact on Healthcare

    Yubin Kim, Hyewon Jeong, Shan Chen, et al. “Medical Hallucinations in Foundation Models and Their Impact on Healthcare”. In:CoRRabs/2503.05777 (2025).doi:10.48550/ARXIV.2503.05777. arXiv:2503.05777.url: https://doi.org/10.48550/arXiv.2503.05777

    work page doi:10.48550/arxiv.2503.05777 2025

  20. [20]

    Med-HALT: Medical Domain Hallucination Test for Large Language Models

    Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. “Med-HALT: Medical Domain Hallucination Test for Large Language Models”. In:Proceedings of the 27th Conference on Computational Natural Language Learning, CoNLL 2023, Singapore, December 6-7, 2023. Ed. by Jing Jiang, David Reitter, and Shumin Deng. Association for Computational Linguistics, 2...

    work page doi:10.18653/v1/2023.conll-1.21.url:https://doi.org/10.18653/v1/2023.conll-1.21 2023

  21. [21]

    HealthBench: Evaluating Large Language Models Towards Improved Human Health

    Rahul K. Arora, Jason Wei, Rebecca Soskin Hicks, et al. “HealthBench: Evaluating Large Language Models Towards Improved Human Health”. In:CoRRabs/2505.08775 (2025).doi:10.48550/ARXIV.2505.08775. arXiv: 2505.08775.url:https://doi.org/10.48550/arXiv.2505.08775

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2505.08775 2025

  22. [22]

    Openai gpt-5 system card

    Aaditya Singh, Adam Fry, Adam Perelman, et al. “Openai gpt-5 system card”. In:CoRR(2025). arXiv: 2601.03267

    Pith/arXiv arXiv 2025

  23. [23]

    DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

    DeepSeek-AI and collaborators. “DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models”. In: arXiv preprint arXiv:2512.02556(2025). DeepSeek-V3.2 thinking-enhanced reasoning model technical report; accessed 2026-01-29.url:https://arxiv.org/abs/2512.02556. 16 Baichuan-M4 Model Card

    Pith/arXiv arXiv 2025

  24. [24]

    Overview of the TREC 2003 Question Answering Track

    Ellen M Voorhees. “Overview of the TREC 2003 Question Answering Track.” In:TREC. Vol. 2003. 2003, pp. 54–68

    2003

  25. [25]

    Qwen3 Technical Report

    An Yang, Anfeng Li, Baosong Yang, et al.Qwen3 Technical Report. 2025.doi:10.48550/ARXIV.2505.09388. arXiv: 2505.09388.url:https://doi.org/10.48550/arXiv.2505.09388

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2505.09388 2025

  26. [26]

    A real-world dataset and benchmark for foundation model adaptation in medical image classification

    Dequan Wang, Xiaosong Wang, Lilong Wang, et al. “A real-world dataset and benchmark for foundation model adaptation in medical image classification”. In:Scientific Data10.1 (2023), p. 574

    2023

  27. [27]

    Preparing a collection of radiology examinations for distribution and retrieval

    Dina Demner-Fushman, Marc D Kohli, Marc B Rosenman, et al. “Preparing a collection of radiology examinations for distribution and retrieval”. In:Journal of the American Medical Informatics Association23.2 (2016), pp. 304–310

    2016

  28. [28]

    MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

    Alistair EW Johnson, Tom J Pollard, Nathaniel R Greenbaum, et al. “MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs”. In:arXiv preprint arXiv:1901.07042(2019)

    Pith/arXiv arXiv 1901

  29. [29]

    Rocov2: Radiology objects in context version 2, an updated multimodal image dataset

    Johannes Rückert, Louise Bloch, Raphael Brüngel, et al. “Rocov2: Radiology objects in context version 2, an updated multimodal image dataset”. In:Scientific Data11.1 (2024), p. 688

    2024

  30. [30]

    Chexpert plus: Augmenting a large chest x-ray dataset with text radiology reports, patient demographics and additional image formats

    Pierre Chambon, Jean-Benoit Delbrouck, Thomas Sounack, et al. “Chexpert plus: Augmenting a large chest x-ray dataset with text radiology reports, patient demographics and additional image formats”. In:arXiv preprint arXiv:2405.19538(2024)

    arXiv 2024

  31. [31]

    Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images

    Linda Wang, Zhong Qiu Lin, and Alexander Wong. “Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images”. In:Scientific reports10.1 (2020), p. 19549

    2020

  32. [32]

    Cider: Consensus-based image description evaluation

    Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. “Cider: Consensus-based image description evaluation”. In:Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, pp. 4566–4575

    2015

  33. [33]

    Green: Generative radiology report evaluation and error notation

    Sophie Ostmeier, Justin Xu, Zhihong Chen, et al. “Green: Generative radiology report evaluation and error notation”. In:Findings of the association for computational linguistics: EMNLP 2024. 2024, pp. 374–390

    2024

  34. [34]

    Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset

    Matthew Groh, Caleb Harris, Luis Soenksen, et al. “Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset”. In:Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pp. 1820–1828. 17

    2021