0
1

Delete article

Deleted articles cannot be recovered.

Draft of this article would be also deleted.

Are you sure you want to delete this article?

Medrax: Medical reasoning agent for chest x-ray

Last updated at Posted at 2025-08-04

A. Fallahpour, J. Ma, A. Munim, H. Lyu, and B. Wang. Medrax: Medical reasoning agent for chest x-ray, 2025. URL https://arxiv.org/abs/2502.02673

References

Adibi, A., Cao, X., Ji, Z., Kaur, J. N., Chen, W., Healey, E., Nuwagira, B., Ye, W., Woollard, G., Xu, M. A., Cui, H., Xi, J., Chang, T., Bikia, V., Zhang, N., Noori, A., Xia, Y., Hossain, M. B., Frank, H. A., Peluso, A., Pu, Y., Shen, S. Z., Wu, J., Fallahpour, A., Mahbub, S., Duncan, R., Zhang, Y., Cao, Y., Xu, Z., Craig, M., Krishnan, R. G., Beheshti, R., Rehg, J. M., Karim, M. E., Coffee, M., Celi, L. A., Fries, J. A., Sadatsafavi, M., Shung, D., McWeeney, S., Dafflon, J., and Jabbour, S. Recent advances, applications and open challenges in machine learning for health: Reflections from research roundtables at ml4h 2024 symposium, 2025.
Ahn, J. S., Ebrahimian, S., McDermott, S., Lee, S., Naccarato, L., Di Capua, J. F., Wu, M. Y., Zhang, E. W., Muse, V., Miller, B., et al. Association of artificial intelligence–aided chest radiograph interpretation with reader performance and efficiency. JAMA Network Open, 5(8):e2229289–e2229289, 2022.
Baghbanzadeh, N., Fallahpour, A., Parhizkar, Y., Ogidi, F., Roy, S., Ashkezari, S., Khazaie, V. R., Colacci, M., Etemad, A., Afkanpour, A., and Dolatabadi, E. Advancing medical representation learning through high-quality data, 2025.
Bahl, S., Ramzan, T., and Maraj, R. Interpretation and documentation of chest x-rays in the acute medical unit. Clinical Medicine, 20(2):s73, 2020.
Baltruschat, I., Steinmeister, L., Nickisch, H., Saalbach, A., Grass, M., Adam, G., Knopp, T., and Ittrich, H. Smart chest x-ray worklist prioritization using artificial intelligence: a clinical workflow simulation. European radiology, 31:3837–3845, 2021.
Bannur, S., Bouzid, K., Castro, D. C., Schwaighofer, A., Thieme, A., Bond-Taylor, S., Ilse, M., P´ erez-Garc´ ıa, F., Salvatelli, V., Sharma, H., Meissen, F., Ranjit, M., Srivastav, S., Gong, J., Codella, N. C. F., Falck, F., Oktay, O., Lungren, M. P., Wetscherek, M. T., Alvarez-Valle, J., and Hyland, S. L. Maira-2: Grounded radiology report generation, 2024.
Bansal, H., Israel, D., Zhao, S., Li, S., Nguyen, T., and Grover, A. Medmax: Mixed-modal instruction tuning for training biomedical assistants, 2024.
Chambon, P., Bluethgen, C., Delbrouck, J.-B., der Sluijs, R. V., Połacin, M., Chaves, J. M. Z., Abraham, T. M., Purohit, S., Langlotz, C. P., and Chaudhari, A. Roentgen: Vision-language foundation model for chest x-ray generation, 2022.
Chambon, P., Delbrouck, J.-B., Sounack, T., Huang, S.-C., Chen, Z., Varma, M., Truong, S. Q., Chuong, C. T., and Langlotz, C. P. Chexpert plus: Augmenting a large chest x-ray dataset with text radiology reports, patient demographics and additional image formats, 2024.
Chen, Z., Varma, M., Delbrouck, J.-B., Paschali, M., Blankemeier, L., Van Veen, D., Valanarasu, J. M. J., Youssef, A., Cohen, J. P., Reis, E. P., et al. Chexagent: Towards
a foundation model for chest x-ray interpretation. arXiv preprint arXiv:2401.12208, 2024a.
Chen, Z., Varma, M., Xu, J., Paschali, M., Veen, D. V., Johnston, A., Youssef, A., Blankemeier, L., Bluethgen, C., Altmayer, S., Valanarasu, J. M. J., Muneer, M. S. E., Reis, E. P., Cohen, J. P., Olsen, C., Abraham, T. M., Tsai, E. B., Beaulieu, C. F., Jitsev, J., Gatidis, S., Delbrouck, J.-B., Chaudhari, A. S., and Langlotz, C. P. A vision-language foundation model to enhance efficiency of chest x-ray interpretation, 2024b.
MedRAX: Medical Reasoning Agent for Chest X-ray Cohen, J. P., Hashir, M., Brooks, R., and Bertrand, H. On the limits of cross-domain generalization in automated x-ray prediction. In Medical Imaging with Deep Learning, 2020.
Cohen, J. P., Viviano, J. D., Bertin, P., Morrison, P., Torabian, P., Guarrera, M., Lungren, M. P., Chaudhari, A., Brooks, R., Hashir, M., and Bertrand, H. TorchXRayVision: A library of chest X-ray datasets and models. In Medical Imaging with Deep Learning, 2022.
Erdal, B. S., Gupta, V., Demirer, M., Fair, K. H., White, R. D., Blair, J., Deichert, B., Lafleur, L., Qin, M. M., Bericat, D., and Genereaux, B. Integration and implementation strategies for ai algorithm deployment with smart routing rules and workflow management, 2023.
Eriksen, A. V., M¨ oller, S., and Ryg, J. Use of gpt-4 to diagnose complex clinical cases, 2024.
Fallahpour, A., Alinoori, M., Ye, W., Cao, X., Afkanpour, A., and Krishnan, A. Ehrmamba: Towards generalizable and scalable foundation models for electronic health records, 2024.
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025.
Huang, J., Neill, L., Wittbrodt, M., Melnick, D., Klug, M., Thompson, M., Bailitz, J., Loftus, T., Malik, S., Phull, A., et al. Generative artificial intelligence for chest radiograph interpretation in the emergency department. JAMA network open, 6(10):e2336100–e2336100, 2023.
Hyland, S. L., Bannur, S., Bouzid, K., Castro, D. C., Ranjit, M., Schwaighofer, A., P´ erez-Garc´ ıa, F., Salvatelli, V., Srivastav, S., Thieme, A., Codella, N., Lungren, M. P., Wetscherek, M. T., Oktay, O., and Alvarez-Valle, J. Maira1: A specialised large multimodal model for radiology report generation, 2024.
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., Patel, B. N., Lungren, M. P., and Ng, A. Y. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison, 2019.
Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., Beutel, A., Carney, A., et al. Openai o1 system card. arXiv preprint arXiv:2412.16720, 2024.
Javan, R., Kim, T., and Mostaghni, N. Gpt-4 vision: Multimodal evolution of chatgpt and potential role in radiology. Cureus, 16(8):e68298, 2024.
Jiang, Y., Black, K. C., Geng, G., Park, D., Ng, A. Y., and Chen, J. H. Medagentbench: Dataset for benchmarking llms as agents in medical applications, 2025.
Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., and Narasimhan, K. Swe-bench: Can language models resolve real-world github issues? arXiv preprint arXiv:2310.06770, 2023.
Kim, Y., Park, C., Jeong, H., Chan, Y. S., Xu, X., McDuff, D., Lee, H., Ghassemi, M., Breazeal, C., and Park, H. W. Mdagents: An adaptive collaboration of llms for medical decision-making. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Doll´ ar, P., and Girshick, R. Segment anything, 2023.
Li, B., Yan, T., Pan, Y., Luo, J., Ji, R., Ding, J., Xu, Z., Liu, S., Dong, H., Lin, Z., et al. Mmedagent: Learning to use medical tools with multi-modal agent. arXiv preprint arXiv:2407.02483, 2024a.
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., and Gao, J. Llava-med: Training a large language-and-vision assistant for biomedicine in one day. Advances in Neural Information Processing Systems, 36, 2024b.
Lian, J., Liu, J., Zhang, S., Gao, K., Liu, X., Zhang, D., and Yu, Y. A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation. IEEE Transactions on Medical Imaging, 2021. doi: 10.48550/arxiv.2104.10326.
Liu, B., Zhan, L.-M., Xu, L., Ma, L., Yang, Y., and Wu, X.-M. Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering, 2021.
Liu, X., Yu, H., Zhang, H., Xu, Y., Lei, X., Lai, H., Gu, Y., Ding, H., Men, K., Yang, K., et al. Agentbench: Evaluating llms as agents. arXiv preprint arXiv:2308.03688, 2023.
Ma, J., He, Y., Li, F., Han, L., You, C., and Wang, B. Segment anything in medical images. Nature Communications, 15(1), January 2024. ISSN 2041-1723. doi:10.1038/s41467-024-44824-z.
Ma, J., Yang, Z., Kim, S., Chen, B., Baharoon, M., Fallahpour, A., Asakereh, R., Lyu, H., and Wang, B. Medsam2: Segment anything in 3d medical images and videos, 2025.
Masterman, T., Besen, S., Sawtell, M., and Chao, A. The landscape of emerging ai agent architectures for reasoning, planning, and tool calling: A survey. arXiv preprint arXiv:2404.11584, 2024.
MedRAX: Medical Reasoning Agent for Chest X-ray Nori, H., King, N., McKinney, S. M., Carignan, D., and Horvitz, E. Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375, 2023.
Ouis, M. Y. and Akhloufi, M. A. Chestbiox-gen: contextual biomedical report generation from chest x-ray images using biogpt and co-attention mechanism. Frontiers in Imaging, 3:1373420, 2024.
Park, J., Kim, S., Yoon, B., Hyun, J., and Choi, K. M4cxr: Exploring multi-task potentials of multi-modal large language models for chest x-ray interpretation, 2024.
Pellegrini, C., Keicher, M., Ozsoy, E., and Navab, N. Radrestruct: A novel vqa benchmark and method for structured radiology reporting, 2023.
Pellegrini, C., Ozsoy, E., Busam, B., Navab, N., and Keicher, M. Radialog: A large vision-language model for radiology report generation and conversational assistance, 2025.
Pham, H. H., Nguyen, H. Q., Nguyen, H. T., Le, L. T., and Khanh, L. An accurate and explainable deep learning system improves interobserver agreement in the interpretation of chest radiograph. IEEE Access, 10:104512–104531, 2022.
Schmidgall, S., Ziaei, R., Harris, C., Reis, E., Jopling, J., and Moor, M. Agentclinic: a multimodal agent benchmark to evaluate ai in simulated clinical environments, 2024.
Shin, H. J., Han, K., Ryu, L., and Kim, E.-K. The impact of artificial intelligence on the reading times of radiologists for chest radiographs. NPJ Digital Medicine, 6(1):82, 2023.
Tanno, R., Barrett, D. G., Sellergren, A., Ghaisas, S., Dathathri, S., See, A., Welbl, J., Lau, C., Tu, T., Azizi, S., et al. Collaboration between clinicians and vision–language models in radiology report generation. Nature Medicine, pp. 1–10, 2024.
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., Mustafa, B., Chowdhery, A., Liu, Y., Kornblith, S., Fleet, D., Mansfield, P., Prakash, S., Wong, R., Virmani, S., Semturs, C., Mahdavi, S. S., Green, B., Dominowska, E., y Arcas, B. A., Barral, J., Webster, D., Corrado, G. S., Matias, Y., Singhal, K., Florence, P., Karthikesalingam, A., and Natarajan, V. Towards generalist biomedical ai, 2023.
United Nations Scientific Committee on the Effects of Atomic Radiation. Sources, Effects and Risks of Ionizing Radiation: UNSCEAR 2020/2021 Report, Volume I. United Nations, New York, 2022. ISBN 978-92-1- 139206-7.
Wu, C., Zhang, X., Zhang, Y., Wang, Y., and Xie, W. Towards generalist foundation model for radiology by leveraging web-scale 2d and 3d medical data, 2023.
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. The rise and potential of large language model based agents: A survey. Science China Information Sciences, 68(2):121101, 2025.
Yan, Z., Zhang, K., Zhou, R., He, L., Li, X., and Sun, L. Multimodal chatgpt for medical applications: an experimental study of gpt-4v. arXiv preprint arXiv:2310.19061, 2023.
Yang, H. M., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., et al. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225, 2017.
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: Synergizing reasoning and acting in language models, 2023.
Yin, G., Bai, H., Ma, S., Nan, F., Sun, Y., Xu, Z., Ma, S., Lu, J., Kong, X., Zhang, A., et al. Mmau: A holistic benchmark of agent capabilities across diverse domains. arXiv preprint arXiv:2407.18961, 2024.
Zambrano Chaves, J., Huang, S.-C., Xu, Y., Xu, H., Usuyama, N., and Zhang, S, e. a. Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation. arXiv preprint arXiv:2403.08002, 2024.
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. Pyramid scene parsing network, 2017.
Zhao, P., Jin, Z., and Cheng, N. An in-depth survey of large language model-based artificial intelligence agents. arXiv preprint arXiv:2309.14365, 2023.

Related document on the Qiita

making reference list on biorxiv pdf file
https://qiita.com/kaizen_nagoya/items/75f6f93ce9872a5d622d

Genome modeling and design across all domains of life with evo 2
https://qiita.com/kaizen_nagoya/items/eecda74f758008633ee2

BIOREASON: DNA-LLMモデルによるマルチモーダル生物学的推論の動機付け
https://qiita.com/kaizen_nagoya/items/0718b214043a614deee0

Mckusick’s online mendelian inheritance in man (omim®)
https://qiita.com/kaizen_nagoya/items/c599d867201d1ffb1f4d

Anthropic. Claude 3.7 sonnet
https://qiita.com/kaizen_nagoya/items/4364d9c475114353cf2a

Genomic language models: Opportunities and challenges
https://qiita.com/kaizen_nagoya/items/f797330e64e0c7d05f39

A dna language model based on multispecies alignment predicts the effects of genome-wide variants
https://qiita.com/kaizen_nagoya/items/6e8858c2395dcc98804a

A genomic mutational constraint map using variation in 76,156 human genomes
https://qiita.com/kaizen_nagoya/items/e799ad85ee98bb2a8cf6

Genomic language models: Opportunities and challenges
https://qiita.com/kaizen_nagoya/items/f797330e64e0c7d05f39

Nucleotide transformer: building and evaluating robust foundation models for human genomics
https://qiita.com/kaizen_nagoya/items/1c147c2b095364f04ef7

A genomic mutational constraint map using variation in 76,156 human genomes
https://qiita.com/kaizen_nagoya/items/e799ad85ee98bb2a8cf6

DeepSeek-AI
https://qiita.com/kaizen_nagoya/items/bb5ee9f17c03e07659d8

Codontransformer: A multispecies codon optimizer using context-aware neural networks.
https://qiita.com/kaizen_nagoya/items/d4be1d4dd9eb307f09cc

Medrax: Medical reasoning agent for chest x-ray
https://qiita.com/kaizen_nagoya/items/94c7835b2f461452b2e7

0
1
0

Register as a new user and use Qiita more conveniently

  1. You get articles that match your needs
  2. You can efficiently read back useful information
  3. You can use dark theme
What you can do with signing up
0
1

Delete article

Deleted articles cannot be recovered.

Draft of this article would be also deleted.

Are you sure you want to delete this article?