The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Explaining Chest X-Ray Pathologies in Natural Language
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Studies utilizing explanations for model improvement employed diverse techniques: rationale-guided training, iterative self-refinement, knowledge distillation, and feature engineering.
“Studies utilizing explanations for model improvement employed diverse techniques: rationale-guided training where explanation losses improve learned representations [98,123,124]; iterative self-refinement through agent dialogue or bidirectional verification [65,83,109]; knowledge distillation from large to compact models via structured rationales [110]; and feature engineering where LLM-generated”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [98]
Lexical overlap metrics were employed in studies with available gold-standard rationales.
“These were employed in studies with available gold-standard rationales [65,69,72,98,124]”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [98]
Rationale-supervised training represents a middle ground where models learn to generate explanations through supervised examples, potentially inheriting the reasoning patterns of expert annotators.
“Rationale-supervised training [67,69,90,98,110,123,124] represents a middle ground where models learn to generate explanations through supervised examples, potentially inheriting the reasoning patterns of expert annotators.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [98]
Fifteen studies used only open-source models.
“fifteen used only open-source models [67,72,73,96,98,113,129]”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [98]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
The false hope of current approaches to explainable artificial intelligence in health care
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00208-9 · Cited by 1,724
Machine learning in medicine: Addressing ethical challenges
Vayena, E., Blasimme, A., & Cohen, I. G. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002689 · Cited by 912
Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., et al. (2019) · Radiology · doi:10.1148/radiol.2019190613 · Cited by 368
Smit, A., Jain, S., Rajpurkar, P., Pareek, A., Ng, A., & Lungren, M. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.117 · Cited by 262
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
Kayser, M., Camburu, O.-M., Salewski, L., Emde, C., Do, V., Akata, Z., & Lukasiewicz, T. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00128 · Cited by 68
Self-citation
Flagged first, then by citations
Research works printed but not linked: 29
- Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: NeurIPS (2018)
- Banerjee, S., Lavie, A.: METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization (2005)
- Camburu, O.M., Rocktäschel, T., Lukasiewicz, T., Blunsom, P.: e-SNLI: natural language inference with natural language explanations. In: NeurIPS (2018)
- Cohen, J.P., et al.: TorchXRayVision: a library of chest X-ray datasets and models. arXiv preprint arXiv:2111.00595 (2020) · arXiv
- Gale, W., et al.: Producing radiologist-quality reports for interpretable artificial intelligence. arXiv preprint arXiv:1806.00340 (2018) · arXiv
- Gu, J., Tresp, V.: Saliency methods for explaining adversarial attacks. arXiv preprint arXiv: 1908.08413 (2019) · arXiv
- Hajian, S., Bonchi, F., Castillo, C.: Algorithmic bias: from discrimination discovery to fairness-aware data mining. In: SIGKDD (2016) · DOI · Paper page
- Hou, B., Kaissis, G., Summers, R.M., Kainz, B.: RATCHET: medical transformer for chest X-ray diagnosis and reporting. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 293–303. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_28 · DOI
- Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017) · DOI · Paper page
- Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison, vol. 33(01) (2019) · DOI · Paper page
- Johnson, A.E.W., et al.: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. arXiv:1901.07042 (2019) · arXiv
- Kim, E., Kim, S., Seo, M., Yoon, S.: XProtoNet: diagnosis in chest radiography with global and local explanations. In: CVPR (2021) · DOI
- Kim, J., Rohrbach, A., Darrell, T., Canny, J., Akata, Z.: Textual explanations for self-driving vehicles. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 577–593. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_35 · DOI
- Koh, P.W., et al.: Concept bottleneck models. In: ICML (2020)
- Kotonya, N., Toni, F.: Explainable automated fact-checking for public health claims. In: EMNLP (2020) · DOI · Paper page
- Kougia, V., et al.: RTEX: a novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams. J. Am. Med. Inform. Assoc. 28(8), 1651–1659 (2021) · DOI
- Lee, H., Kim, S.T., Ro, Y.M.: Generation of multimodal justification using visual word constraint model for explainable computer-aided diagnosis. In: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support (2019) · DOI
- Majumder, B.P., Camburu, O.M., Lukasiewicz, T., McAuley, J.: Knowledge-grounded self-rationalization via extractive and natural language explanations. In: ICML (2022)
- Marasović, A., et al.: Natural language rationales with full-stack visual reasoning: from pixels to semantic frames to commonsense graphs. In: EMNLP Findings (2020) · DOI
- Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019) · DOI · Paper page
- Mozaffari-Kermani, M., et al.: Systematic poisoning attacks on and defenses for machine learning in healthcare. IEEE J. Biomed. Health Inform. 19(6), 1893–1905 (2015) · DOI
- Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016) · arXiv
- Park, D.H., et al.: Multimodal explanations: justifying decisions and pointing to the evidence. In: ICCV (2018) · DOI · Paper page
- Puyol-Antón, E., et al.: Interpretable deep models for cardiac resynchronisation therapy response prediction. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 284–293. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_28 · DOI
- Rajani, N.F., McCann, B., Xiong, C., Socher, R.: Explain yourself! Leveraging language models for commonsense reasoning. In: ACL (2019) · DOI
- Schutte, K., Moindrot, O., Hérent, P., Schiratti, J.B., Jégou, S.: Using StyleGAN for visual interpretability of deep learning models on medical images. arXiv preprint arXiv:2101.07563 (2021) · arXiv
- Stacey, J., Belinkov, Y., Rei, M.: Natural language inference with a human touch: using human explanations to guide model attention. In: AAAI (2022)
- Wang, X., et al.: TieNet: text-image embedding network for common thorax disease classification and reporting in chest X-rays. In: CVPR (2018) · DOI
- Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: evaluating text generation with BERT. In: ICLR (2019)
Cited by
Sorted by citations
Rigorously assessing natural language explanations of neurons
Huang, J., Geiger, A., D’Oosterlinck, K., Wu, Z., & Potts, C. (2023) · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/2023.blackboxnlp-1.24 · Cited by 3
Explainability of decoder-only clinical large language models: A scoping review
Mishra, N., Abu-Hanna, A., & Calixto, I. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103501
Metadata
- Authors
- Kayser, M., Emde, C., Camburu, O.-M., Parsons, G., Papiez, B., & Lukasiewicz, T.
- Affiliations
- University of Oxford (GB)
- University College London (GB)
- TU Wien (AT)
- Corresponding author
- Kayser, Maxime
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 701-713
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4295916547
- Cited by
- 14
- References
- 7
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 1
- FWCI
- 1.66
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Leverhulme Trust
- Deutsche Forschungsgemeinschaft (206/4-1, 390727645)
- AXA Research Fund
- Engineering and Physical Sciences Research Council (N510129, EP/S02428X/1, EP/N510129/, EP/N510129/1, EP/R013667/1, 390727645)
- Economic and Social Research Council (EP/N510129/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- natural language explanations, explainable AI, chest radiography, thoracic pathology, diagnostic imaging, clinical decision support, natural language generation, clinician evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
