XLR-KGDD: leveraging LLM and RAG for knowledge graph-based explainable disease diagnosis using multimodal clinical information
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The literature on KG-augmented LLM retrieval and the roadmap on unifying LLMs with knowledge graphs support the graph-based Combination interpretation.
“The literature on KG-augmented LLM retrieval (Wang et al. 2025; Bedi et al. 2025) and the roadmap on unifying LLMs with knowledge graphs (Pan et al. 2024) support this graphbased Combination interpretation.”
From From Organizational Knowledge to AI Agent Memory: Empirical Validation of the SECI Model on the LongMemEval Benchmark (2026) · cites this paper as Bedi et al. 2025
Some explanations cite specific, retrievable evidence, e.g. RAG-grounded source citations or knowledge-graph paths, enabling independent verification.
“Some explanations cite specific, retrievable evidence, e.g. RAG-grounded source citations or knowledge-graph paths, enabling independent verification . This characterizes the RAG-grounded approaches in our review [63, 87,91,92,94,99].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [92]
Cites
MIMIC-IV, a freely accessible electronic health record dataset
Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., et al. (2023) · Scientific Data · doi:10.1038/s41597-022-01899-x · Cited by 3,401 · see citing works
Harnessing the power of llms in practice: a survey on chatGPT and beyond
Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Zhong, S., Yin, B., & Hu, X. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3649506 · Cited by 644 · see citing works
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Żatuchin, D. (2026) · Research Square (preprint) · doi:10.21203/rs.3.rs-9801639/v1
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
- Authors
- Bedi, P., Thukral, A., & Dhiman, S.
- Affiliations
- University of Delhi (IN)
- Corresponding author
- Dhiman, Shivani
- Venue
- Knowledge and Information Systems
- Published
- 2025-05-13
- Volume
- 67
- Issue
- 9
- Pages
- 7451-7471
- ISSN
- 0219-1377 (ISSN-L)
- 0219-3116
- OpenAlex
- W4410315685
- Cited by
- 10
- References
- 2
- Citations by year
- 2026: 9 · 2025: 1
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge and Information Systems (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- disease diagnosis, explainable AI, knowledge graphs, retrieval-augmented generation, large language models, prompt engineering, parameter-efficient fine-tuning, hallucination mitigation, clinical guidelines, natural language explanations, congestive heart failure, Llama 3
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.
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