{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T11:51:39Z","timestamp":1782647499923,"version":"3.54.5"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.eswa.2026.132966","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T06:58:21Z","timestamp":1779433101000},"page":"132966","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation"],"prefix":"10.1016","volume":"329","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0863-4339","authenticated-orcid":false,"given":"Musarrat","family":"Zeba","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah Al","family":"Mamun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kishoar Jahan","family":"Tithee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debopom","family":"Sutradhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4793-5382","authenticated-orcid":false,"given":"Mohaimenul Azam Khan","family":"Raiaan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saddam","family":"Mukta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reem E.","family":"Mohamed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Rafiqul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0499-6565","authenticated-orcid":false,"given":"Yakub","family":"Sebastian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mukhtar","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7572-9750","authenticated-orcid":false,"given":"Sami","family":"Azam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132966_bib0001","unstructured":"Anthropic (2023a). Claude 2. https:\/\/www.anthropic.com\/index\/claude-2. Accessed: October 19, 2025."},{"key":"10.1016\/j.eswa.2026.132966_bib0002","unstructured":"Anthropic (2023b). Releasing claude instant 1.2. https:\/\/www.anthropic.com\/index\/releasing-claude-instant-1-2. Accessed: October 19, 2025."},{"issue":"2","key":"10.1016\/j.eswa.2026.132966_bib0003","first-page":"32","article-title":"The likert scale what it is and how to use it","volume":"50","author":"Batterton","year":"2017","journal-title":"Phalanx"},{"key":"10.1016\/j.eswa.2026.132966_bib0004","series-title":"Proceedings of the 2024 conference on clinical natural language processing (clinicalNLP)","first-page":"596","article-title":"Overview of the MEDIQA-CORR 2024 shared task on medical error detection and correction","author":"Ben Abacha","year":"2024"},{"key":"10.1016\/j.eswa.2026.132966_bib0005","article-title":"DairygoatQA: A knowledge graph enhanced large language models approach for question answering in the dairy goat domain","author":"Chen","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132966_bib0006","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, H., Liu, Y., Xie, J., Yang, R., Yuan, H., Fu, Y., Zhou, P. Y., Chen, Q., Caverlee, J., & Li, I. (2025b). Graphcheck: Breaking long-term text barriers with extracted knowledge graph-powered fact-checking. arXiv preprint arXiv: 2502.16514.","DOI":"10.18653\/v1\/2025.acl-long.729"},{"key":"10.1016\/j.eswa.2026.132966_bib0007","unstructured":"DSS Solutions (2024). Benchmarking LLM inference backends. DSS Solutions Tech Blog. [Online]. Available: https:\/\/dsssolutions.com\/2024\/06\/17\/benchmarking-llm-inference-backends\/."},{"key":"10.1016\/j.eswa.2026.132966_bib0008","unstructured":"Feng, Y. (2025). Counterfactual probing for hallucination detection and mitigation in large language models. arXiv preprint arXiv: 2508.01862."},{"key":"10.1016\/j.eswa.2026.132966_bib0009","unstructured":"Garcia-Fernandez, C., Felipe, L., Shotande, M., Zitu, M., Tripathi, A., Rasool, G., El Naqa, I., Rudrapatna, V., & Valdes, G. (2025). Trustworthy AI for medicine: Continuous hallucination detection and elimination with CHECK. arXiv preprint arXiv: 2506.11129."},{"key":"10.1016\/j.eswa.2026.132966_bib0010","series-title":"Human-computer creativity: Generative AI in education, art, and healthcare","first-page":"321","article-title":"Generative AI hallucinations in healthcare: A challenge for prompt engineering and creativity","author":"Geroimenko","year":"2025"},{"key":"10.1016\/j.eswa.2026.132966_bib0011","doi-asserted-by":"crossref","unstructured":"Goel, A., Schwartz, D., & Qi, Y. (2025). Zero-knowledge LLM hallucination detection and mitigation through fine-grained cross-model consistency. arXiv preprint arXiv: 2508.14314.","DOI":"10.18653\/v1\/2025.emnlp-industry.139"},{"key":"10.1016\/j.eswa.2026.132966_bib0012","article-title":"Dynamic feature fusion guiding and multimodal large language model refining for medical image report generation","author":"Han","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132966_bib0013","unstructured":"Han, T., Adams, L. C., Papaioannou, J.-M., Grundmann, P., Oberhauser, T., Figueroa, A., L\u00f6ser, A., Truhn, D., & Bressem, K. K. (2025). MedAlpaca \u2013 an open-source collection of medical conversational AI models and training data. arXiv preprint arXiv: 2304.08247."},{"key":"10.1016\/j.eswa.2026.132966_bib0014","unstructured":"Hegselmann, S., Shen, S. Z., Gierse, F., Agrawal, M., Sontag, D., & Jiang, X. (2024). A data-centric approach to generate faithful and high quality patient summaries with large language models. arXiv preprint arXiv: 2402.15422."},{"key":"10.1016\/j.eswa.2026.132966_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126712","article-title":"Halucheck: Explainable and verifiable automation for detecting hallucinations in LLM responses","volume":"272","author":"Heo","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.132966_bib0016","first-page":"3","article-title":"Lora: Low-rank adaptation of large language models","volume":"1","author":"Hu","year":"2022","journal-title":"ICLR"},{"key":"10.1016\/j.eswa.2026.132966_bib0017","unstructured":"Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2023). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv: 10.1145\/3703155."},{"issue":"2","key":"10.1016\/j.eswa.2026.132966_bib0018","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3703155","article-title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions","volume":"43","author":"Huang","year":"2025","journal-title":"ACM Transactions on Information Systems"},{"issue":"12","key":"10.1016\/j.eswa.2026.132966_bib0019","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3571730","article-title":"Survey of hallucination in natural language generation","volume":"55","author":"Ji","year":"2023","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"10.1016\/j.eswa.2026.132966_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2016.35","article-title":"Mimic-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Scientific Data"},{"key":"10.1016\/j.eswa.2026.132966_bib0021","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2016.35","article-title":"Mimic-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Scientific Data"},{"key":"10.1016\/j.eswa.2026.132966_bib0022","doi-asserted-by":"crossref","first-page":"139171","DOI":"10.1109\/ACCESS.2023.3340719","article-title":"ps-CALR: Periodic-shift cosine annealing learning rate for deep neural networks","volume":"11","author":"Johnson","year":"2023","journal-title":"IEEE access"},{"key":"10.1016\/j.eswa.2026.132966_bib0023","doi-asserted-by":"crossref","unstructured":"Joseph, S. A., Chen, L., Trienes, J., G\u00f6ke, H. L., Coers, M., Xu, W., Wallace, B. C., & Li, J. J. (2024). FactPico: Factuality evaluation for plain language summarization of medical evidence. arXiv preprint arXiv: 2402.11456.","DOI":"10.18653\/v1\/2024.acl-long.459"},{"issue":"6","key":"10.1016\/j.eswa.2026.132966_bib0024","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1093\/jamia\/ocad036","article-title":"Evidencemap: A three-level knowledge representation for medical evidence computation and comprehension","volume":"30","author":"Kang","year":"2023","journal-title":"Journal of the American Medical Informatics Association"},{"issue":"5","key":"10.1016\/j.eswa.2026.132966_bib0025","article-title":"Embracing large language models for medical applications: Opportunities and challenges","volume":"15","author":"Karabacak","year":"2023","journal-title":"Cureus"},{"key":"10.1016\/j.eswa.2026.132966_bib0026","doi-asserted-by":"crossref","unstructured":"Kim, Y., Jeong, H., Chen, S., Li, S. S., Lu, M., Alhamoud, K., Mun, J., Grau, C., Jung, M., Gameiro, R. et al. (2025). Medical hallucinations in foundation models and their impact on healthcare. arXiv preprint arXiv: 2503.05777.","DOI":"10.1101\/2025.02.28.25323115"},{"key":"10.1016\/j.eswa.2026.132966_bib0027","article-title":"HaluGNN: Hallucination detection in large language models using graph neural network","author":"Kong","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132966_bib0028","series-title":"Proceedings of the 16th international conference on agents and artificial intelligence (ICAART)","article-title":"Scientific claim verification with fine-tuned NLI models","author":"Ko\u0161prdi\u0107","year":"2024"},{"key":"10.1016\/j.eswa.2026.132966_bib0029","doi-asserted-by":"crossref","unstructured":"Kweon, S., Kim, J., Kim, J., Im, S., Cho, E., Bae, S., Oh, J., Lee, G., Moon, J. H., You, S. C., Baek, S., Han, C. H., Jung, Y. B., Jo, Y., & Choi, E. (2024). Publicly shareable clinical large language model built on synthetic clinical notesFindings of the Association for Computational Linguistics: ACL 2024. 5148\u20135168.","DOI":"10.18653\/v1\/2024.findings-acl.305"},{"key":"10.1016\/j.eswa.2026.132966_bib0030","first-page":"15589","article-title":"Ehrsql: A practical text-to-sql benchmark for electronic health records","volume":"35","author":"Lee","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132966_bib0031","unstructured":"Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., K\u00fcttler, H., Lewis, M., Yih, W.-t., Rockt\u00e4schel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. arXiv preprint arXiv: 2005.11401."},{"key":"10.1016\/j.eswa.2026.132966_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125920","article-title":"Taming large language models to implement diagnosis and evaluating the generation of LLMs at the semantic similarity level in acupuncture and moxibustion","volume":"264","author":"Li","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132966_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.bj.2025.100868","article-title":"Roles and potential of large language models in healthcare: A comprehensive review","author":"Lin","year":"2025","journal-title":"Biomedical Journal"},{"key":"10.1016\/j.eswa.2026.132966_bib0034","article-title":"Mire: A medical information enhanced framework for long-tail medical dialogue synthesis","author":"Lv","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132966_bib0035","doi-asserted-by":"crossref","unstructured":"Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv: 2005.00661.","DOI":"10.18653\/v1\/2020.acl-main.173"},{"issue":"suppl_2","key":"10.1016\/j.eswa.2026.132966_bib0036","doi-asserted-by":"crossref","first-page":"W170","DOI":"10.1093\/nar\/gkp440","article-title":"Bioportal: Ontologies and integrated data resources at the click of a mouse","volume":"37","author":"Noy","year":"2009","journal-title":"Nucleic Acids Research"},{"issue":"6","key":"10.1016\/j.eswa.2026.132966_bib0037","doi-asserted-by":"crossref","first-page":"e428","DOI":"10.1016\/S2589-7500(24)00061-X","article-title":"Ethical and regulatory challenges of large language models in medicine","volume":"6","author":"Ong","year":"2024","journal-title":"The Lancet Digital Health"},{"key":"10.1016\/j.eswa.2026.132966_bib0038","doi-asserted-by":"crossref","unstructured":"Pal, A., Umapathi, L. K., & Sankarasubbu, M. (2023). Med-halt: Medical domain hallucination test for large language models. arXiv preprint arXiv: 2307.15343.","DOI":"10.18653\/v1\/2023.conll-1.21"},{"key":"10.1016\/j.eswa.2026.132966_bib0039","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2024.1341697","article-title":"The perils and promises of fact-checking with large language models","volume":"7","author":"Quelle","year":"2024","journal-title":"Frontiers in Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132966_bib0040","unstructured":"Rawte, V., Sheth, A., & Das, A. (2023). A survey of hallucination in large foundation models. arXiv preprint arXiv: 2309.05922."},{"key":"10.1016\/j.eswa.2026.132966_bib0041","unstructured":"Red Hat Developer (2024). Deploy llama 3 8b with vLLM. Red Hat Developer. [Online]. Available:. https:\/\/developers.redhat.com\/articles\/2024\/06\/18\/deploy-llama-3-8b-with-vllm."},{"key":"10.1016\/j.eswa.2026.132966_bib0042","unstructured":"Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Sauvestre, R., Remez, T. et al. (2023). Code llama: Open foundation models for code. arXiv preprint arXiv: 2308.12950."},{"key":"10.1016\/j.eswa.2026.132966_bib0043","doi-asserted-by":"crossref","unstructured":"Sawczyn, A., Binkowski, J., Janiak, D., Gabrys, B., & Kajdanowicz, T. (2025). FactSelfCheck: Fact-level black-box hallucination detection for LLMs. arXiv preprint arXiv: 2503.17229.","DOI":"10.18653\/v1\/2026.findings-eacl.296"},{"key":"10.1016\/j.eswa.2026.132966_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125723","article-title":"Mitigating reasoning hallucination through multi-agent collaborative filtering","volume":"263","author":"Shi","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132966_bib0045","unstructured":"Shirish Keskar, N., Mudigere, D., Nocedal, J., Smelyanskiy, M., & Tang, P. T. P. (2016). On large-batch training for deep learning: Generalization gap and sharp minima. ArXiv e-prints arXiv: 1609.04836."},{"key":"10.1016\/j.eswa.2026.132966_bib0046","series-title":"Findings of the association for computational linguistics: EMNLP 2021","first-page":"3784","article-title":"Retrieval augmentation reduces hallucination in conversation","author":"Shuster","year":"2021"},{"key":"10.1016\/j.eswa.2026.132966_bib0047","series-title":"Proceedings of the 18th international workshop on semantic evaluation (semeval-2024)","first-page":"82","article-title":"Brainllama at semeval-2024 task 6: Prompting llama to detect hallucinations and related observable overgeneration mistakes","author":"Siino","year":"2024"},{"key":"10.1016\/j.eswa.2026.132966_bib0048","series-title":"Proc. of the 25th working notes of the conference and labs of the evaluation forum","first-page":"712","article-title":"Gpt hallucination detection through prompt engineering","volume":"vol. 3740","author":"Siino","year":"2024"},{"issue":"7972","key":"10.1016\/j.eswa.2026.132966_bib0049","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.eswa.2026.132966_bib0050","doi-asserted-by":"crossref","unstructured":"Tang, L., Laban, P., & Durrett, G. (2024). MiniCheck: Efficient fact-checking of llms on grounding documents. arXiv preprint arXiv: 2404.10774.","DOI":"10.18653\/v1\/2024.emnlp-main.499"},{"issue":"1","key":"10.1016\/j.eswa.2026.132966_bib0051","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41746-023-00896-7","article-title":"Evaluating large language models on medical evidence summarization","volume":"6","author":"Tang","year":"2023","journal-title":"NPJ Digital Medicine"},{"issue":"8","key":"10.1016\/j.eswa.2026.132966_bib0052","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nature Medicine"},{"key":"10.1016\/j.eswa.2026.132966_bib0053","unstructured":"Thorne, J., & Vlachos, A. (2018). Automated fact checking: Task formulations, methods and future directions. arXiv preprint arXiv: 1806.07687."},{"key":"10.1016\/j.eswa.2026.132966_bib0054","unstructured":"Toma, A., Lawler, P. R., Ba, J., Krishnan, R. G., Rubin, B. B., & Wang, B. (2023). Clinical camel: An open expert-level medical language model with dialogue-based knowledge encoding. arXiv preprint arXiv: 2305.12031."},{"key":"10.1016\/j.eswa.2026.132966_bib0055","unstructured":"Tonmoy, S. M., Zaman, S. M., Jain, V., Rani, A., Rawte, V., Chadha, A., & Das, A. (2024). A comprehensive survey of hallucination mitigation techniques in large language models. arXiv preprint arXiv: 2401.01313."},{"key":"10.1016\/j.eswa.2026.132966_bib0056","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S. et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv: 2307.09288."},{"key":"10.1016\/j.eswa.2026.132966_bib0057","doi-asserted-by":"crossref","unstructured":"Vladika, J., Schneider, P., & Matthes, F. (2023). HealthFC: Verifying health claims with evidence-based medical fact-checking. arXiv preprint arXiv: 2309.08503.","DOI":"10.63317\/3soozndxgt86"},{"key":"10.1016\/j.eswa.2026.132966_bib0058","unstructured":"Vykopal, I., Pikuliak, M., Ostermann, S., & \u0160imko, M. (2024). Generative large language models in automated fact-checking: A survey. arXiv preprint arXiv: 2407.02351."},{"key":"10.1016\/j.eswa.2026.132966_bib0059","unstructured":"Wang, G., Yang, G., Du, Z., Fan, L., & Li, X. (2023). ClinicalGPT: Large language models finetuned with diverse medical data and comprehensive evaluation. arXiv preprint arXiv: 2306.09968."},{"key":"10.1016\/j.eswa.2026.132966_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127241","article-title":"Contrastive learning with large language models for medical code prediction","volume":"277","author":"Wu","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132966_bib0061","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127582","article-title":"Staf-LLM: A scalable and task-adaptive fine-tuning framework for large language models in medical domain","volume":"281","author":"Xu","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132966_bib0062","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3352\/jeehp.2024.21.6","article-title":"Opportunities, challenges, and future directions of large language models, including chatGPT in medical education: A systematic scoping review","volume":"21","author":"Xu","year":"2024","journal-title":"Journal of Educational Evaluation for Health Professions"},{"issue":"4","key":"10.1016\/j.eswa.2026.132966_bib0063","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/hcs2.61","article-title":"Large language models in health care: Development, applications, and challenges","volume":"2","author":"Yang","year":"2023","journal-title":"Health Care Science"},{"key":"10.1016\/j.eswa.2026.132966_bib0064","series-title":"Proceedings of the 9th machine learning for healthcare conference","article-title":"DOSSIER: Fact checking in electronic health records while preserving patient privacy","volume":"vol. 252","author":"Zhang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132966_bib0065","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, H., Pan, X., Yao, W., Yu, D., Wu, T., & Chen, J. (2024). Fact-and-reflection (far) improves confidence calibration of large language models. arXiv preprint arXiv: 2402.17124.","DOI":"10.18653\/v1\/2024.findings-acl.515"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426018786?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426018786?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T21:02:05Z","timestamp":1780434125000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426018786"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":65,"alternative-id":["S0957417426018786"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132966","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132966","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"132966"}}