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## Publisher's Note

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2020. pp. 38–45. https://www.aclweb.org/anthology/2020.emnlp-demos.6. Accessed 2024.

89. Kormilitzin A, Vaci N, Liu Q, Nevado-Holgado A. Med7: A transferable clinical natural language processing model for electronic health records. Artif Intell Med. 2021;118:102086. https://doi.org/10.1016/j.artmed.2021.102086.

90. Akbik A, Blythe D, Vollgraf R. Contextual string embeddings for sequence labeling. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe, New Mexico, USA: Association for Computational Linguistics; 2018. pp. 1638–1649. https://aclanthology.org/C18-1139.

91. Pennington J, Socher R, Manning C. GloVe: Global vectors for word representation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: Association for Computational Linguistics; 2014. pp. 1532–1543. https://doi.org/10.3115/v1/D14-1162. https://aclanthology.org/D14-1162.

92. Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for Computational Linguistics; 2019. pp. 3982–3992. https://doi.org/10.18653/v1/D19-1410. https://aclanthology.org/D19-1410.

93. Deka P, Jurek-Loughrey A, Deepak P. Improved Methods To Aid Unsupervised Evidence-based Fact Checking For Online Health News. J Data Intell. 2022;3(4):474–504.

94. Ngai H, Rudzicz F. Doctor XAvler: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation. In: Proceedings of the 21st Workshop on Biomedical Language Processing. Dublin: Association for Computational Linguistics; 2022. pp. 337–44. https://doi.org/10.18653/v1/2022.bionlp-1.33.

95. Caufield JH. MACCROBAT. 2020. https://doi.org/10.6084/m9.fishshare.9764942.v2.

96. Brahman F, Shwartz V, Rudinger R, Choi Y. Learning to rationalize for non-monotonic reasoning with distant supervision. Proceedings of the AAAI Conference on Artificial Intelligence. 2021;35(14):12592–601. https://doi.org/10.1609/aaai.v35i14.17492.

97. Latcinnik V, Berant J. Explaining question answering models through text generation. 2020. arXiv preprint arXiv:200405569.

98. Athaluri S, Manthena V, Kesapragada M, Yarlagadda V, Dave T, Duddumpudi S. Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References. Cureus. 2023;15. https://doi.org/10.7759/cureus.37432.

99. Miller T. Explanation in artificial intelligence: Insights from the social sciences. Artif Intell. 2019;267:1–38. https://doi.org/10.1016/j.artint.2018.07.007.

## Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.