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Cited by 4Biomedical Text Mining and OntologiesExplainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Explanatory argumentation in natural language for correct and incorrect medical diagnoses

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Molinet, Benjamin, Marro, Santiago, Cabrio, Elena, Villata, Serena·2024·Journal of Biomedical Semantics
DOI 10.1186/s13326-024-00306-1PMC PMC11138001
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Abstract

Background: A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is the lack of transparency and interpretability of the achieved results, making it hard to employ such methods for educational purposes. It is therefore necessary to develop new frameworks to enhance explainability in these solutions. Results: In this paper, we present a novel full pipeline to generate automatically natural language explanations for medical diagnoses. The proposed solution starts from a clinical case description associated with a list of correct and incorrect diagnoses and, through the extraction of the relevant symptoms and findings, enriches the information contained in the description with verified medical knowledge from an ontology. Finally, the system returns a pattern-based explanation in natural language which elucidates why the correct (incorrect) diagnosis is the correct (incorrect) one. The main contribution of the paper is twofold: first, we propose two novel linguistic resources for the medical domain (i.e, a dataset of 314 clinical cases annotated with the medical entities from UMLS, and a database of biological boundaries for common findings), and second, a full Information Extraction pipeline to extract symptoms and findings from the clinical cases and match them with the terms in a medical ontology and to the biological boundaries. An extensive evaluation of the proposed approach shows the our method outperforms comparable approaches. Conclusions: Our goal is to offer AI-assisted educational support framework to form clinical residents to formulate sound and exhaustive explanations for their diagnoses to patients.

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  • Educational applications included training medical residents in evidence-based diagnostic argumentation, simulating virtual medical tutors, and educating patients on health indicators via conversational explanations.

    “Educational applications included training medical residents in evidence-based diagnostic argumentation [127], simulating virtual medical tutors [105], and educating patients on health indicators via conversational explanations [100].”

    From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [127]

  • AI systems that articulate diagnostic reasoning can function as tutoring tools, demonstrating structured clinical thinking to trainees.

    “AI systems that articulate diagnostic reasoning can function as tutoring tools, demonstrating structured clinical thinking to trainees [105,127].”

    From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [127]

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  1. [1]Stöger K Schneeberger D Holzinger A Medical artificial intelligence: the European legal perspective Commun ACM. 2021 64 11 34 36 10.1145/3458652 · DOI · Paper page
  2. [2]Tjoa E, Guan C. A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI. CoRR. 2021. abs/1907.07374. http://arxiv.org/abs/1907.07374. Accessed 2024. 33079674 · arXiv
  3. [3]Saeed W, Omlin CW. Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. ArXiv abs/2111.06420. 2021. · arXiv
  4. [4]Cyras K, Rago A, Albini E, Baroni P, Toni F. Argumentative XAI: A survey. ArXiv abs/2105.11266. 2021. · arXiv
  5. [5]Johnson RH. Manifest Rationality: A Pragmatic Theory of Argument, p. 408. New York: Lawrence Earlbaum Associates; 2012. 10.4324/9781410606174. · DOI
  6. [6]Rago A, Cocarascu O, Toni F. Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them. In: Lang J, editor. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018. Stockholm; 2018. pp. 1949–55. 10.24963/ijcai.2018/269. · DOI
  7. [7]Rago A Cocarascu O Bechlivanidis C Lagnado DA Toni F Argumentative explanations for interactive recommendations Artif Intell. 2021 296 103506 10.1016/j.artint.2021.103506 · DOI
  8. [8]Vassiliades A Bassiliades N Patkos T Argumentation and explainable artificial intelligence: a survey Knowl Eng Rev. 2021 36 e5 10.1017/S0269888921000011 · DOI
  9. [10]Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32(suppl_1):D267–D270. PMC308795 14681409
  10. [11]Marro S, Molinet B, Cabrio E, Villata S. Natural Language Explanatory Arguments for Correct and Incorrect Diagnoses of Clinical Cases. In: ICAART 2023 - 15th International Conference on Agents and Artificial Intelligence. Proceedings of the 15th International Conference on Agents and Artificial Intelligence. 2023;1:438–49. Lisbon (Portugal), Portugal.
  11. [12]Nadkarni PM Ohno-Machado L Chapman WW Natural language processing: an introduction J Am Med Inform Assoc. 2011 18 5 544 551 10.1136/amiajnl-2011-000464 21846786 PMC3168328 · DOI
  12. [13]Friedman CP Elstein AS Wolf FM Murphy GC Franz TM Heckerling PS Enhancement of clinicians’ diagnostic reasoning by computer-based consultation: a multisite study of 2 systems Jama. 1999 282 19 1851 1856 10.1001/jama.282.19.1851 10573277 · DOI
  13. [14]Berner ES Webster GD Shugerman AA Jackson JR Algina J Baker AL Performance of four computer-based diagnostic systems N Engl J Med. 1994 330 25 1792 1796 10.1056/NEJM199406233302506 8190157 · DOI
  14. [15]Wang H Li Y Khan SA Luo Y Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network Artif Intell Med. 2020 110 101977 10.1016/j.artmed.2020.101977 33250149 PMC7983067 · DOI
  15. [16]Rumshisky A Ghassemi M Naumann T Szolovits P Castro V McCoy T Predicting early psychiatric readmission with natural language processing of narrative discharge summaries Transl Psychiatry. 2016 6 10 e921 10.1038/tp.2015.182 27754482 PMC5315537 · DOI
  16. [17]Feller DJ, Zucker J, Yin MT, Gordon P, Elhadad N. Using clinical notes and natural language processing for automated HIV risk assessment. J Acquir Immune Defic Syndr (1999). 2018;77(2):160. PMC5762388 29084046
  17. [18]Bacchi S Oakden-Rayner L Zerner T Kleinig T Patel S Jannes J Deep learning natural language processing successfully predicts the cerebrovascular cause of transient ischemic attack-like presentations Stroke. 2019 50 3 758 760 10.1161/STROKEAHA.118.024124 30653397 · DOI
  18. [19]Plass M, Kargl M, Kiehl TR, Regitnig P, Geißler C, Evans T, et al. Explainability and causability in digital pathology. J Pathol Clin Res. 2023;9(4):251–60. PMC10240147 37045794
  19. [20]Donnelly K, et al. SNOMED CT: The advanced terminology and coding system for eHealth. Stud Health Technol Inform. 2006;121:279. 17095826
  20. [21]Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–9. 16224307
  21. [22]Liu S Ma W Moore R Ganesan V Nelson S RxNorm: prescription for electronic drug information exchange IT Prof. 2005 7 5 17 23 10.1109/MITP.2005.122 · DOI · Paper page
  22. [23]Hirsch JA Leslie-Mazwi TM Nicola GN Barr RM Bello JA Donovan WD Current procedural terminology; a primer J Neurointerventional Surg. 2015 7 4 309 312 10.1136/neurintsurg-2014-011156 24589819 · DOI
  23. [24]Köhler S Gargano M Matentzoglu N Carmody LC Lewis-Smith D Vasilevsky NA The human phenotype ontology in 2021 Nucleic Acids Res. 2021 49 D1 D1207 D1217 10.1093/nar/gkaa1043 33264411 PMC7778952 · DOI
  24. [25]Sun W Rumshisky A Uzuner O Evaluating temporal relations in clinical text: 2012 i2b2 challenge J Am Med Inform Assoc. 2013 20 5 806 813 10.1136/amiajnl-2013-001628 23564629 PMC3756273 · DOI
  25. [26]Henry S Buchan K Filannino M Stubbs A Uzuner O 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records J Am Med Inform Assoc. 2020 27 1 3 12 10.1093/jamia/ocz166 31584655 PMC7489085 · DOI
  26. [27]Ben Abacha A, Shivade C, Demner-Fushman D. Overview of the MEDIQA 2019 shared task on textual inference, question entailment and question answering. In: Demner-Fushman, D., Cohen, K.B., Ananiadou, S., Tsujii, J. (eds.) Proceedings of the 18th BioNLP Workshop and Shared Task. Florence: Association for Computational Linguistics; 2019. pp. 370–379.
  27. [28]Khetan V, Wadhwa S, Wallace B, Amir S. SemEval-2023 task 8: Causal medical claim identification and related PIO frame extraction from social media posts. In: Ojha, A.K., Do ˘gru ¨oz, A.S., Da San Martino, G., Tayyar Madabushi, H., Kumar, R., Sartori, E. (eds.) Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). Toronto: Association for Computational Linguistics; 2023. pp. 2266–2274.
  28. [29]Jullien M, Valentino M, Frost H, O’Regan P, Landers D, Freitas A. SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data. 2023. arXiv preprint arXiv:230502993.
  29. [30]Wadhwa S, Khetan V, Amir S, Wallace B. Redhot: A corpus of annotated medical questions, experiences, and claims on social media. 2022. arXiv preprint arXiv:221006331.
  30. [31]Johnson AE, Pollard TJ, Shen L, Lehman LwH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3(1):1–9. PMC4878278 27219127
  31. [32]Pollard TJ Johnson AE Raffa JD Celi LA Mark RG Badawi O The eICU Collaborative Research Database, a freely available multi-center database for critical care research Sci Data. 2018 5 1 1 13 10.1038/sdata.2018.178 30204154 PMC6132188 · DOI · Paper page
  32. [33]Moody GB Mark RG Goldberger AL PhysioNet: a web-based resource for the study of physiologic signals IEEE Eng Med Biol Mag. 2001 20 3 70 75 10.1109/51.932728 11446213 · DOI · Paper page
  33. [34]Sudlow C Gallacher J Allen N Beral V Burton P Danesh J UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age PLoS Med. 2015 12 3 e1001779 10.1371/journal.pmed.1001779 25826379 PMC4380465 · DOI · Paper page
  34. [35]Clark K Vendt B Smith K Freymann J Kirby J Koppel P The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository J Digit Imaging. 2013 26 1045 1057 10.1007/s10278-013-9622-7 23884657 PMC3824915 · DOI · Paper page
  35. [36]Ohta T, Tateisi Y, Kim JD, Mima H, Tsujii J. The GENIA corpus: An annotated research abstract corpus in molecular biology domain. In: Proceedings of the human language technology conference. San Francisco: Morgan Kaufmann Publishers Inc.,; 2002. pp. 73–7.
  36. [37]Bada M Eckert M Evans D Garcia K Shipley K Sitnikov D Concept annotation in the CRAFT corpus BMC Bioinforma. 2012 13 1 1 20 10.1186/1471-2105-13-161 PMC3476437 22776079 · DOI
  37. [38]Pyysalo S Ananiadou S Anatomical entity mention recognition at literature scale Bioinformatics. 2014 30 6 868 875 10.1093/bioinformatics/btt580 24162468 PMC3957068 · DOI
  38. [39]Kim J.-D, Ohta T, Pyysalo S, Kano Y, Tsujii J. Overview of BioNLP’09 shared task on event extraction. In: Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task, pp. 1–9. Omnipress, Portland, Oregon, United States; 2009.
  39. [40]Kim JD, Wang Y, Yasunori Y. The genia event extraction shared task, 2013 edition-overview. In: Proceedings of the BioNLP Shared Task 2013 Workshop. 2013. pp. 8–15.
  40. [41]Smith L Tanabe LK Kuo CJ Chung I Hsu CN Lin YS Overview of BioCreative II gene mention recognition Genome Biol. 2008 9 2 1 19 PMC2559986 18834493
  41. [42]Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, et al. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database. 2016; p. baw068. PMC4860626 27161011
  42. [43]Kim J.-D, Ohta T, Tsuruoka Y, Tateisi Y, Collier N. Introduction to the bio-entity recognition task at JNLPBA. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications. JNLPBA ’04, pp. 70–75. USA: Association for Computational Linguistics; 2004.
  43. [44]Pyysalo S, Ohta T, Miwa M, Tsujii J. Towards exhaustive protein modification event extraction. In: Proceedings of BioNLP 2011 Workshop. United Kingdom: Oxford University Press; 2011. pp. 114–123.
  44. [45]Krallinger M Rabal O Leitner F Vazquez M Salgado D Lu Z The CHEMDNER corpus of chemicals and drugs and its annotation principles J Cheminformatics. 2015 7 1 1 17 10.1186/1758-2946-7-S1-S1 PMC4331692 25810773 · DOI
  45. [46]Gerner M Nenadic G Bergman CM LINNAEUS: a species name identification system for biomedical literature BMC Bioinforma. 2010 11 1 1 17 10.1186/1471-2105-11-85 PMC2836304 20149233 · DOI
  46. [47]Doğan RI Leaman R Lu Z NCBI disease corpus: a resource for disease name recognition and concept normalization J Biomed Inform. 2014 47 1 10 10.1016/j.jbi.2013.12.006 24393765 PMC3951655 · DOI
  47. [48]Honnibal M, Montani I. spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. neural machine translation. In: Proceedings of the Association for Computational Linguistics (ACL); 2017. pp. 688–697.
  48. [49]Eyre H, Chapman AB, Peterson KS, Shi J, Alba PR, Jones MM, et al. Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python. In: AMIA Annual Symposium Proceedings. vol. 2021. 6218 Georgia Avenue NW, Suite 1 PMB 3077 Washington, DC 20011. American Medical Informatics Association; 2021. p. 438. PMC8861690 35308962
  49. [50]Soysal E Wang J Jiang M Wu Y Pakhomov S Liu H CLAMP-a toolkit for efficiently building customized clinical natural language processing pipelines J Am Med Inform Assoc. 2018 25 3 331 336 10.1093/jamia/ocx132 29186491 PMC7378877 · DOI
  50. [51]Naseem U, Khushi M, Reddy VB, Rajendran S, Razzak I, Kim J. BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition. In: 2021 International Joint Conference on Neural Networks (IJCNN). 2021. pp. 1–7.
  51. [52]Kanakarajan Kr, Kundumani B, Sankarasubbu M. BioELECTRA:pretrained biomedical text encoder using discriminators. In: Demner-Fushman, D., Cohen, K.B., Ananiadou, S., Tsujii, J. (eds.) Proceedings of the 20th Workshop on Biomedical Language Processing. Association for Computational Linguistics, Online; 2021. pp. 143–154.
  52. [53]Beltagy I, Lo K, Cohan A. SciBERT: A pretrained language model for scientific text. 2019;3615–20. 10.18653/v1/D19-1371. · DOI · Paper page
  53. [54]Lee J Yoon W Kim S Kim D Kim S So CH BioBERT: a pre-trained biomedical language representation model for biomedical text mining Bioinformatics. 2020 36 4 1234 1240 10.1093/bioinformatics/btz682 31501885 PMC7703786 · DOI · Paper page
  54. [55]Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, Naumann T, Gao J, Poon H. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH). 2011;3(1):1–23.
  55. [56]Gururangan S, Marasovi ´c A, Swayamdipta S, Lo K, Beltagy I, Downey D, Smith NA. Don’t stop pretraining: Adapt language models to domains and tasks. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online; 2020. pp. 8342–8360.
  56. [57]Michalopoulos G, Wang Y, Kaka H, Chen H, Wong A. UmlsBERT: Clinical domain knowledge augmentation of contextual embeddings using the Unified Medical Language System Metathesaurus. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online; 2021. pp. 1744–1753. 10.18653/v1/2021.naacl-main.139. https://aclanthology.org/2021.naacl-main.139. · DOI
  57. [58]Raza S Reji DJ Shajan F Bashir SR Large-scale application of named entity recognition to biomedicine and epidemiology PLoS Digit Health. 2022 1 12 e0000152 10.1371/journal.pdig.0000152 36812589 PMC9931203 · DOI
  58. [59]Sanh V, Debut L, Chaumond J, Wolf T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. 2019. arXiv preprint arXiv:191001108.
  59. [60]Manzini E Garrido-Aguirre J Fonollosa J Perera-Lluna A Mapping layperson medical terminology into the Human Phenotype Ontology using neural machine translation models Expert Syst Appl. 2022 204 117446 10.1016/j.eswa.2022.117446 · DOI
  60. [61]Patterson OV Jones M Yao Y Viernes B Alba PR Iwashyna TJ Extraction of vital signs from clinical notes Stud Health Technol Inform. 2015 216 1035 26262334
  61. [62]Gavrilov D, Gusev A, Korsakov I, Novitsky R, Serova L. Feature extraction method from electronic health records in Russia. In: Conference of Open Innovations Association, FRUCT. Helsinki: FRUCT Oy; 2020. pp. 497–500. e-ISSN 2343-073.
  62. [63]Maffini MD, Ojea FA, Manzotti M. Automatic Detection of Vital Signs in Clinical Notes of the Outpatient Settings. In: MIE. 2020. pp. 1211–2. 32570584
  63. [64]Genes N Chandra D Ellis S Baumlin K Validating emergency department vital signs using a data quality engine for data warehouse Open Med Inform J. 2013 7 34 10.2174/1874431101307010034 24403981 PMC3881102 · DOI
  64. [65]Camburu O.-M, Rockt ¨aschel T, Lukasiewicz T, Blunsom P. e-snli: natural language inference with natural language explanations. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS’18, pp. 9560–9572. Red Hook: Curran Associates Inc.,; 2018.
  65. [66]Bowman S, Angeli G, Potts C, Manning C. A large annotated corpus for learning natural language inference, 632–642 (2015) 10.18653/v1/d15-1075. Publisher Copyright: © 2015 Association for Computational Linguistics.; Conference on Empirical Methods in Natural Language Processing, EMNLP 2015; Conference date: 17-09-2015 Through 21-09-2015 · DOI · Paper page
  66. [67]Kumar S, Talukdar P. NILE: Natural language inference with faithful natural language explanations. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online; 2020. pp. 8730–8742. 10.18653/v1/2020.acl-main.771. https://aclanthology.org/2020.acl-main.771. · DOI
  67. [72]Campos DG On the distinction between Peirce’s abduction and Lipton’s inference to the best explanation Synthese. 2011 180 3 419 442 10.1007/s11229-009-9709-3 · DOI
  68. [73]Dragulinescu S Inference to the best explanation and mechanisms in medicine Theor Med Bioeth. 2016 37 211 232 10.1007/s11017-016-9365-9 27193115 · DOI
  69. [74]Reiter E Dale R Building applied natural language generation systems Nat Lang Eng. 1997 3 1 57 87 10.1017/S1351324997001502 · DOI
  70. [75]Carenini G, Mittal VO, Moore JD. Generating patient-specific interactive natural language explanations. In: Proceedings of the Annual Symposium on Computer Application in Medical Care. American Medical Informatics Association; 218 Georgia Avenue NW, Suite 1 PMB 3077 Washington, DC 20011; 1994. p. 5. PMC2247744 7949978
  71. [76]Abujabal A, Saha Roy R, Yahya M, Weikum G. QUINT: Interpretable question answering over knowledge bases. In: Specia, L., Post, M., Paul, M. (eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Copenhagen: Association for Computational Linguistics; 2017. pp. 61–66. 10.18653/v1/D17-2011. https://aclanthology.org/D17-2011. · DOI
  72. [78]Albright D Lanfranchi A Fredriksen A Styler WF IV Warner C Hwang JD Towards comprehensive syntactic and semantic annotations of the clinical narrative J Am Med Inform Assoc. 2013 20 5 922 930 10.1136/amiajnl-2012-001317 23355458 PMC3756257 · DOI
  73. [80]Stenetorp P, Pyysalo S, Topi ´c G, Ohta T, Ananiadou S, Tsujii J. Brat: a web-based tool for nlp-assisted text annotation. In: Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics. EACL ’12. USA: Association for Computational Linguistics; 2012. pp. 102–107.
  74. [81]Fleiss JL Measuring nominal scale agreement among many raters Psychol Bull. 1971 76 5 378 10.1037/h0031619 · DOI · Paper page
  75. [82]Brown T Mann B Ryder N Subbiah M Kaplan JD Dhariwal P Language models are few-shot learners Adv Neural Inf Process Syst. 2020 33 1877 1901
  76. [84]Wei J, Bosma M, Zhao V, Guu K, Yu AW, Lester B, Du N, Dai AM, Le QV. Finetuned language models are zero-shot learners. In: International Conference on Learning Representations; 2022. https://openreview.net/forum?id=gEZrGCozdqR.
  77. [85]Wei J, Wang X, Schuurmans D, Bosma M, ichter Xia F, Chi EH, Le QV, Zhou D. Chain of thought prompting elicits reasoning in large language models. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems; 2022. https://openreview.net/forum?id=VjQlMeSBJ.
  78. [86]Wang X, Wei J, Schuurmans D, Le QV, Chi EH, Narang S, Chowdhery A, Zhou D. Self-consistency improves chain of thought reasoning in language models. In: The Eleventh International Conference on Learning Representations; 2023. https://openreview.net/forum?id=1PL1NIMMrw.
  79. [88]Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, et al. Transformers: State-of-the-Art Natural Language Processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics; 2020. pp. 38–45. https://www.aclweb.org/anthology/2020.emnlp-demos.6. Accessed 2024.
  80. [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. 10.1016/j.artmed.2021.102086. 34412834 · DOI
  81. [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.
  82. [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. 10.3115/v1/D14-1162. https://aclanthology.org/D14-1162. · DOI · Paper page
  83. [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. 10.18653/v1/D19-1410. https://aclanthology.org/D19-1410. · DOI · Paper page
  84. [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 10.26421/JDI3.4-5 · DOI
  85. [94]Ngai H, Rudzicz F. Doctor XAvIer: 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. 10.18653/v1/2022.bionlp-1.33. · DOI
  86. [96]Brahman F, Shwartz V, Rudinger R, Choi Y. Learning to rationalize for nonmonotonic reasoning with distant supervision. Proceedings of the AAAI Conference on Artificial Intelligence. 2021;35(14):12592–601. 10.1609/aaai.v35i14.17492. · DOI
  87. [97]Latcinnik V, Berant J. Explaining question answering models through text generation. 2020. arXiv preprint arXiv:200405569.
  88. [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. 10.7759/cureus.37432. PMC10173677 37182055 · DOI · Paper page
  89. [99]Miller T Explanation in artificial intelligence: Insights from the social sciences Artif Intell. 2019 267 1 38 10.1016/j.artint.2018.07.007 · DOI · Paper page

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Authors
Molinet, B., Marro, S., Cabrio, E., & Villata, S.
Affiliations
Centre National de la Recherche Scientifique (FR)
Corresponding author
Molinet, Benjamin
ORCID
Venue
Journal of Biomedical Semantics
Publisher
Springer Science and Business Media LLC
Published
2024-05-30
Volume
15
Issue
1
Pages
8-8
ISSN
2041-1480 (ISSN-L)
DOI
10.1186/s13326-024-00306-1
PMC
PMC11138001
OpenAlex
W4399180721
Cited by
4
References
4
Citations by year
2026: 2 · 2025: 2
FWCI
0.54
Type
journal article
Language
en
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  • Journal of Biomedical Semantics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
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Funders
  • CHIST-ERA XAI (Project-ANR-21-CHR4-0002)
  • 3IA Côte d'Azur (ANR-19-P3IA-0002)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
explainable AI, natural language explanations, diagnosis, clinical reasoning, information extraction, medical ontologies, UMLS, clinical case, medical knowledge base, clinical teaching, medical residents
MeSH terms
Artificial Intelligence, Diagnosis, Diagnostic Errors, Humans, Natural Language Processing, Unified Medical Language System

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 ›

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