{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:15:17Z","timestamp":1760832917858,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032083326","type":"print"},{"value":"9783032083333","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>As (explainable) artificial intelligence becomes increasingly integrated into high-stakes domains like healthcare, it is paramount to understand what makes explanations effective and convincing. In this work, we propose an approach that integrates argumentation schemes and Bayesian networks. The goal is to enhance the transparency and interpretability of medical diagnostic decision-making based on machine learning models. We design a novel argumentation scheme based on Walton\u2019s abductive inference scheme that captures the reasoning process underlying medical diagnoses. The proposed scheme functions as a structured explanation template, which is instantiated with the conditional probabilities derived from a Bayesian network. These conditional probabilities are turned into statistical evidence that can support or challenge a conclusion made explicit in the argumentative scheme, thereby providing a robust and transparent basis for decision-making. The resulting explanations were evaluated in a user study by medical experts, who assessed their value and answered targeted questions about their usefulness and clarity. We present the results of this user study and provide directions for future work.<\/jats:p>","DOI":"10.1007\/978-3-032-08333-3_7","type":"book-chapter","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T05:22:40Z","timestamp":1760764960000},"page":"138-158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explanations for\u00a0Medical Diagnosis Predictions Based on\u00a0Argumentation Schemes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2556-9430","authenticated-orcid":false,"given":"Felix","family":"Liedeker","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3483-265X","authenticated-orcid":false,"given":"Olivia","family":"Sanchez-Graillet","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8666-1640","authenticated-orcid":false,"given":"Christian","family":"Brandt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2919-0496","authenticated-orcid":false,"given":"J\u00f6rg","family":"Wellmer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4771-441X","authenticated-orcid":false,"given":"Philipp","family":"Cimiano","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,19]]},"reference":[{"issue":"1","key":"7_CR1","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1001\/jama.1987.03400010071030","volume":"258","author":"GO Barnett","year":"1987","unstructured":"Barnett, G.O., Cimino, J.J., Hupp, J.A., Hoffer, E.P.: DXplain: an evolving diagnostic decision-support system. JAMA 258(1), 67\u201374 (1987)","journal-title":"JAMA"},{"key":"7_CR2","doi-asserted-by":"publisher","unstructured":"Bussone, A., Stumpf, S., O\u2019Sullivan, D.: The role of explanations on trust and reliance in clinical decision support systems. In: 2015 International Conference on Healthcare Informatics, pp. 160\u2013169 (2015). https:\/\/doi.org\/10.1109\/ICHI.2015.26","DOI":"10.1109\/ICHI.2015.26"},{"key":"7_CR3","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"7_CR4","doi-asserted-by":"publisher","unstructured":"\u010cyras, K., Rago, A., Albini, E., Baroni, P., Toni, F.: Argumentative XAI: A Survey (2021). https:\/\/doi.org\/10.48550\/arXiv.2105.11266","DOI":"10.48550\/arXiv.2105.11266"},{"issue":"2","key":"7_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/0004-3702(94)00041-X","volume":"77","author":"PM Dung","year":"1995","unstructured":"Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321\u2013357 (1995). https:\/\/doi.org\/10.1016\/0004-3702(94)00041-X","journal-title":"Artif. Intell."},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Eleanor\u00a0Mill, Wolfgang\u00a0Garn, N.R.T., Turner, C.: The SAGE framework for explaining context in explainable artificial intelligence. Appl. Artif. Intell. 38(1), 2318670 (2024). https:\/\/doi.org\/10.1080\/08839514.2024.2318670","DOI":"10.1080\/08839514.2024.2318670"},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.compbiomed.2016.08.016","volume":"77","author":"T Exarchos","year":"2016","unstructured":"Exarchos, T., Rigas, G., Bibas, A., Kikidis, D., Nikitas, C., et al.: Mining balance disorders\u2019 data for the development of diagnostic decision support systems. Comput. Biol. Med. 77, 240\u2013248 (2016). https:\/\/doi.org\/10.1016\/j.compbiomed.2016.08.016","journal-title":"Comput. Biol. Med."},{"key":"7_CR8","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., Giannotti, F.: Local rule-based explanations of black box decision systems (2018)"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Jia, Y., McDermid, J., Habli, I.: Enhancing the value of counterfactual explanations for deep learning. In: International Conference on AI in Medicine, pp. 389\u2013394. Springer (2021)","DOI":"10.1007\/978-3-030-77211-6_46"},{"key":"7_CR10","doi-asserted-by":"publisher","unstructured":"Kondo, T., Washio, K., Hayashi, K., Miyao, Y.: Bayesian argumentation-scheme networks: a probabilistic model of argument validity facilitated by argumentation schemes. In: Proceedings of the 8th Workshop on Argument Mining, pp. 112\u2013124. Association for Computational Linguistics, Punta Cana, Dominican Republic (2021). https:\/\/doi.org\/10.18653\/v1\/2021.argmining-1.11","DOI":"10.18653\/v1\/2021.argmining-1.11"},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Krause, J., Perer, A., Ng, K.: Interacting with predictions: visual inspection of black-box machine learning models. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 5686\u20135697. CHI \u201916, Association for Computing Machinery, New York, NY, USA (2016). https:\/\/doi.org\/10.1145\/2858036.2858529","DOI":"10.1145\/2858036.2858529"},{"issue":"3","key":"7_CR12","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1214\/15-AOAS848","volume":"9","author":"B Letham","year":"2015","unstructured":"Letham, B., Rudin, C., McCormick, T.H., Madigan, D.: Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model. Ann. Appl. Stat. 9(3), 1350\u20131371 (2015). https:\/\/doi.org\/10.1214\/15-AOAS848","journal-title":"Ann. Appl. Stat."},{"key":"7_CR13","unstructured":"Liedeker, F., Sanchez-Graillet, O., Seidler, M., Brandt, C., Wellmer, J., Cimiano, P.: A user study evaluating argumentative explanations in diagnostic decision support. In: The 1st Workshop on Natural Language Argument-Based Explanations. Santiago de Compostela, Spain (2024)"},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Lundberg, S., Lee, S.I.: A Unified Approach to Interpreting Model Predictions (2017). https:\/\/doi.org\/10.48550\/arXiv.1705.07874","DOI":"10.48550\/arXiv.1705.07874"},{"key":"7_CR15","doi-asserted-by":"publisher","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), pp. 4768\u20134777 (2017). https:\/\/doi.org\/10.5555\/3295222.3295230","DOI":"10.5555\/3295222.3295230"},{"key":"7_CR16","unstructured":"MJ, H.: Verbal probability terms for communicating clinical risk - a systematic review. Ulster Med. J. 93(1), 18\u201323 (2024)"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Pearl, J.: Causality. Cambridge University Press (2009)","DOI":"10.1017\/CBO9780511803161"},{"issue":"1","key":"7_CR18","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81\u2013106 (1986). https:\/\/doi.org\/10.1007\/BF00116251","journal-title":"Mach. Learn."},{"key":"7_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2024.101243","volume":"86","author":"CO Retzlaff","year":"2024","unstructured":"Retzlaff, C.O., et al.: Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists. Cogn. Syst. Res. 86, 101243 (2024). https:\/\/doi.org\/10.1016\/j.cogsys.2024.101243","journal-title":"Cogn. Syst. Res."},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should I trust you?\u201d Explaining the predictions of any classifier. In: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"issue":"1","key":"7_CR21","doi-asserted-by":"publisher","first-page":"3923","DOI":"10.1038\/s41467-020-17419-7","volume":"11","author":"JG Richens","year":"2020","unstructured":"Richens, J.G., Lee, C.M., Johri, S.: Improving the accuracy of medical diagnosis with causal machine learning. Nat. Commun. 11(1), 3923 (2020). https:\/\/doi.org\/10.1038\/s41467-020-17419-7","journal-title":"Nat. Commun."},{"key":"7_CR22","doi-asserted-by":"publisher","DOI":"10.1136\/jme-2024-110074","author":"R Rosenbacke","year":"2024","unstructured":"Rosenbacke, R., Melhus, \u00c5., McKee, M., Stuckler, D.: AI and XAI second opinion: the danger of false confirmation in human-AI collaboration. J. Med. Ethics (2024). https:\/\/doi.org\/10.1136\/jme-2024-110074","journal-title":"J. Med. Ethics"},{"key":"7_CR23","doi-asserted-by":"publisher","unstructured":"S Band, S., et al.: Application of explainable artificial intelligence in medical health: a systematic review of interpretability methods. Inf. Med. Unlocked 40, 101286 (2023). https:\/\/doi.org\/10.1016\/j.imu.2023.101286","DOI":"10.1016\/j.imu.2023.101286"},{"key":"7_CR24","doi-asserted-by":"publisher","unstructured":"Sevilla, J.: Finding, scoring and explaining arguments in Bayesian networks. arXiv preprint arXiv:2112.00799 (2021). https:\/\/doi.org\/10.48550\/arXiv.2112.00799","DOI":"10.48550\/arXiv.2112.00799"},{"key":"7_CR25","doi-asserted-by":"publisher","unstructured":"Theunissen, M., Browning, J.: Putting explainable AI in context: institutional explanations for medical AI. Ethics Inf. Technol. 24(2) (2022). https:\/\/doi.org\/10.1007\/s10676-022-09649-8","DOI":"10.1007\/s10676-022-09649-8"},{"key":"7_CR26","doi-asserted-by":"publisher","unstructured":"Timmer, S.T., Meyer, J.J.C., Prakken, H., Renooij, S., Verheij, B.: Explaining Bayesian networks using argumentation. In: Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp. 83\u201392. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-20807-7_8","DOI":"10.1007\/978-3-319-20807-7_8"},{"key":"7_CR27","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888921000011","volume":"36","author":"A Vassiliades","year":"2021","unstructured":"Vassiliades, A., Bassiliades, N., Patkos, T.: Argumentation and explainable artificial intelligence: a survey. Knowl. Eng. Rev. 36, e5 (2021). https:\/\/doi.org\/10.1017\/S0269888921000011","journal-title":"Knowl. Eng. Rev."},{"key":"7_CR28","doi-asserted-by":"publisher","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR (2018). https:\/\/doi.org\/10.48550\/arXiv.1711.00399","DOI":"10.48550\/arXiv.1711.00399"},{"key":"7_CR29","doi-asserted-by":"publisher","unstructured":"Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press (2008). https:\/\/doi.org\/10.1017\/CBO9780511802034","DOI":"10.1017\/CBO9780511802034"},{"key":"7_CR30","doi-asserted-by":"publisher","unstructured":"Wang, D., Yang, Q., Abdul, A., Lim, B.Y.: Designing theory-driven user-centric explainable AI. In: Proceedings the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1\u201315. CHI \u201919, Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3290605.3300831","DOI":"10.1145\/3290605.3300831"},{"key":"7_CR31","doi-asserted-by":"publisher","unstructured":"Wardrope, A., Jamnadas-Khoda, J., Broadhurst, M., Gr\u00fcnewald, R.A., et\u00a0al.: Machine learning as a diagnostic decision aid for patients with transient loss of consciousness. Neurol. Clin. Pract. 10(2), 96\u2013105 (2020). https:\/\/doi.org\/10.1212\/CPJ.0000000000000726","DOI":"10.1212\/CPJ.0000000000000726"},{"issue":"1","key":"7_CR32","first-page":"309","volume":"42","author":"C Yuan","year":"2011","unstructured":"Yuan, C., Lim, H., Lu, T.C.: Most relevant explanation in Bayesian networks. J. Artif. Intell. Res. 42(1), 309\u2013352 (2011)","journal-title":"J. Artif. Intell. Res."}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08333-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T06:02:32Z","timestamp":1760767352000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08333-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"ISBN":["9783032083326","9783032083333"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08333-3_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,19]]},"assertion":[{"value":"19 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Istanbul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"T\u00fcrkiye","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/xaiworldconference.com\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}