Abstract
Future healthcare ecosystems integrating human-centered artificial intelligence (AI) will be indispensable. AI-based healthcare technologies can support diagnosis processes and make healthcare more accessible globally. In this context, we conducted a design science research project intending to introduce design principles for user interfaces (UIs) of explainable AI-based (XAI) medical decision support systems (XAI-based MDSS). We used an archaeological approach to analyze the UI of an existing web-based system in the context of skin lesion classification called DIAnA (Dermatological Images – Analysis and Archiving). One of DIAnA’s unique characteristics is that it should be usable for the stakeholder groups of physicians and patients. We conducted the in-situ analysis with these stakeholders using the think-aloud method and semi-structured interviews. We anchored our interview guide in concepts of the Theory of Interactive Media Effects (TIME), which formulates UI features as causes and user psychology as effects. Based on the results, we derived 20 design requirements and developed nine design principles grounded in TIME for this class of XAI-based MDSS, either associated with the needs of physicians, patients, or both. Regarding evaluation, we first conducted semi-structured interviews with software developers to assess the reusability of our design principles. Afterward, we conducted a survey with user experience/interface designers. The evaluation uncovered that 77% of the participants would adopt the design principles, and 82% would recommend them to colleagues for a suitable project. The findings prove the reusability of the design principles and highlight a positive perception by potential implementers.
Keywords
- Design Science Research
- Design Principles
- Explainable Artificial Intelligence
- Medical Decision Support Systems
- Healthcare
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Bulc, V., Hart, B., Hannah, M., Hrovatin, B.: Society 5.0 and a human centred health care. In: Simini, F., Bertemes-Filho, P. (eds.) Medicine-Based Informatics and Engineering. LNB, pp. 147–177. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-87845-0_9
Rojas, C.N., Penafiel, G.A.A., Buitrago, D.F.L., Romero, C.A.T.: Society 5.0: a Japanese Concept for a super intelligent society. Sustainability 13, 1–16 (2021)
Sahoo, P.R., Chatterjee, S.M.: Threats and challenges of artificial intelligence in healthcare industry. In: Zhang, Y.D., Senjyu, T., So-In, C., Joshi, A. (eds.) Smart Trends in Computing and Communications, LNNS, vol. 396, pp. 761–770. Springer, Singapore (2023)
Tschandl, P., et al.: Comparison for the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet oncology 20(7), 938–947 (2019)
Tschandl, P., et al.: Human-coputer collaboration for skin cancer recognition 26, 1229–1234 (2020)
Arrieta, A.B., et al.: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)
Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)
Payrovnaziri, S.N., et al.: Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review. J. Am. Med. Inform. Assoc. 27(7), 1173–1185 (2020)
Schoonderwoerd, T.A.J., Jorritsma, W., Neerincx, M.A., van den Bosch, K.: Human-centered XAI: developing design patterns for explanations of clinical decision support systems. Int. J. Hum. Comput. Stud. 154, 1–25 (2021)
Barda, A.J., Horvat, C.M., Hochheiser, H.: A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare. BMC Med. Inform. Decis. Mak. 20, 1–16 (2020)
Meske, C., Bunde, E.: Transparency and trust in human-AI-interaction: the role of model-agnostic explanations in computer vision-based decision support. In: Degen, H., Reinerman-Jones, L. (eds.) HCII 2020. LNCS, vol. 12217, pp. 54–69. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50334-5_4
Chandra Kruse, L., Seidel, S., vom Brocke, J.: Design archaeology: generating design knowledge from real-world artifact design. In: Tulu, B., Djamasbi, S., Leroy, G. (eds.) DESRIST 2019. LNCS, vol. 11491, pp. 32–45. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19504-5_3
Sonntag, D., Nunnari, F., Profitlich, H.-J.: The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. DFKI Tech. Rep. H2020, 1–20 (2020)
Meske, C., Bunde, E., Schneider, J., Gersch, M.: Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Inf. Syst. Manag. 39(1), 53–63 (2022)
Fernandez, A., Insfran, E., Abrahao, S.: Usability evaluation methods for the web: a systematic mapping study. Inf. Softw. Technol. 53, 789–817 (2011)
Sundar, S.S., Jia, H., Waddell, T.F., Huang, Y.: The Handbook of Psychology of Communication Technology, 1st edn. Wiley, Hoboken (2015)
Gregor, S., Chandra Kruse, L., Seidel, S.: Research perspectives: the anatomy of a design principle. J. Assoc. Inf. Syst. 21(6), 1622–1652 (2020)
Sundar, S.S.: Rise of machine agency: a framework for studying the psychology of Human-AI Interaction (HAII). J. Comput.-Mediat. Commun. 25, 74–88 (2020)
Ivari, J., Hansen, M.R.P., Haj-Bolouri, A.: A proposal for minimum reusability evaluation of design principles. Eur. J. Inf. Syst. 30(3), 286–303 (2021)
Dwivedi, A., Dwivedi, S.S., Tariq, M.R., Qiu, X., Hong, S., Xin, Y.: Scope of artificial intelligence in medicine. J. Res. Med. Dent. Sci. 8(3), 137–140 (2020)
Westerbeek, L., et al.: Barriers and facilitators influencing medication-related CDSS acceptance according to clinicians: a systematic review. Int. J. Med. Inform. 152, 1–14 (2021)
Westerbeek, L., de Bruijn, G.-J., van Weert, H.C., Abu-Hanna, A., Medlock, S., van Weert, J.C.M.: General practitioners’ needs and wishes for clinical decision support systems: a focus group study. Int. J. Med. Inform. 168, 1–7 (2022)
Fuji, K.T., Abbot, A.A., Galt, K.A., Drincic, A., Kraft, M., Kasha, T.: Standalone personal health records in the United States: meeting patient desires. Health Technol. 2, 197–205 (2012)
Attfield, S.J., Adams, A., Blandford, A.: Patient information needs: pre- and post-consultation. Health Inform. J. 12(2), 165–177 (2006)
Jefford, M., Tattersall, M.H.: Informing and involving cancer patients in their own care. Lancet Oncol. 3(10), 629–637 (2002)
Hwang, A.H.-C., Oh, J.: Interacting with background music engages E-Customers more: the impact of interactive music on consumer perception and behavioral intention. J. Retail. Consum. Serv. 54, 1–15 (2020)
Sun, Y., Sundar, S.S.: Exploring the effects of interactive dialogue in improving user control for explainable online symptom checkers. In: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–7. Association for Computing Machinery, New Orleans, LA, USA (2022)
Kuechler, W., Vaishnavi, V.: A framework for theory development in design science research: multiple perspectives. J. Assoc. Inf. Syst. 13(6), 395–423 (2012)
Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)
Xu, W., Zammit, K.: Applying thematic analysis to education: a hybrid approach to interpreting data in practitioner research. Int. J. Qual. Methods 19, 1–9 (2020)
Kreuter, M.W., Strecher, V.J., Glassman, B.: One size does not fit all: the case for tailoring print materials. Ann. Behav. Med. 21, 276–283 (1999)
Ku, O., Hou, C.-C., Chen, S.Y.: Incorporating customization and personalization into game-based learning: a cognitive style perspective. Comput. Hum. Behav. 5, 359–368 (2016)
Sundar, S. S., Oh, J., Bellur, S., Jia, H., Kim, H.-S.: Interactivity as self-expression: a field experiment with customization and blogging. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 395–404. Association for Computing Machinery, Austin, Texas, USA (2012)
Bunde, E., Kühl, N., Meske, C.: Fake or credible? towards designing services to support users’ credibility assessment of news content. In: Proceedings of the 55th Hawaii International Conference on System Sciences, pp. 1883–1892 (2021)
Meske, C., Bunde, E.: Design principles for user interfaces in AI-based decision support systems: the case of explainable hate speech detection. Inf. Syst. Front. 1–31 (2022)
Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37(2), 337–355 (2013)
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This research is partly funded by the pAItient project (BMG, 2520DAT0P2).
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Bunde, E., Eisenhardt, D., Sonntag, D., Profitlich, HJ., Meske, C. (2023). Giving DIAnA More TIME – Guidance for the Design of XAI-Based Medical Decision Support Systems. In: Gerber, A., Baskerville, R. (eds) Design Science Research for a New Society: Society 5.0. DESRIST 2023. Lecture Notes in Computer Science, vol 13873. Springer, Cham. https://doi.org/10.1007/978-3-031-32808-4_7
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