[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126750-en":3,"doc-seo-126750-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126750,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","The Power of Trust - Designing Trustworthy Machine Learning Systems in Healthcare","Machine Learning (ML) systems can significantly enhance medical care, yet persistent skepticism limits adoption. A key obstacle is their perceived inscrutability, which undermines user trust and can trigger rejection. The paper proposes a user-centered, trustworthy ML design developed through design science research, using meta-requirements and design principles instantiated via mockups. Iterative design cycles refine the solution through focus groups, application reviews, and an online survey, followed by an end-user effectiveness test showing improved perceived trustworthiness.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \nRising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies ICIS 2023  \nData Analytics for Business and Societal Challenges  \nDec 11th, 12:00 AM  \nThe Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare  \nMariska Fecho  \nTechnische Universität Darmstadt, [mariska.fecho@tu-darmstadt.de](mariska.fecho@tu-darmstadt.de)  \nAnne Zöll  \nTU Darmstadt, [anne.zoell@tu-darmstadt.de](anne.zoell@tu-darmstadt.de)  \nFollow this and additional works at: [https://aisel.aisnet.org/icis2023](https://aisel.aisnet.org/icis2023)  \nRecommended Citation  \nFecho, Mariska and Zöll, Anne, \"The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare\" (2023) . Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies ICIS 2023. 3.  \n[https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/3](https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/3)  \nThis material is brought to you by the International Conference on Information Systems (ICIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies ICIS 2023 by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org).  \nThe Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare  \nCompleted Research Paper  \nMariska Fecho 1  \nTechnical University of Darmstadt Hochschulstraße 1, 64289 Darmstadt [mariska.fecho@tu-darmstadt.de](mariska.fecho@tu-darmstadt.de)  \nAnne Zöll1  \nTechnical University of Darmstadt Hochschulstraße 1, 64289 Darmstadt [anne.zoell@tu-darmstadt.de](anne.zoell@tu-darmstadt.de)  \nAbstract  \nMachine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection.  \nConsequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy.  \nKeywords: Trust, Machine Learning, Healthcare, Design Science Research, Trustworthy AI, Artificial Intelligence  \nIntroduction  \nOver the recent years, and particularly during the COVID-19 pandemic, the healthcare sector has been subject to unprecedented strains and challenges, repeatedly testing its limits worldwide (Tong et al. 2022) . One outstanding challenge is that physicians are overworked, and patients often have to wait months for appointments. Beyond that, an increasingly aging society demands additional medical care, underlining the need for scalable and accessible solutions that can alleviate the burden on the healthcare sector while improving patient outcomes. The growing prevalence of Information Systems (IS) in healthcare, specifically Machine Learning (ML) systems designed to aid in medical diagnoses, is anticipated to transform the provision of medical services, potentially serving as the primary point of contact for patient care (Wang and Siau 2018) . ML systems are able to identify diseases like cancer and strokes from medical images or","cbCaitXL9hTpjSrz","https://ap.wps.com/l/cbCaitXL9hTpjSrz","pdf",676930,1,18,"English","en",105,"# Introduction\n# Trustworthy Machine Learning Design Approach\n## Design science research and kernel theory basis\n## Iterative design cycles and evaluation methods\n## End-user effectiveness testing and results","[{\"question\":\"Why do users remain skeptical about machine learning in healthcare?\",\"answer\":\"Skepticism is driven largely by concerns about ML system inscrutability, which can reduce end users’ trust and lead to rejection. Additional factors include insufficient performance and privacy concerns.\"},{\"question\":\"How does the paper design a trustworthy ML system?\",\"answer\":\"It uses design science research to create a user-centered design based on meta-requirements and design principles, instantiated through mockups and grounded in a kernel theory of Trustworthy Artificial Intelligence.\"},{\"question\":\"What evidence supports the effectiveness of the proposed design?\",\"answer\":\"The design is refined through three design cycles using focus group discussions, evaluation of existing applications, and an online survey, then validated with an end-user effectiveness test. The results show end users perceive the design as more trustworthy.\"}]","The Power of Trust - Designing Trustworthy Machine Learning Systems in Healthcare | PDF",1785934582,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-power-of-trust-designing-trustworthy-machine-learning-systems-in-healthcare","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-power-of-trust-designing-trustworthy-machine-learning-systems-in-healthcare/126750/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why do users remain skeptical about machine learning in healthcare?","Question",{"text":76,"@type":77},"Skepticism is driven largely by concerns about ML system inscrutability, which can reduce end users’ trust and lead to rejection. Additional factors include insufficient performance and privacy concerns.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the paper design a trustworthy ML system?",{"text":81,"@type":77},"It uses design science research to create a user-centered design based on meta-requirements and design principles, instantiated through mockups and grounded in a kernel theory of Trustworthy Artificial Intelligence.",{"name":83,"@type":74,"acceptedAnswer":84},"What evidence supports the effectiveness of the proposed design?",{"text":85,"@type":77},"The design is refined through three design cycles using focus group discussions, evaluation of existing applications, and an online survey, then validated with an end-user effectiveness test. 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