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Cited by 302Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and EducationUsability and User Interface Design

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Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review

Open access
Chen, Haomin, Gomez, Catalina, Huang, Chien-Ming, Unberath, Mathias·2022·npj Digital Medicine
DOI 10.1038/s41746-022-00699-2PMC PMC9581990
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Abstract

Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users and the knowledge imbalance between those users and ML designers. To investigate the state of transparent ML in medical image analysis, we conducted a systematic review of the literature from 2012 to 2021 in PubMed, EMBASE, and Compendex databases. We identified 2508 records and 68 articles met the inclusion criteria. Current techniques in transparent ML are dominated by computational feasibility and barely consider end users, e.g. clinical stakeholders. Despite the different roles and knowledge of ML developers and end users, no study reported formative user research to inform the design and development of transparent ML models. Only a few studies validated transparency claims through empirical user evaluations. These shortcomings put contemporary research on transparent ML at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research, we introduce the INTRPRT guideline, a design directive for transparent ML systems in medical image analysis. The INTRPRT guideline suggests human-centered design principles, recommending formative user research as the first step to understand user needs and domain requirements. Following these guidelines increases the likelihood that the algorithms afford transparency and enable stakeholders to capitalize on the benefits of transparent ML.

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Fig. 1 Schematic representation of the INTRPRT guideline within the main stages of a human-centered design process. The blue boxes demonstrate the process from understanding end users and their context to the validation of the developed system, which ultimately might result in large scale deployment. The guidelines are summarized below and are located within the design phases based on the aspects pertinent to each one and the corresponding themes of each guideline are listed on the left. Opportunities for iterative design are illustrated with the dashed arrows.
Fig. 1 Schematic representation of the INTRPRT guideline within the main stages of a human-centered design process. The blue boxes demonstrate the process from understanding end users and their context to the validation of the developed system, which ultimately might result in large scale deployment. The guidelines are summarized below and are located within the design phases based on the aspects pertinent to each one and the corresponding themes of each guideline are listed on the left. Opportunities for iterative design are illustrated with the dashed arrows.CC BY · PubMed Central
Fig. 2 Illustrative examples of different techniques used in transparent ML systems for classification and segmentation tasks from the systematic review. Within each task, a non-interpretable model generates the task outcome from the input image (top). The use of clinical knowledge or computer vision information as priors attempts to add transparency in the outcome generation process (bottom). Images retrieved from the ORIGA127 and BraTS2020 datasets128.
Fig. 2 Illustrative examples of different techniques used in transparent ML systems for classification and segmentation tasks from the systematic review. Within each task, a non-interpretable model generates the task outcome from the input image (top). The use of clinical knowledge or computer vision information as priors attempts to add transparency in the outcome generation process (bottom). Images retrieved from the ORIGA127 and BraTS2020 datasets128.CC BY · PubMed Central
Fig. 3 Relationships of components in ML systems. a Shows the relationship between the clinical scenario and requirements in non-transparent ML systems, where the system may not be human-facing, and as such, meeting quantitative performance requirements is sufficient. b Shows the relationship between the clinical scenario, requirements, and end users in transparent ML systems, as they arise in a human-centered system that seeks to enable users to accomplish a certain task.
Fig. 3 Relationships of components in ML systems. a Shows the relationship between the clinical scenario and requirements in non-transparent ML systems, where the system may not be human-facing, and as such, meeting quantitative performance requirements is sufficient. b Shows the relationship between the clinical scenario, requirements, and end users in transparent ML systems, as they arise in a human-centered system that seeks to enable users to accomplish a certain task.CC BY · PubMed Central
Fig. 4 PRISMA diagram for transparent ML in medical imaging. The flow diagram shows the number of records identified, of studies excluded and the reasons for exclusion, and of studies included in our systematic review.
Fig. 4 PRISMA diagram for transparent ML in medical imaging. The flow diagram shows the number of records identified, of studies excluded and the reasons for exclusion, and of studies included in our systematic review.CC BY · PubMed Central

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Metadata

Authors
Chen, H., Gomez, C., Huang, C.-M., & Unberath, M.
Affiliations
Johns Hopkins University (US)
Corresponding author
Unberath, Mathias
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2022-10-19
Volume
5
Issue
1
Pages
156-156
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-022-00699-2
PMC
PMC9581990
OpenAlex
W4306790271
Cited by
300
References
101
Citations by year
2026: 97 · 2025: 94 · 2024: 67 · 2023: 38 · 2022: 4
FWCI
3.61
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords
explainable AI, machine learning transparency, human-centered design, medical image analysis, user-centered design, user evaluation

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