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Transparency in Machine Learning (ML), attempts to reveal the workingmechanisms of complex models. Transparent ML promises to advance human factorsengineering goals of human-centered AI in the target users. From ahuman-centered design perspective, transparency is not a property of the MLmodel but an affordance, i.e. a relationship between algorithm and user; as aresult, iterative prototyping and evaluation with users is critical toattaining adequate solutions that afford transparency. However, followinghuman-centered design principles in healthcare and medical image analysis ischallenging due to the limited availability of and access to end users. Toinvestigate the state of transparent ML in medical image analysis, we conducteda systematic review of the literature. Our review reveals multiple severeshortcomings in the design and validation of transparent ML for medical imageanalysis applications. We find that most studies to date approach transparencyas a property of the model itself, similar to task performance, withoutconsidering end users during neither development nor evaluation. Additionally,the lack of user research, and the sporadic validation of transparency claimsput contemporary research on transparent ML for medical image analysis at riskof being incomprehensible to users, and thus, clinically irrelevant. Toalleviate these shortcomings in forthcoming research while acknowledging thechallenges of human-centered design in healthcare, we introduce the INTRPRTguideline, a systematic design directive for transparent ML systems in medicalimage analysis. The INTRPRT guideline suggests formative user research as thefirst step of transparent model design to understand user needs and domainrequirements. Following this process produces evidence to support designchoices, and ultimately, increases the likelihood that the algorithms affordtransparency.

Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review