Radiologists Who Expect Explainable AI in Medical Image Analysis: Peering Into Their Thoughts
Exploring Radiologists' Expectations of Explainable Machine Learning Models in Medical Image Analysis
HCI Today summarized the key points
- •This article reports a study investigating what radiologists expect from explainable ML models for medical image analysis.
- •The research team surveyed 46 radiologists and residents, confirming that ML is useful for managing clinical workflows, handling repetitive tasks, and identifying urgent findings.
- •Physicians wanted the model to clearly show important image features, and they also requested explanations such as heatmaps, written descriptions, similar cases, and SRTCS.
- •They also found that explanations are a key condition for building trust, while concerns about bias in data and training, as well as accuracy, emerged as major worries that can block real-world adoption.
- •The study suggests that teams should define the problems together with clinicians, strengthen explanation and verification, and then design ML tailored to radiology and day-to-day work.
This summary was generated by an AI editor based on HCI expert perspectives.
Why Read This from an HCI Perspective
This article looks at medical AI not as a problem of having a merely “accurate model,” but as a question of how people in real clinical settings verify, trust, and reconsider their judgments. In particular, it addresses explainability, trust, and the pathways for user involvement together—making it highly relevant for HCI and UX practitioners. It helps readers understand why a model that looks good may still fail to be adopted in practice, and it shows why interface and workflow design are central.
CIT's Commentary
The core of this study is that it treats explanations not as a “pretty add-on,” but as an interactive mechanism that enables clinicians to verify results and intervene. A key point is that, rather than relying on a simple heatmap, the most important cues are those that let users recalibrate their judgments—such as clinical terminology frameworks, similar cases, and confidence information. This also demonstrates that even with high AI performance, adoption is difficult if the interface is opaque. However, in real products there is a clear trade-off between providing more explanations and enabling fast, effortless use. As explanations increase, verification may become easier, but the screen can also become more complex. Therefore, an approach is needed that structures information in layers—for example, showing some information on the default screen and revealing other details only when needed.
Questions to Consider While Reading
- Q.To increase explainability, what information should be shown on the default screen, and what information should be displayed only when the user requests it?
- Q.Among heatmaps, similar cases, and clinical terminology frameworks, which explanation approach most builds trust in real clinical practice?
- Q.How can we verify whether explanations help users make better judgments—or instead inflate confidence too much?
This commentary was generated by an AI editor based on HCI expert perspectives.
Please refer to the original for accurate details.
Subscribe to Newsletter
Get the weekly HCI highlights delivered to your inbox every Friday.