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Skip search results from other journals and go to results- 5 Journal of Medical Internet Research
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Interpretability refers to how a model arrives at a decision, and explainability refers to why the decision is reached [16]. While explainability in AI has been a topic of interest since the 1980s, the recent accelerations in the widespread use of AI have resulted in guidelines on the responsible use of AI. Responsible AI requires a robust model that conveys the confidence and degree of uncertainty in prediction [16-18].
JMIR Med Inform 2025;13:e64617
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To address interpretability, we applied two complementary explainability techniques: SHAP and Grad-CAM, each offering unique insights into model behavior.
SHAP provides a feature attribution approach, assigning precise numerical contributions to each input feature—such as latency or main frequency—to quantify its role in the prediction process.
J Med Internet Res 2025;27:e63937
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This system addresses nonlinearity assumptions and enhances explainability. In addition, we applied the EACH score to predict perioperative stroke to assess its performance in real-world clinical practice and examined its performance compared with traditional scores and other ML-based scoring systems.
Perioperative stroke significantly impacts postoperative morbidity and mortality.
J Med Internet Res 2025;27:e58021
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LLMs have been shown to excel in tasks such as text generation and summarization, but their application in sensitive fields such as mental health requires careful consideration of factors such as ethical concerns, privacy, and model explainability.
As generative AI becomes increasingly integrated into various tools and applications, its presence in our everyday lives grows.
JMIR Res Protoc 2025;14:e62865
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A crucial determinant for successfully implementing the human-AI collaboration approach is decision transparency [32,33], which is often referred to as explainability. Explainability is the concept that an ML model’s prediction can be explained in a way that human operators can comprehend and reconstruct the model’s reasoning [33].
JMIR Hum Factors 2024;11:e53378
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The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review
Guidance for ethics and explainability is expected to meet the needs of a broad range of stakeholders, but what constitutes AI ethics and explainability best practices, who determines them, how principles are applied and regulated in practice, and how it is documented still needs to be defined [13,21,22,24,25,42].
Interact J Med Res 2023;12:e45903
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Nevertheless, explainability; transparency; and, most importantly, accountability and responsibility do not receive due consideration, in part because it remains difficult to transform these necessary concepts (eg, trust) into actual computational tools or metrics. Understanding why and how a particular model produced the observed predictions is of paramount importance, especially in health care applications.
J Med Internet Res 2023;25:e43838
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SHAP tool (version 0.40.0) was used to assess the explainability the model; that is, to identify factors protecting against chronic stress.
In addition to the performance evaluation, this study maximizes the interpretability of the underlying models. It focuses particularly on the explainability of the model, which can serve as an indispensable tool in the era of precision medicine.
JMIR AI 2023;2:e41868
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Levels of Autonomous Radiology
One of the major requirements to enable such human-AI interfaces in radiological workflows is an effective and consistent mapping of explainability with causability [18]. Specialized explainer systems of explainable AI (widely acknowledged as an important feature of practical deployment of AI models) aim at explaining AI inferences to human users [19].
Interact J Med Res 2022;11(2):e38655
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We evaluated the potential of our model to predict next-day stress and applied an explainability model to provide meaning to our predictions.
We collaborated with a private university’s obstetrics and gynecology clinic to recruit pregnant women into our study. To be eligible for enrollment, women had to be aged ≥18 years, enrolled at 10 to 18 weeks’ gestation with a singleton pregnancy, and own a smartphone.
JMIR Mhealth Uhealth 2022;10(8):e33850
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