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Involving clinicians in the co-design of interpretable rather than fully transparent systems could thus be a solution to solving the explainable AI conundrum [41]. Given the high safety risks for patients, these considerations are particularly important for “diagnostic decision-making,” “prescribing medication or treatment,” and “analyzing medical data.” Also, both stakeholder groups agreed that at levels 2 and 3, both the control over and responsibility for system outcomes must reside with clinicians.
J Med Internet Res 2024;26:e50130
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The first experiment was performed to compare the proposed method with similar graph algorithms, while the second experiment was performed to compare the proposed method with other common explainable ML methods. The evaluation metrics of the algorithm are accuracy, precision, sensitivity, specificity, F1-score, area under receiver operating characteristic curve (AUC), etc.
JMIR Med Inform 2024;12:e57678
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Moreover, we enhanced the explainability of the ML classifiers by using an explainable AI technique. Furthermore, we have investigated the ML classifier’s performance under 2 conditions, differentiated by whether the explanation is valid for the predicted event type. Based on this analysis, we offer recommendations for optimizing human-AI collaboration in the context of PSE report classification.
JMIR Hum Factors 2024;11:e53378
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