# Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability Source: [Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:50:54.619Z Authors: Mohammad Hossein Najafi, Mohammad Morsali, Mohammadreza Pashanejad, Saman Soleimani Roudi, Mohammad Norouzi, Saeed Bagheri Shouraki Published: 2025-04-07T20:26:02.000Z ## Abstract Deep neural networks for medical image classification often fail to generalize consistently in clinical practice due to violations of the i.i.d. assumption and opaque decision-making. This paper examines interpretability in deep neural networks fine-tuned for fracture detection by evaluating model performance against adversarial attack and comparing interpretability methods to fracture regions annotated by an orthopedic surgeon. Our findings prove that robust models yield explanations more aligned with clinically meaningful areas, indicating that robustness encourages anatomically relevant feature prioritization. We emphasize the value of interpretability for facilitating human-AI collaboration, in which models serve as assistants under a human-in-the-loop paradigm: clinically plausible explanations foster trust, enable error correction, and discourage reliance on AI for high-stakes decisions. This paper investigates robustness and interpretability as complementary benchmarks for bridging the gap between benchmark performance and safe, actionable clinical deployment. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()