Medical imaging techniques, encompassing modalities such as X-ray, CT, MRI, ultrasound, and PET, are fundamental to modern healthcare, playing a vital role in diagnosis, disease monitoring, treatment planning, and guiding interventions across various medical specialties. The exponential growth in the volume and complexity of medical image data has spurred the rapid development and application of Artificial Intelligence (AI) algorithms for automated analysis, interpretation, and decision support.

AI holds significant promise for enhancing the efficiency, accuracy, and accessibility of medical image analysis, potentially leading to earlier disease detection, personalized treatment strategies, and reduced workload for healthcare professionals. However, the successful and ethical integration of AI into routine clinical practice hinges on establishing trust in these technologies. This necessitates a comprehensive focus on the reliability, robustness, fairness, transparency, explainability, and accountability of AI systems deployed in the sensitive domain of medical imaging. A lack of trust can lead to clinician skepticism, potential misinterpretations, and ultimately impede the beneficial adoption of AI in healthcare.

This Research Topic aims to explore the multifaceted aspects of Trustworthy AI (TAI) in the broad context of medical imaging. We will examine:
• The current landscape of AI applications across different imaging modalities and clinical domains,
• Overarching challenges to trustworthiness,
• Methodologies and frameworks for building and evaluating TAI systems applicable to diverse medical image analysis tasks,
• Case studies highlighting successful TAI systems in clinical settings,
• Future directions for fostering responsible innovation and clinical translation.

All article types are welcome, though Original Research, Brief Research Reports, Methods, Reviews, Systematic Reviews, Mini Reviews, and Case Reports are encouraged.

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Fostering Trustworthy Artificial Intelligence in Medical Imaging