Clinical bottom line
Artificial intelligence (AI) is increasingly integrated into radiology, enhancing diagnostic accuracy, workflow efficiency, and personalized patient care. Current applications span cancer imaging, stroke evaluation, and breast imaging, with AI algorithms assisting in lesion detection, characterization, and prognostication. Explainable AI methods are emerging to improve clinician trust and interpretability. Despite promising advances, challenges remain in clinical validation, regulatory approval, and integration into routine practice. Ongoing research and multidisciplinary collaboration are essential to realize AI’s full potential in radiology (Haupt et al., 2026; Taylor et al., 2023; Bi et al., 2019).
What the evidence shows
Bi et al. (2019) provide a comprehensive overview of AI in cancer imaging, highlighting improved lesion detection and tumor characterization across modalities. They emphasize AI’s role in addressing interobserver variability and accelerating image interpretation.
Taylor et al. (2023) review AI applications in breast imaging, demonstrating enhanced sensitivity and specificity in mammography and ultrasound, alongside risk stratification models that may personalize screening protocols.
Liu et al. (2024) discuss AI in ischemic stroke imaging, where rapid automated detection of infarcts and vessel occlusions facilitates timely intervention and improved outcomes.
Jiang et al. (2024) explore AI’s role in pancreaticobiliary endoscopy imaging, noting advances in lesion identification and procedural guidance.
Haupt et al. (2026) focus on explainable AI techniques in radiology, addressing the “black box” challenge by developing models that provide transparent decision rationales, which is critical for clinical adoption and regulatory compliance.
Yasaka et al. (2018) summarize foundational AI developments in radiology, underscoring the trajectory from research to clinical deployment.
Mathias et al. (2023) highlight broad healthcare AI trends, including radiology, emphasizing the need for robust validation and ethical frameworks.
Caveats and uncertainty
Many AI tools lack large-scale prospective validation and standardized performance benchmarks.
Algorithm generalizability across diverse populations and imaging platforms remains uncertain.
Explainability methods are evolving but not yet universally implemented, limiting clinician confidence.
Regulatory pathways for AI integration are complex and evolving.
Potential biases in training data may affect diagnostic equity.
Integration into clinical workflows requires infrastructure and training investments.
How this may change practice
AI-assisted image interpretation may reduce diagnostic errors and improve detection of subtle findings.
Workflow automation can enhance radiologist efficiency, allowing focus on complex cases.
Explainable AI models may foster clinician trust and facilitate shared decision-making.
Personalized imaging protocols and risk stratification could optimize patient management.
Radiology departments will need to adapt to AI integration through training and infrastructure upgrades.
Multidisciplinary collaboration will be key to developing clinically relevant AI solutions.