1. How do you see AI reshaping the core workflows in diagnostic radiology, and what aspects are evolving the fastest?
AI is reshaping diagnostic radiology by creating a collaborative human–AI workflow that streamlines tasks from image acquisition to reporting, with rapid advances in triage, image reconstruction, enhancement, and multimodal report generation.
- Automated Pre‐reading and Triage: Real‐time AI algorithms flag critical findings (e.g., intracranial hemorrhage, pulmonary embolism) to prioritise emergent cases, reducing turnaround times and improving patient outcomes.
- Opportunistic Screening: AI tools identify incidental pathologies (e.g., lung nodules, osteoporosis, hepatic steatosis) on routine scans without requiring additional imaging, enabling early intervention and preventive care.
- Deep Learning–Based Image Reconstruction: Neural network–driven CT and MRI reconstruction reduces radiation dose, improving image quality compared to traditional iterative methods.
- Image Enhancement: AI‐powered denoising, super‐resolution, and artifact suppression models restore fine anatomical details—especially in low‐dose or accelerated acquisitions—bolstering diagnostic confidence in dose‐sensitive populations.
- Advanced Segmentation and Quantitative Analysis: DL-based models automatically delineate organs, tumors, and vessels, providing reproducible volumetrics and feature extraction for prognostication and treatment planning.
- Generative Reporting: Multimodal foundation models draft preliminary reports by integrating imaging findings with clinical context, standardizing language and reducing reporting times.
- Workflow Integration and Decision Support: AI modules embedded in PACS/RIS suggest optimal imaging protocols, annotate studies with measurements (e.g., nodule size, ejection fraction), and recommend follow-up exams.
- Agentic AI for Workflow Optimisation: Multi-agent frameworks autonomously coordinate tasks—data ingestion, preprocessing, segmentation, classification, report drafting, QA—minimizing manual intervention and dynamically adapting workflows to clinical needs.
2. From your research and clinical experience, what are the most promising deep learning applications currently being adopted in neuroradiology?
Brain Tumor Segmentation and Classification
Convolutional neural networks (CNNs) accurately delineate gliomas, meningiomas, and metastases across multiparametric MRI sequences, enabling automated volumetric assessment that feeds radiogenomic pipelines to noninvasively predict molecular subtypes.
Hemorrhage and Stroke Detection
Deep learning models trained on large CT datasets swiftly identify intracranial hemorrhage, early ischemic changes, and large vessel occlusions, triaging acute stroke patients in real time and significantly reducing door-to-needle times.
White Matter Hyperintensity (WMH) Quantification
AI-based pipelines automatically segment and quantify WMHs on FLAIR MRI, providing reproducible burden metrics that facilitate longitudinal tracking in aging, dementia, and small vessel disease studies.
Differentiation of Demyelinating versus Neoplastic Lesions
Radiomic feature extraction combined with deep embeddings enables distinguishing tumefactive multiple sclerosis from high-grade gliomas, reducing diagnostic uncertainty and better guiding biopsy decisions.
Prognostic Outcome Prediction
Integrating AI-derived imaging features with clinical variables, machine learning models predict outcomes in traumatic brain injury, ischemic stroke, and neurodegenerative diseases, allowing personalised risk stratification and management planning.
Image Enhancement and Accelerated MRI
Deep learning–based denoising and super-resolution algorithms enhance signal-to-noise ratio and spatial resolution in accelerated MRI, enabling up to fourfold scan time reduction without compromising diagnostic quality. K-space–to–image reconstruction networks recover high-fidelity images directly from undersampled k-space data, facilitating faster acquisitions and reduced motion artifacts.
ARIA Detection and Quantification
AI tools automatically detect and grade both ARIA-E (edema/sulcal effusion) and ARIA-H (microhemorrhage/superficial siderosis) on brain MRI, significantly improving sensitivity (87% for ARIA-E, 79% for ARIA-H) compared to unassisted radiologist readings, thereby enhancing safety monitoring in Alzheimer’s disease immunotherapy trials.
Automated Volumetric Brain Analysis
Automated volumetric brain analysis solutions segment and quantify multiple brain structures on 3D T1-weighted MRI, comparing volumes against age- and sex-matched normative databases to deliver objective metrics for hippocampal atrophy, cortical thinning, and whole-brain volume changes, aiding early diagnosis and longitudinal monitoring of neurodegenerative disorders.
3. As someone working at the intersection of AI and imaging informatics, how do you define and operationalize the principles of Responsible AI in your projects?
Although there is no universally agreed-upon definition, as I presented recently during the American Roentgen Ray Society 2025 meeting in San Diego, I consider Responsible AI to be a framework grounded in cost-effectiveness, trustworthiness, sustainability, fairness, ethical principles, safety, and human-centered design. In my projects, we operationalize these principles by ensuring data privacy, bias mitigation, employing interpretable models, and implementing robust cybersecurity safeguards. We also incorporate uncertainty quantification to bolster trust and support informed clinical decision-making.
4. Given the increasing reliance on AI systems, how should radiologists prepare for shifts in their interpretive and decision-making roles?
As AI transforms radiology, radiologists must learn to optimize their interaction with these tools to maximize their value. As ultimate overseers of AI, they need strong AI literacy to understand inherent biases and limitations, integrate tools into workflow and PACS, and maintain diagnostic expertise for complex cases beyond AI’s reach. Active involvement in quality assurance and multidisciplinary collaboration will keep radiologists central to patient care and empower them to shape AI development in ways that best serve physicians and patients alike.
5. What are the primary challenges of deploying AI models in real-time hospital environments, particularly in high-throughput imaging centers?
Integration with HIS, PACS, and imaging devices is complex due to interoperability and possible metadata inconsistencies. High-throughput settings demand robust infrastructure to manage latency and maintain uptime. Security of PHI, regulatory compliance, and FDA traceability are essential. Moreover, user resistance to workflow changes and the need for continuous model monitoring and retraining complicate deployment. Successful implementation requires institutional support and iterative adaptation.
6. How do you approach the validation of AI tools to ensure both generalizability across diverse populations and fairness in diagnostic outcomes?
Ensuring generalizability and fairness in AI tools requires a multi-layered validation approach. First, models should be trained and validated on multi-center, multi-vendor datasets to minimize site-specific bias and enhance robustness. External prospective validation helps assess real-world performance. Subgroup analyses by age, sex, race, and disease severity are essential to identify disparities. Fairness metrics and bias audits can quantify inequities in predictions. Finally, reader studies and post-deployment monitoring ensure the AI remains accurate, equitable, and clinically useful over time.
7. In your view, what gaps still exist in current FDA regulatory frameworks for AI-based radiology tools, and how might these affect innovation?
Regulatory frameworks for AI in radiology face several limitations that may hinder innovation. Current FDA pathways lack mechanisms for adaptive learning, standardized post-market surveillance, and clear validation criteria for generative models. Additionally, there are no strict mandates for having uncertainty-aware models, bias assessment across demographics, and global regulatory fragmentation further complicates deployment. These challenges discourage rapid iteration, especially for smaller developers, and may slow access to cutting-edge AI tools that could enhance patient care.
8. How is the Mayo Clinic integrating generative and agent-driven AI to assist with radiological reporting or clinical summaries?
Mayo Clinic is integrating generative and agent-driven AI to streamline radiological reporting and clinical communication. In collaboration with Microsoft Research, a multimodal foundation model was developed to generate structured impressions from imaging and clinical text. For tumor boards, generative AI creates concise summaries by synthesizing imaging, pathology, and lab data.
9. What ethical concerns do you think deserve more attention as AI becomes more autonomous in radiological decision support?
How explainable should AI decisions be to maintain trust? Who holds responsibility for AI-driven errors? Should patients be informed when AI is involved in their care? How do we prevent bias and skill erosion? Additionally, alignment with clinical goals, cybersecurity threats from continuous data use, and privacy risks—especially in cloud-based systems.
10. How can healthcare systems strike a balance between AI-enabled automation and preserving the critical human elements of radiologic interpretation?
Healthcare systems can balance AI automation and human interpretation by positioning AI as a supportive tool, not a substitute. Radiologists should remain central through human-in-the-loop workflows, contextual decision-making, and collaborative review of AI outputs. Automation can reduce repetitive tasks, freeing radiologists for complex cases and patient interaction. Ongoing education, transparent communication, and strong ethical oversight further ensure that AI enhances, rather than erodes, the clinical judgment, empathy, and critical thinking essential to radiologic care.
11. What does effective human–AI collaboration look like in practice, and how can institutions foster a culture that encourages this synergy?
Effective human–AI collaboration involves integrating AI into daily workflows while preserving clinical judgment. Institutions can foster this synergy through intuitive PACS integration, feedback loops where radiologist edits improve future model performance, and regular interdisciplinary “AI rounds” to review outcomes. Designating AI champions, offering protected time for education, and clearly communicating model limitations promote trust and adoption. This collaborative environment empowers radiologists to remain in control while leveraging AI to enhance efficiency, accuracy, and learning.
12. Which emerging trends in imaging informatics do you believe will have the most transformative impact on U.S. hospital radiology departments over the next five years?
In the coming years, imaging informatics will likely advance through generative AI for synthetic image creation, radiogenomics for integrated diagnostics, and federated learning for secure collaboration. Agentic AI systems may streamline workflows, while edge AI and NLP enhance real-time analysis and data extraction. As these tools mature, medicolegal frameworks will evolve to address issues like liability and informed consent. New challenges—especially around autonomous decision-making—will require updated governance to ensure trust, safety, and accountability in clinical practice.
13. Looking ahead, how do you envision AI shaping the educational curriculum for future radiologists, especially in academic and research institutions?
In the next decade, radiology curricula are likely to embed AI fluency as a core competency. Academic programs may introduce foundational training in machine learning, vibe coding, and ethics, alongside hands-on experiences through hackathons and AI-focused rotations. Interdisciplinary journal clubs and responsible AI workshops could foster critical thinking. Board exams and CME modules will likely begin to reflect AI literacy. These shifts aim to prepare radiologists to critically evaluate, integrate, and guide AI tools in clinical and research settings.
