Abstract
This commentary introduces agentic artificial intelligence (AI) as an emerging paradigm in radiology, marking a shift from passive, user-triggered tools to systems capable of autonomous workflow management, task planning, and clinical decision support. Agentic AI models may dynamically prioritize imaging studies, tailor recommendations based on patient history and scan context, and automate administrative follow-up tasks, offering potential gains in efficiency, triage accuracy, and cognitive support. While not yet widely implemented, early pilot studies and proof-of-concept applications highlight promising utility across high-volume and high-acuity settings. Key barriers, including limited clinical validation, evolving regulatory frameworks, and integration challenges, must be addressed to ensure safe, scalable deployment. Agentic AI represents a forward-looking evolution in radiology that warrants careful development and clinician-guided implementation.
Keywords: artificial intelligence, agentic AI, radiology workflows, clinical decision support, autonomous systems, human-AI collaboration, regulatory affairs
Artificial intelligence (AI) has gradually been incorporated into radiology workflows over the past decade, primarily through systems designed to passively assist radiologists in identifying abnormalities or streamlining report generation.1,2 These early tools, while valuable, operate largely as reactive agents: they perform a predefined task, often only when prompted by a user. Despite their usefulness, such models have clear limitations in dynamic clinical environments where imaging volumes are high, acuity varies, and workflows must adapt rapidly.
Agentic AI, defined as systems capable of initiating actions independently based on contextual understanding, introduces a new model of interaction (Figure 1). Rather than awaiting specific prompts, agentic systems may independently assess imaging queues, prioritize studies based on clinical urgency, suggest additional sequences or protocols, tailor decision support based on clinical context, and dynamically adapt their outputs based on a patient’s history, prior imaging, and emerging findings. In other words, agentic AI represents a move from passive assistance to active clinical partnership.
Figure 1.
Conceptual model of agentic AI integration in radiology.
At present, agentic AI is not yet broadly used in daily clinical radiology practice.3 Most implementations are confined to research settings, pilot programs, or limited real-world deployments.3–7 However, the enthusiasm for agentic models is high given the pressing need to optimize workflows, enhance clinical decision-making, reduce radiologist cognitive load, and improve time-to-diagnosis for critical findings. Table 1 summarizes key distinctions between traditional and agentic AI systems.
Table 1.
Comparison of traditional and agentic AI in radiology.
| Feature | Traditional AI | Agentic AI |
|---|---|---|
| Core applications | Rule-based decision-making, pattern recognition upon request | Proactive workflow management, decision support, and task initiation |
| Example tasks | Anomaly detection, automated segmentation of lesions, structured report auto-filling | Case triage, clinical protocol adjustments, dynamic prioritization |
| Interaction model | Reactive (often requires user prompt) | Autonomous (or semi-autonomous; initiates tasks based on context) |
| Clinical context awareness | Limited or static | Dynamic and patient-specific |
| Impact on workflow | Streamlines select tasks | Continuously optimizes workflow and resource allocation |
| Degree of autonomy | Low (task-specific assistance) | Moderate (suggests clinical actions with oversight) |
| Adaptability | Static functionality | Learns/adapts (potentially with safeguards) |
| Regulatory status | Widely approved | Largely investigational |
| Integration requirements | Moderate (plug-in to EHR, PACS, RIS) | High (seamless interoperability with EHR, PACS, RIS) |
| Oversight needs | Radiologist reviews outputs | Radiologist supervises and can override AI-initiated actions |
Abbreviations: EHR = Electronic Health Record; RIS = Radiology Information System; PACS = Picture Archiving and Communication System.
Optimizing workflows and enhancing decisions
One of the primary advantages of agentic AI lies in its ability to dynamically prioritize workflows without constant radiologist intervention. In pilot simulations, agentic AI models have reprioritized imaging studies based on automatically detected critical findings, such as pulmonary embolism or intracranial hemorrhage, escalating these cases for expedited review ahead of non-urgent studies.5,8–10 By triaging cases in real time, agentic AI could substantially reduce delays in diagnosis and intervention, particularly in emergency and inpatient settings where case volumes are large and acuity can shift rapidly.
Another important advantage is the ability of agentic AI to provide context-sensitive decision support. In contrast to static rule-based systems, agentic models may integrate clinical history, prior imaging findings, and real-time scan observations to dynamically tailor recommendations. For example, in the case of a patient presenting with right upper quadrant pain, an agentic AI system could recognize a history of cholecystectomy on prior imaging and automatically adjust differential diagnoses accordingly, prioritizing considerations such as pancreatitis or nephrolithiasis rather than ruling out acute cholecystitis.
Agentic AI also shows strong promise in task planning and automation. By proposing additional imaging sequences when needed, suggesting the appropriate structured report templates based on preliminary findings, or initiating follow-up tracking according to established guidelines, agentic systems may reduce administrative workload.4,10 Radiologists would retain supervisory control, but the cognitive and logistical burden associated with managing incidental findings, checklists, and follow-up intervals could be reduced.
Additionally, agentic AI provides valuable cognitive support. By automating low-complexity but high-importance tasks, these systems may allow radiologists to devote more attention to the nuanced interpretation of imaging findings. By reducing interruptions and administrative distractions, agentic AI may also decrease error rates associated with missed findings or incomplete documentation, particularly under conditions of fatigue or high clinical load.11,12
Although not yet widely available, the cloud-based design of many agentic AI platforms suggests good scalability. In principle, systems could be progressively deployed across academic centres, community hospitals, and outpatient imaging facilities. Institutions could selectively adopt agentic modules that align with their workflow and clinical decision support needs, and gradually expand functionality as trust and infrastructure support grow.
Navigating hurdles to clinical integration
Despite its promise, agentic AI remains in the early stages of development and validation. Most published studies of agentic AI performance are limited to retrospective datasets or controlled research environments.3,4,7,10 In addition, obtaining large, diverse datasets for model development and evaluation remains challenging. Until prospective, multi-institutional trials demonstrate real-world benefit and safety, agentic AI must be regarded as largely investigational.
Regulatory and institutional oversight frameworks will also need to evolve to accommodate agentic behavior. Current AI regulations are designed for tools that assist radiologists within narrow, predefined scopes.13 Agentic systems, which may autonomously suggest clinical actions, reprioritize studies, or recommend workflow changes, introduce new complexities regarding liability and responsibility. Radiologists must maintain ultimate oversight and decision-making authority, but institutions will need clear policies defining the acceptable boundaries of AI-initiated actions.
Integration with existing Radiology Information System, Picture Archiving and Communication System, and Electronic Health Record platforms presents another challenge. Agentic AI will require seamless, secure interoperability with these platforms to function effectively without disrupting clinical care. Institutions must ensure that any new integration maintains cybersecurity, complies with data privacy regulations, and does not add unnecessary steps to clinical processes.13
Oversight will remain essential even as agentic AI matures. Radiologists must have the ability to review, edit, and override AI-generated triage decisions, clinical recommendations, or workflow proposals easily and transparently. Systems must be designed to support human-AI collaboration, not replace expert clinical judgment. Building trust in agentic outputs will take time, and early deployments must prioritize safety, explainability, and clinician control.
Finally, cost and resource investment will be significant barriers to early adoption.14,15 In addition to software licensing, institutions will need to invest in IT infrastructure, user training, ongoing monitoring, and maintenance. Smaller practices in particular may find it challenging to justify these costs without clear reimbursement pathways or demonstration of tangible clinical and operational benefits.
The path forward
Agentic AI represents a promising evolution in radiology, shifting the role of AI from passive assistance to active workflow optimization, clinical decision support, and task management. While agentic AI is not yet widely used in clinical radiology, early experience suggests strong potential to improve triage of urgent cases, enhance clinical decision-making, optimize reporting workflows, and reduce radiologist cognitive load.5,8–11
Nonetheless, significant barriers must be addressed before agentic AI can be broadly deployed. These include achieving regulatory approval, validating effectiveness through prospective studies, ensuring seamless and secure integration into clinical systems, and establishing robust frameworks for human oversight and responsibility.
Looking ahead, institutions may find strategic value in engaging with this emerging technology early, whether through pilot programs, collaborative development, or contribution to validation efforts. Such proactive involvement will not only shape the evolution of agentic AI but also position radiology departments to capitalize on its benefits as clinical adoption expands.
Acknowledgements
None declared.
Funding
None declared.
Conflicts of interest
The author declares no financial or non-financial competing interests.
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