Developing the Next-Generation Interventional Radiology Clinician-Scientists in India

Sir,

Interventional radiology (IR) is undergoing a rapid transformation from a procedure-based specialty to a knowledge-intensive discipline that integrates imaging science, device engineering, computational analytics, material sciences, biologics, and translational vascular biology. As global IR practice embraces advanced robotics, high-fidelity simulation, and artificial intelligence (AI) enhanced image guidance, there is a pressing need to develop IR clinician-scientists. India, characterized by its high case volumes and diverse disease patterns, is uniquely positioned to emerge as a leader in this transformative journey. Several critical learning domains must be cultivated to achieve this objective.

Simulation-based skill development has become an indispensable component of IR education. High-fidelity in vitro simulation enhances catheter–wire control, minimizes complications, and accelerates learning curves, particularly in neurovascular and complex endovascular procedures.[1] For resource-constrained settings, low-cost benchtop models, pulsatile flow phantoms, and indigenous 3D-printed vascular replicas can make structured simulation accessible without substantial financial burden. Incorporating simulation as a mandatory rotation during advanced IR training and integrating rehearsal of real cases—especially ruptured aneurysms, complex arteriovenous fistulas, and fenestrated anatomies—can significantly enhance technical proficiency. Small institutional “simulation libraries,” where patient-specific models for upcoming cases are routinely printed, can be established even within modest budgets. Such simulation libraries could incorporate “worst-case” scenarios to prepare physicians for unforeseen adverse events.

Advanced imaging physics and console literacy remain underutilized strengths of radiology-trained interventional radiologists. With modern angiographic systems offering customizable pulse-width modulation, dynamic collimation, iterative reconstruction, and cone-beam computed tomography (CT) optimization, structured training in imaging physics should be mandatory rather than aspirational. Collaborative teaching with medical physicists can assist trainees in comprehending system-level parameters, radiation dose negotiation, and acquisition tailoring. In India, where equipment heterogeneity is prevalent, structured modules on cross-platform console standardization would enhance national uniformity in imaging quality. Regular “imaging optimization audits” conducted jointly by radiologists, physicists, and technologists can further reinforce these competencies.

Animal models and translational IR science remain the cornerstone of device development, embolic innovation, and vascular biology research. Regrettably, a limited number of Indian IR training institutions currently possess routine access to preclinical laboratories. A viable model for developing countries is the establishment of “shared preclinical research clusters” at regional veterinary universities or institutional animal houses, enabling multiple hospitals to access swine animal vascular models, elastase aneurysm creation workshops, and basic biomaterial testing facilities. In the short term, protocol-driven training camps can expose trainees to endothelial response mechanisms, polymer kinetics, embolic–tissue interactions, and hemodynamic phenomena. Even in centers lacking animal facilities, structured dry-laboratory research, such as clot analog development, catheter friction testing, and prototype bench evaluation, can meaningfully introduce trainees to mechanistic thinking.

AI is increasingly becoming an integral component of IR workflows, encompassing a range of applications such as AI-assisted perfusion analytics, synthetic digital subtraction angiography (DSA), automated catheter navigation, and real-time noise reduction. To effectively utilize these AI tools, IR physicians must possess a comprehensive understanding of algorithmic design, dataset bias, and performance metrics. In regions with limited access to commercial AI platforms, collaborations with academic engineering departments can facilitate the joint development of open-source radiomics pipelines, segmentation tools, and computational fluid dynamics models. Concurrently, short certificate modules in Python programming, machine learning fundamentals, and radiomics can be seamlessly integrated into IR curricula without significantly extending the training duration.

To integrate these parallel learning pathways into mainstream IR education in India, a practical multitier framework is essential. First, national IR simulation networks can be established under the guidance of professional bodies such as the Indian Society of Vascular and Interventional Radiology (ISVIR). This would facilitate shared access to 3D printers, vascular phantoms, and simulation curricula. Second, annual IR-engineering innovation hackathons can bring together polymer chemists, mechanical engineers, surgeons, and IR trainees to collaboratively create prototypes, clot analogs, and digital tools. Third, mandatory imaging physics, computational IR, and radiation optimization modules can be incorporated into all accredited training programs, accompanied by objective assessments. Fourth, structured clinician-scientist tracks with protected research time and access to wet laboratories, bioengineering facilities, and data science groups can foster genuine translational competencies. Fifth, international short-term exchange programs can provide exposure to advanced simulation centers, device R&D laboratories, and AI-driven angiographic suites. Sixth, institutional innovation and patenting cells can streamline idea incubation, prototype refinement, intellectual property filing, and industry partnerships, thereby making device development an accessible pathway for IR trainees.

India finds itself at a critical juncture. To transcend merely high case volume, the next paradigm shift in Indian IR training necessitates the integration of simulation science, imaging physics expertise, translational vascular biology, biomaterial engineering, data science, and AI literacy into the core curriculum. By embedding these parallel learning pathways, we can cultivate the hybrid IR clinician-scientist who not only executes patient care with precision but also propels innovation, contributes to global scientific leadership, and molds the future of minimally invasive therapy.[2] [3] [4]

Table 1 Essential parallel competencies for developing the hybrid IR clinician-scientist

Domain	Why it matters in modern IR	Key components	Practical integration strategies for India
1. Simulation-based training and benchtop learning	Enhances catheter–wire skills, reduces complications, and accelerates learning in complex endovascular procedures	• High-fidelity simulators • Pulsatile flow phantoms • Low-cost benchtop models • 3D-printed patient-specific replicas • Rehearsal of complex anatomies	• National IR simulation networks under ISVIR • Low-cost indigenous flow models • Mandatory simulation rotation during DM/DrNB IR training • “Simulation libraries” for upcoming cases • Regional skill labs in tier-2/tier-3 cities
2. Advanced imaging physics and console mastery	Optimal image acquisition determines procedural success, radiation dose, and diagnostic accuracy	• Pulse width and frame rate optimization • DSA subtraction principles • Cone-beam CT refinement • AI-driven denoising and motion correction • System cross-platform literacy	• Mandatory imaging physics module in all IR fellowships • Joint teaching with medical physicists • Imaging optimization audits • Cross-vendor training workshops • National online console literacy modules
3. Preclinical models and translational IR science	Foundation for device innovation, embolics, stent technology, and mechanistic disease understanding	• Swine neurovascular models • Rabbit elastase aneurysm models • Murine thrombosis models • Biomaterial and polymer testing • Clot analog development	• Shared regional preclinical clusters at veterinary universities • Short animal-lab training camps • Dry-lab device testing tracks • IR engineering research projects • Fellowships with protected research time
4. AI, radiomics, and computational flow modeling	AI now guides procedural planning, navigation, perfusion analysis, and radiation optimization	• Synthetic DSA • Automated vessel segmentation • Predictive perfusion analytics • Computational flow dynamics–based treatment modeling • Radiomics-derived biomarkers	• Certificate modules in Python/ML for IR trainees • IR engineering hackathons to develop open-source tools • National IR-AI skill curriculum • Partnerships with IITs/engineering colleges
5. Biomaterials, device innovation, and IR–pharma interfacing	New devices require an understanding of polymer chemistry, drug-delivery scaffolds, and mechanical–material interactions	• Liquid embolic kinetics • Steerable microcatheter technology • Nanotechnology-enhanced stents • Drug–device combination science	• Institutional innovation incubators and patent cells • Collaboration with material science departments • Industry-supported device innovation sprints • Short-term R&D rotations in device companies
6. Cross-disciplinary competency development	True hybrid IR clinician-scientists require fluency across engineering, physics, vascular biology, and data science	• Vascular biology • Hemodynamics • Imaging informatics • Materials engineering • Coding and simulation science	• Formal clinician-scientist track within IR training • Protected research time • Structured mentorship from engineering/physics faculty • International short-term exchanges
7. National and institutional reforms	System-level reforms are essential to bring Indian IR education to global standards	• Standardized curricula incorporating research methodology • Mandatory assessment modules • Simulation boards • IR innovation networks	• National IR curriculum overhaul • Accreditation for simulation centers • IR innovation centers • Nationwide faculty development programs

Abbreviations: AI, artificial intelligence; CT, computed tomography; DM/DrNB, Doctorate of Medicine/Doctorate of National Board in Interventional Radiology; DSA, digital subtraction angiography; IIT, Indian Institutes of Technology; IR, interventional radiologist; ISVIR, Indian Society of Vascular and Interventional Radiology; ML, machine learning; R&D, research and development.

Conflict of Interest

None declared.

• References

• 1 Hilleke S, Wiener R, Frisch A. et al. Endovascular simulator training and shadowing in interventional radiology: a comparison of two teaching methods in the curricular training of medical students. Cardiovasc Intervent Radiol 2024; 47: 1540-1546
• 2 Herrmann AM, Meckel S, Gounis MJ. et al. Large animals in neurointerventional research: a systematic review on models, techniques, and their application in endovascular procedures for stroke, aneurysms, and vascular malformations. J Cereb Blood Flow Metab 2019; 39 (03) 375-394
• 3 Lesaunier A, Khlaut J, Dancette C, Tselikas L, Bonnet B, Boeken T. Artificial intelligence in interventional radiology: current concepts and future trends. Diagn Interv Imaging 2025; 106 (01) 5-10
• 4 Passias N, Wormald N. Advancements in neurointervention: current techniques and future directions. NL 2024; 3 (02) 68-71

Publication History

Article published online:
27 January 2026