Success Story: A Research Fellow from India Secures EB-2 NIW Approval in 3 Months Without RFE
Client’s Testimonial:
“Thank you for your great support in filing the I-140.”
On February 17th, 2026, we received another EB-2 NIW (National Interest Waiver) approval for a Research Fellow in the field of Medical Imaging (Approval Notice).
General Field: Medical Imaging
Position at the Time of Case Filing: Research Fellow
Country of Origin: India
State of Residence at the Time of Filing: Minnesota
Approval Notice Date: February 17th, 2026
Processing Time: 3 months
Case Summary:
It was a privilege for our team to assist a research fellow from India specializing in medical imaging from India with an EB-2 NIW petition. Drawing on our extensive experience and a proven track record of over 32,000 successful cases, this case demonstrates our commitment to I-140 clients through strategic, case-by-case analysis and meticulous petition development. The petition achieved approval in just 3 months without an RFE.
Building the Foundation
The client holds a Ph.D. in biomedical engineering and works as a research fellow, leveraging advanced machine learning and AI techniques applied to medical imaging. The client's research aims to develop frameworks that model and predict radiologists' diagnostic performance on CT scans, ultimately reducing variability in cancer detection and improving patient outcomes.
Our team's initial assessment identified several compelling strengths: a strong first-authored publication portfolio in highly ranked journals, meaningful citation metrics reflecting field influence, active peer review service for prestigious venues, and direct alignment with national priorities in artificial intelligence and cancer care initiatives.
We documented the client's impressive metrics: 118 citations, 5 first-authored peer-reviewed journal articles, 8 peer-reviewed conference articles (4 of them first-authored), and 8 preprints (5 of them first-authored). The research has made significant contributions, including reducing variability in quantitative angiography through singular value decomposition techniques, improving the accuracy and interpretability of AI-based aneurysm occlusion prediction models, and building machine learning frameworks that help predict individual radiologists' performance in detecting liver metastases on CT images. These advances directly address diagnostic inconsistency, a leading source of preventable patient harm, and help streamline clinical workflows in cancer care.
We further strengthened the petition by demonstrating that the client's work has received funding from the National Science Foundation and that independent researchers have actively relied upon these findings in their own studies, underscoring the real-world influence of the client's contributions.
We included an expert recommendation letter to validate the client’s research outcomes. One expert stated:
“Taken together, it is my professional opinion that the continuation of [Client]'s research is in the best interests of the U.S.”
NIW Approval and OutlookThis outcome underscores our continued success in securing approvals for researchers whose work bridges artificial intelligence, diagnostic radiology, and public health. The client's ongoing research into machine learning frameworks for CT-based cancer detection continues to drive progress toward more consistent and accurate diagnoses, directly supporting the United States' long-term goals of innovation, health security, and technological leadership in AI-driven healthcare.