Gerstner Scholars Program in AI Translation
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The Gerstner Scholars Program in AI Translation at Mayo Clinic is accelerating breakthroughs in artificial intelligence (AI)-powered medical translation. Through this program, junior and early-career clinicians and clinical investigators will collaborate with leading experts in AI, data science and informatics to drive breakthrough cures for patients.
AI holds incredible promise for the future of medicine, but it takes more than just algorithms to make that promise a reality.
— Louis V. Gerstner, Jr.
"AI holds incredible promise for the future of medicine, but it takes more than just algorithms to make that promise a reality,” says Louis V. Gerstner, Jr. “It requires a commitment to innovation and to the talented individuals who can turn theory into practice. By creating the Gerstner Scholars Program at Mayo Clinic, we ensure that clinicians with patient-first strategies have what they need to redefine what’s possible in healthcare."
The Gerstner Scholars demonstrate exceptional promise, presenting high-impact projects poised to significantly advance healthcare. Their innovative proposals highlight the transformative potential of AI in medicine.
Learn more about Gerstner Philanthropies.
2026 GERSTNER SCHOLARS
Sushil Kumar Garg, M.B.B.S. | Eau Claire, Wisconsin
Division of Gastroenterology and Hepatology
Dr. Garg is developing Colon‑Pilot, an AI system that guides the entire colonoscopy process — from preparation to follow up. By unifying seven tools into one workflow, it gives patients clearer instructions, safer procedures and more consistent, guideline‑aligned decisions that reduce unnecessary repeat visits and support better long‑term outcomes.
Oluwadamilola T. Oladeru, M.D., M.B.A. | Jacksonville, Florida
Department of Radiation Oncology
Dr. Oladeru is developing OPTIMA, an AI system that automates breast radiation therapy planning so patients can start treatment sooner and with less stress. By predicting the safest, most effective dose for each patient, OPTIMA reduces planning delays and delivers more consistent, high‑quality care.
Yemi Omotoso, M.B.B.S., M.D. | Jacksonville, Florida
Department of Emergency Medicine
Dr. Omotoso is developing TRIAGE‑AI, a tool that brings a patient’s full medical context into the first minutes of an emergency department visit. It helps clinicians spot high‑risk patients sooner, reduce delays and ensure every patient receives timely, equitable triage.
David M. Routman, M.D. | Rochester, Minnesota
Department of Radiation Oncology
Dr. Routman is developing VOICE‑AE, an AI system that captures treatment‑related side effects directly from conversations during oncology visits. It reduces documentation burden, ensures symptoms aren’t missed and gives patients more focused time with their care team.
Elizabeth H. Stephens, M.D., Ph.D. | Rochester, Minnesota
Department of Surgery
Dr. Stephens is developing a virtual pediatric cardiac surgery agent that provides families clear, consistent guidance before and after their child’s heart surgery. The tool offers 24/7 support to ease anxiety, reinforce key instructions and help caregivers recognize concerns early.
Naoki Takahashi, M.D. | Rochester, Minnesota
Department of Radiology
Dr. Takahashi is advancing an AI model that helps radiologists interpret prostate MRI exams by estimating a patient’s risk of clinically significant cancer and highlighting areas of concern. This support leads to clearer, more consistent decisions across readers and sites, giving patients timely answers and reducing unnecessary biopsies. The goal is to provide men with more confident, well‑supported guidance during a critical step in their evaluation.
Cody C. Wyles, M.D. | Rochester, Minnesota
Department of Orthopedic Surgery
Dr. Wyles is developing OrthoLLM, an AI system that reads orthopedic operative notes and automatically extracts data for joint replacement registries. Automating this work makes registry information more complete and timely, helping identify implant issues sooner and improve long‑term outcomes for patients.
Tony C. Yen, M.D. | Phoenix, Arizona
Department of Anesthesiology and Perioperative Medicine
Dr. Yen is developing a machine learning tool that analyzes perioperative data in real time to predict which patients are likely to develop low blood pressure after surgery — a complication that can slow recovery and trigger urgent interventions. By flagging risk while the patient is still in the operating room, the tool helps care teams adjust management early and prepare the right level of support.
2025 GERSTNER SCHOLARS
Alina M. Allen, M.D., M.S. | Rochester, Minnesota
Division of Gastroenterology and Hepatology
Dr. Allen is advancing an automated, AI‑driven pathway that flags patients at risk for liver fibrosis during routine care and streamlines diagnostic testing and referral. Early results show the model helps clinicians identify high‑risk patients sooner and improves the appropriateness of testing and specialty evaluation. She notes that this work “finds patients earlier — often before they have symptoms,” and she is now preparing to scale the pathway more broadly.
Carrie M. Carr, M.D. | Rochester, Minnesota
Department of Radiology
Dr. Carr is developing GRACIE, an AI system that automatically synthesizes clinical notes, imaging history, pathology data and treatments into a clear timeline for radiologists. Her team’s early work shows the tool “synthesizes essential clinical information into a concise, accurate timeline,” improving interpretation in complex cases such as brain tumors. She is now expanding GRACIE across radiology subspecialties and building the infrastructure needed for clinical deployment.
Chia-Chun Chiang, M.D. | Rochester, Minnesota
Department of Neurology
Dr. Chiang is developing AI‑based models that help clinicians select the most effective migraine preventive therapy sooner, reducing the long trial‑and‑error process many patients face. Her team’s early work shows that “real‑world electronic health record data can predict which preventive medications are most likely to help a given patient,” and they have expanded model inputs to include comorbidities and key lab markers. She is now advancing toward prospective validation to give patients a faster, clearer path to relief.
Learn more about how Dr. Chiang is using AI to pinpoint migraine treatments.
Lauren A. Dalvin, M.D. | Rochester, Minnesota
Department of Ophthalmology
Dr. Dalvin is developing AI models that detect choroidal melanocytic lesions earlier and more reliably using standard fundus photographs from routine eye exams. Her team’s first‑year work produced a “high‑performing AI model capable of detecting lesions across all three fundus camera types,” demonstrating strong sensitivity and specificity for early cancer detection. She is now building models that distinguish high‑risk, referrable lesions from low‑risk ones to ensure no ocular tumor goes unnoticed or untreated.
Christopher A. Dinh, M.D. | Rochester, Minnesota
Division of Hospital Internal Medicine
Dr. Dinh is developing AI‑enabled tools that help hospitals anticipate discharge needs earlier and coordinate care more efficiently. His team’s first‑year work produced a “systemwide model to predict skilled nursing facility placement needs” using information available within the first 24 hours of hospitalization, and they have begun building a second model to identify medically stable patients delayed by complex discharge barriers. He is now advancing these tools toward broader deployment to reduce unnecessary hospital days and ease stress for patients and families.
Antonio J. Forte, M.D., Ph.D. | Jacksonville, Florida
Department of Surgery
Dr. Forte is developing AIVA, an AI‑powered virtual assistant that provides clear, surgeon‑verified guidance throughout surgical recovery. In its first year, the team advanced AIVA from concept to a fully functional system, demonstrating 98.4% accuracy and 100% detection and escalation of emergency or out‑of‑scope scenarios, along with high patient trust in early usability testing. He is now preparing AIVA for real‑world deployment through a clinical pilot, aiming to offer safer, clearer and more reassuring postoperative support at home.
William D. Freeman, M.D. | Jacksonville, Florida
Department of Neurosurgery
Dr. Freeman is developing SAHVAI, an AI system that measures subarachnoid hemorrhage volume in seconds and provides clinicians with clear, objective data during one of neurology’s most time‑critical emergencies. His team’s landmark study showed that SAHVAI quantifies hemorrhage volume “in 6.7 seconds with 99.8% accuracy” and established the first quantitative prognostic threshold for SAH at 10 mL. He is now advancing SAHVAI toward clinical deployment to improve early detection and guide treatment when every minute matters.
Learn more about how Dr. Freeman is transforming stroke care and outcomes using AI.
Scott A. Helgeson, M.D. | Jacksonville, Florida
Division of Allergy and Pulmonary Medicine
Dr. Helgeson is developing a cuffless, light‑based method for estimating blood pressure using photoplethysmography (PPG), aiming to make monitoring more comfortable, continuous and accessible. In his first year, he and his collaborators collected data from more than 150 patients and “completed the first round of model development, confirming that PPG‑based blood pressure estimation is feasible.” He is now focused on improving accuracy, ensuring equitable performance across skin tones and building clinic‑ready workflows for real‑world use.
Abhinav Khanna, M.D. | Rochester, Minnesota
Department of Urology
Dr. Khanna is developing AI‑driven tools that analyze CT scans over time to predict how an individual patient’s kidney tumor is likely to grow, reducing uncertainty around decisions between surgery and active surveillance. In his first year, he and his team curated a two‑decade longitudinal imaging dataset — “more than 10,000 abdominal CT studies” — and deployed the ARCTIC autosegmentation model across it, creating one of the most comprehensive renal tumor imaging repositories ever assembled. He is now advancing toward predictive modeling to estimate personalized tumor growth rates from the initial scan.
Irbaz B. Riaz, M.B.B.S., Ph.D. | Phoenix, Arizona
Division of Hematology and Oncology
Dr. Riaz is developing PRIORITY, an AI‑powered platform that proactively identifies patients eligible for clinical trials and newly approved therapies. In its first year, the team built natural language processing pipelines that “extract diagnosis, treatment history and genomic markers from the electronic health record” and deployed a working chatbot prototype within a secure, HIPAA‑compliant AI environment. He is now advancing PRIORITY toward clinical readiness to make trial access more timely, equitable and patient‑centered.
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