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 inaugural cohort of 10 Gerstner Scholars has already demonstrated 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.
FIRST COHORT OF GERSTNER SCHOLARS
Alina M. Allen, M.D. | Rochester, Minnesota
Division of Gastroenterology and Hepatology
Project: Intelligent and Automated Clinical Care Pathway for Diagnosis and Referral of Patients with Liver Disease and Fibrosis
Dr. Allen will develop and implement an automated clinical care pathway that integrates machine learning models to diagnose metabolic dysfunction-associated steatotic liver disease (MASLD), also known as fatty liver disease, which affects 1 in 3 adults and is one of the most common causes of liver transplantation. This fully automated care pathway will identify patients at risk of MASLD, stage the severity of liver fibrosis/scarring, and trigger appropriate diagnostic imaging and hepatology referrals.
Carrie M. Carr, M.D. | Rochester, Minnesota
Department of Radiology
Project: GRACIE: Glioma Review Using AI, Clinical Notes, Imaging and Other Electronic Documents
Dr. Carr will use a large language model (LLM) to succinctly abstract data from the electronic health record to present a radiologist with a clear timeline that contains prior radiologic results, radiochemotherapy regimens, surgical procedures and pathology data for patients with primary brain tumors. By having a clear timeline leading up to the current study, radiologists will be better equipped to render higher-quality, more timely interpretation of a patient’s MRI that can be used in clinical care.
Chia-Chun Chiang, M.D. | Rochester, Minnesota
Department of Neurology
Project: Precision Migraine Treatment: An AI-Powered Approach for Migraine Prevention
Dr. Chiang will construct and implement machine learning models that can accurately predict treatment response to commonly used migraine preventive medications based on clinical phenotypes and electronic health record data elements. Based on promising pilot studies, she will expand variables, validate models and translate models into the clinical practice.
Lauren A. Dalvin, M.D. | Rochester, Minnesota
Department of Ophthalmology
Project: AI-Assisted Screening for Choroidal Melanoma
Choroidal melanoma is the most common adult intraocular cancer, which is fatal in up to 50% of patients. Dr. Dalvin will leverage Mayo Clinic’s robust database within the ocular oncology service to develop machine learning models that detect and triage high- and low-risk choroidal lesions to improve patient survival rates by early cancer detection.
Christopher A. Dinh, M.D. | Rochester, Minnesota
Division of Hospital Internal Medicine
Project: Implementing Predictive AI Models to Improve Hospital Patient Flow
Dr. Dinh will implement three predictive AI models into the clinical practice to improve hospital patient flow and discharge efficiency. Early identification of hospitalized patients with complex discharge needs can help focus case management resources earlier on in the hospital stay and thereby reduce hospital length of stay. An efficient hospital discharge will also improve the experience for patients and their families.
Antonio J. Forte, M.D., Ph.D. | Jacksonville, Florida
Department of Surgery
Project: Development of a RAG-LLM in Virtual Assistant for Post-Operative Surgical Care
Dr. Forte will enhance an AI virtual assistant with a retrieval augmented generation (RAG)-based LLM to provide more accurate, personalized and adaptable postoperative care support to patients and improve patient outcomes and safety.
William D. Freeman, M.D. | Jacksonville, Florida
Department of Neurosurgery
Project: SAHVAI: Subarachnoid Hemorrhage Volumetric Artificial Intelligence
SAHVAI is an AI tool for measuring the amount of blood leaked into the spaces and folds of the brain in patients with life-threatening aneurysmal bleeding, reducing time to intervention and improving risk stratification for serious events. “Time is brain,” and SAHVAI has demonstrated significant promise in initial studies for acute stroke care. Dr. Freeman will advance this tool and enhance SAHVAI readiness for wider implementation.
Scott A. Helgeson, M.D. | Jacksonville, Florida
Division of Allergy and Pulmonary Medicine
Project: AI Based Non-Invasive Detection of Blood Pressure Using Photoplethysmogram (PPG)
Dr. Helgeson will develop a cuffless, non-invasive AI model to accurately predict blood pressure across all ranges — from hypotension to hypertension — using PPG signals, trained with arterial line blood pressure measurements for enhanced precision and comfort in patient care.
Abhinav Khanna, M.D. | Rochester, Minnesota
Department of Urology
Project: Leveraging Artificial Intelligence to Personalize Early Detection and Monitoring of Kidney Cancer
A large percentage of patients with kidney cancer present with advanced stages of disease, including a subset of patients with incurable metastatic kidney cancer. Early detection is paramount to facilitate early intervention and to alter disease trajectory. Dr. Khanna will apply a novel AI algorithm to facilitate early detection of kidney tumors and personalize tumor surveillance strategies based on deep learning analysis of tumor radiomic features.
Irbaz B. Riaz, M.B.B.S., Ph.D. | Phoenix, Arizona
Division of Hematology and Oncology
Project: Precise and Intelligent Outreach for Therapeutics and Clinical Trial Yield (PRIORITY)
Using AI, Dr. Riaz will implement an LLM-enabled chatbot to proactively inform patients of relevant new Food and Drug Administration-approved clinical trials and treatments, improving access and engagement beyond clinical visits and increasing accrual to clinical trials through targeted outreach.
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