Research
Our members are actively engaged in cutting-edge AI research at leading laboratories across Emory University, contributing to the advancement of artificial intelligence in healthcare.
Advancing Healthcare Through Research
Med A.I. members contribute to research that spans the full spectrum of AI applications in medicine—from developing novel algorithms for disease detection to investigating the ethical implications of AI deployment in clinical settings. Our collaborative research efforts bridge computer science, biomedical informatics, clinical medicine, and health policy.
Get Involved in AI Research
Opportunities for medical students
MedAI is compiling a list of AI-related research projects that medical students can get involved in. If you know of or are working in a lab at Emory that does AI-related research, we would love for you to fill out this form!
Questions? Contact ritika.pandey@emory.edu
Key Research Laboratories
Emory Department of Radiology and Imaging Sciences
Led by Dr. Judy Gichoya, the HITI Lab develops diagnostic and predictive imaging models, validates existing commercial AI solutions, and advances fairness and bias research in medical AI.
Key Research Areas:
Emory Department of Radiation Oncology
Research focusing on machine learning, deep learning applications in medical imaging and medical physics, with emphasis on improving diagnostic accuracy and treatment planning.
Key Research Areas:
Emory University & Georgia Tech
Led by Dr. Anant Madabhushi, this initiative focuses on using AI to cut diagnostic costs, expand access to care, and reduce patient suffering through precision medicine and computational pathology.
Key Research Areas:
Emory Department of Radiology
Multiple laboratories engaging in multidisciplinary, collaborative basic and translational science research in medical imaging and AI applications across various imaging modalities.
Key Research Areas:
Recent Research Highlights
AI recognition of patient race in medical imaging: a modelling study
Gichoya JW, Banerjee I, Bhimireddy AR, Burns JL, Celi LA, Chen LC, et al.
Lancet Digit Health. 2022;4(6):e406-e414.
Groundbreaking HITI Lab research demonstrating that AI models can detect patient race from medical images across multiple modalities, raising critical questions about algorithmic fairness and the need for bias mitigation in medical AI systems.
Innovating Challenges and Experiences in Emory Health AI Bias Datathon: Experience Report
Paddo AR, Purkayastha S, Newsome J, Trivedi H, Gichoya JW
J Imaging Inform Med. 2025;38(6):4293-4302.
In-depth analysis of the Emory Health AI Bias Datathon held in August 2023, providing insights into collaborative approaches for identifying and mitigating algorithmic bias in healthcare AI through interdisciplinary teamwork and innovative problem-solving.
Agentic AI in Radiology: Evolution from Large Language Models to Future Clinical Integration
Khosravi B, Rouzrokh P, Akinci D'Antonoli T, Moassefi M, et al.
Radiology: Artificial Intelligence. 2026;8(2):e250651.
Explores the evolution from large language models to autonomous agentic AI systems in radiology, covering persistent memory, retrieval-augmented generation, multiagent workflows, and a structured roadmap for safe clinical deployment.
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images
Jeong JJ, Vey BL, Bhimireddy A, Kim T, Santos T, Correa R, Dutt R, et al.
Radiol Artif Intell. 2023;5(1):e220047.
Development of a large-scale, racially diverse breast mammography dataset containing 3.4 million images with lesion-level annotations, pathologic outcomes, and demographic information to support equitable AI model development.
Collaborate With Us
Are you a researcher interested in AI applications in healthcare? We welcome collaborations with faculty, graduate students, and industry partners. Contact us to explore research opportunities.
