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Emory University School of Medicine
Emory University School of Medicine
Department of Radiology and Imaging Sciences

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Department of Radiology and Imaging Sciences

Imaging Informatics

Leveraging technology to deliver safe and efficient care for patients, to maximize learning productivity, and to accelerate discovery in the imaging sciences.

Message from the Division Director

Welcome to the Division of Imaging Informatics at the Emory Department of Radiology and Imaging Sciences. Our mission is to bridge the gap between cutting-edge technology and clinical excellence. As the landscape of medical imaging evolves, we are focused on more than just maintaining systems; we are pioneering flexible and intuitive workflows for radiologists. 

Our division is currently leading national efforts in AI governance and trustworthiness, ensuring that the next generation of artificial intelligence tools are safe, effective, and seamlessly integrated into patient care. Whether through our NIH-funded research in real-world AI surveillance or our premiere fellowship and residency tracks, we are committed to training leaders who will define the future of digital health. 

Elias Kikano, MD 



About Us

group of people standing on a rooftop patio smiling

The Imaging Informatics Division serves as the technological engine of the Emory Department of Radiology and Imaging Sciences. We are a diverse collective of physicians, data scientists, IT professionals, and clinical administrators dedicated to optimizing the digital ecosystem of one of the nation’s largest academic medical centers. Our multidisciplinary team manages the complex intersection of PACS, RIS, and enterprise AI orchestration, ensuring that every image captured is an asset for both immediate diagnosis and long-term discovery. By fostering a culture of technical agility, we empower our clinicians to focus on what matters most: delivering precise, patient-centered care.




Mission

Our mission is to lead the transformation of radiology through the ethical application of technology. We strive to: 

  • Innovate: Develop and deploy advanced informatics solutions that redefine the radiologist’s experience. 
  • Educate: Cultivate the next generation of informatics experts through immersive, hands-on clinical and technical training. 
  • Validate: Establish rigorous standards for AI performance and surveillance to ensure the highest levels of patient safety and health equity. 


Team Members

Elias Kikano, MD Division Director and Medical Director of Health Informatics

smiling radiologist

Nabile Safdar, MD, MPH, FSIIM Vice Chair of Informatics for Radiology, Chief AI Officer for Emory Healthcare

smiling radiologist

Judy Gichoya, MD, MS Director, Healthcare AI, Innovation and Translation Informatics (HITI) Lab

smiling radiologist

Peter Harri, MD Director of Strategic Informatics Programs and Partnreships

smiling radiologist

Sidd Kosaraju, MD Associate Medical Director of Imaging Informatics, Grady Health System

smiling radiologist

Elizabeth Krupinski, PhD, FSIIM, FAIMBE Vice Chair for Research

smiling scientist

Neil Lall, MD Clinical Lead for Imaging Informatics, Children's Healthcare of Atlanta

smiling radiologist

Dr. Colin Segovis Corporate Medical Director, Middle Revenue Cycle

smiling radiologist

Hari Trivedi, MD Co-Director, HITI Lab

smiling radiologist

Marly van Assen, PhD Co-Director, AIHeart Lab

smiling scientist

Education

residents looking at brain scan images as person explains what they are seeing

We are at the forefront of training the next generation of imaging informatics leaders.  

Imaging Informatics Fellowship

The division offers a one-year imaging informatics fellowship for physicians to further develop their skills in imaging informatics. This fellowship, with both clinical and non-clinical components, provides requisite skills and knowledge for the trainee to lead in operations, innovation, quality, and research related to imaging informatics. Fellows participate in an ongoing curriculum covering technical, research, leadership, and management topics. Each fellow engages in year-long academic projects, chooses areas of interest for elective study, and is embedded in operational technology-related projects. Acceptance will be contingent on acceptance to a clinical subspecialty area. Applicants must be ABR Board-certified or eligible, complete a residency program in the US or Canada, and be eligible to obtain an unrestricted Georgia medical license.

I3T - Integrated Imaging Informatics Track

I3T residents learn skills needed to improve the quality and efficiency of imaging services while supporting clinical, educational, and research efforts through the use of information technology. The I3T trainees serve as the liaisons between our Diagnostic Radiology (DR) and/or Interventional Radiology (IR) Residency programs and the Division of Imaging Informatics. The priority for informatics trainee projects is to improve residency training using innovative information technology solutions while acquiring hands-on experience in the field of imaging informatics.




Select Publications

These are just some of our recent, high-impact publications. To see the publications of our individual faculty members, click on their names above.

  1. Viswanathan VS, Harri P, Volin J, Kadakia J, Safdar N, Kikano E. Best Practices in Cybersecurity Governance: Safeguarding Radiology. J Am Coll Radiol. 2025 Oct;22(10):1132-1140. doi: 10.1016/j.jacr.2025.06.001. Epub 2025 Jun 7. PMID: 40490125. 
  2. Volin J, Viswanathan V, Krupinski E, Safdar N, Silverman R, Narayan V, Kikano E. Feasibility of Using the Apple Vision Pro for Diagnostic Radiology: User Experience and Perceived Clinical Utility. J Imaging Inform Med. 2025 Oct 28. Accepted ahead of print.
  3. Kikano EG. Enhancing Healthcare Cybersecurity: Practical Insights and Emerging Trends. AJR Am J Roentgenol. 2025 Aug 6. doi: 10.2214/AJR.25.33635. Epub ahead of print. PMID: 40767634. 
  4. Spaeth-Cook D, McBee MP, Lin MC MD, Chang PD, Kikano EG. JACR Expert Panel: Artificial Intelligence in Radiology Residency Training. J Am Coll Radiol. 2025 Aug 11:S1546-1440(25)00452-1. doi: 10.1016/j.jacr.2025.08.003. Epub ahead of print. PMID: 40803523. 
  5. Trivedi H, Khosravi B, Gichoya J, Benson L, Dyckman D, Galt J, Howard BM, Kikano EG, Kunjummen J, Lall N, Li XT, Patel S, Safdar N, Salastekar N, Segovis C, van Assen M, Harri P. AI in Action: A Road Map From the Radiology AI Council for Effective Model Evaluation and Deployment. J Am Coll Radiol. 2025 Sep;22(9):1041-1049. doi: 10.1016/j.jacr.2025.05.016. Epub 2025 May 23. PMID: 40414408.
  6. Kikano EG, Zygmont ME. Beyond the AJR: Medical Imaging Clinical Decision Support Faces Continued Challenges. AJR Am J Roentgenol. 2025 May 7. doi: 10.2214/AJR.25.33145. Epub ahead of print. PMID: 40334092.
  7. Volin J, Viswanathan V, Harri P, Segovis C, Safdar N, Kikano E. Utilization of an Electronic Health Record Embedded Enterprise Health Data Exchange: A Single Institute Experience. J Imaging Inform Med. 2025 Mar 3. doi: 10.1007/s10278-025-01459-w. Epub ahead of print. PMID: 40032760. 
  8. Gallifant J, Afshar M, Ameen S, Aphinyanaphongs Y, Chen S, Cacciamani G, Demner-Fushman D, Dligach D, Daneshjou R, Fernandes C, Hansen LH, Landman A, Lehmann L, McCoy LG, Miller T, Moreno A, Munch N, Restrepo D, Savova G, Umeton R, Gichoya JW, Collins GS, Moons KGM, Celi LA, Bitterman DS. The TRIPOD-LLM reporting guideline for studies using large language models. Nat Med. 2025 Jan;31(1):60-69. doi: 10.1038/s41591-024-03425-5. Epub 2025 Jan 8. PMID: 39779929; PMCID: PMC12104976. 
  9. Banerjee I, Bhattacharjee K, Burns JL, Trivedi H, Purkayastha S, Seyyed-Kalantari L, Patel BN, Shiradkar R, Gichoya J. "Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation. J Am Coll Radiol. 2023 Sep;20(9):842-851. doi: 10.1016/j.jacr.2023.06.025. Epub 2023 Jul 27. PMID: 37506964; PMCID: PMC11192466. 
  10. Yala A, Mikhael PG, Strand F, Lin G, Satuluru S, Kim T, Banerjee I, Gichoya J, Trivedi H, Lehman CD, Hughes K, Sheedy DJ, Matthis LM, Karunakaran B, Hegarty KE, Sabino S, Silva TB, Evangelista MC, Caron RF, Souza B, Mauad EC, Patalon T, Handelman-Gotlib S, Guindy M, Barzilay R. Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model. J Clin Oncol. 2022 Jun 1;40(16):1732-1740. doi: 10.1200/JCO.21.01337. Epub 2021 Nov 12. PMID: 34767469; PMCID: PMC9148689.