Dr. Bradley J. Erickson

MD, PhD, FSIIM

Radiologist and researcher working in medical imaging, artificial intelligence, and clinical workflow automation.

30+
Years AI & Imaging Research
200+
Publications
80+
Trainees

Medical AI and Imaging Informatics

Research and implementation work in radiology artificial intelligence, workflow automation, and the evaluation of clinical AI systems.

Bradley J. Erickson, MD, PhD, is a Professor of Radiology at Mayo Clinic, Director of the Mayo Clinic AI Lab, and CEO of FlowSigma. He holds MD and PhD degrees from Mayo Medical and Graduate School and has conducted research in medical imaging informatics and artificial intelligence for over three decades, during which he has mentored more than 80 trainees.

His work spans clinical practice, research, and implementation, including direction of Mayo Clinic's transition to filmless and paperless operations and the development of deep learning methods for diagnostic imaging.

Current Positions

  • Professor of Radiology Mayo Clinic College of Medicine
  • Director, Mayo Clinic AI Lab Mentored 80+ trainees in medical AI research
  • CEO, FlowSigma Clinical workflow automation and AI integration

Education

  • MD & PhD Mayo Medical and Graduate School
  • Residency Radiology, Mayo Clinic
  • Fellowship Neuroradiology, Mayo Clinic
  • Board Certifications American Board of Radiology, American Board of Imaging Informatics

Leadership Roles

  • Past President Society for Imaging Informatics in Medicine (SIIM)
  • Chair SIIM Research Committee
  • Founding Chair Division of Imaging Informatics, Mayo Clinic
  • Past Vice Chair for Research Dept Radiology, Mayo Clinic
  • Editorial Board Member Multiple radiology and informatics journals

Research Focus

  • Deep learning in medical imaging
  • Workflow automation & optimization
  • AI bias mitigation in healthcare
  • FDA regulatory processes for AI/ML
  • Brain cancer, MS, and kidney disease imaging

Awards & Recognition

Awards received for contributions to medical imaging informatics and artificial intelligence.

2019
NVIDIA Global Impact Award
NVIDIA Corporation
2013
Sam Dwyer Lecture in Informatics
Society for Imaging Informatics in Medicine (SIIM)
2009
Carmen Award for Research Excellence
Mayo Clinic Department of Radiology
Multiple Years
NIH Research Grants (PI)
Brain Cancer, Multiple Sclerosis, Polycystic Kidney Disease

Featured Publications

Author of more than 200 peer-reviewed publications on medical imaging AI, bias mitigation, and clinical informatics. Selected publications are listed below.

Key Publications

Agentic AI and Large Language Models in Radiology: Opportunities and Hallucination Challenges
Salehi S, Singh Y, Horst KK, Hathaway QA, Erickson BJ. Bioengineering (Basel). 2025 Nov 26;12(12):1303. doi:10.3390/bioengineering12121303
Quantifying Uncertainty in Deep Learning of Radiologic Images
Faghani S, Moassefi M, Rouzrokh P, Khosravi B, Baffour FI, Ringler MD, Erickson BJ. Radiology. 2023 Aug;308(2):e222217. doi:10.1148/radiol.222217
Mitigating Bias in Radiology Machine Learning: 1. Data Handling
Rouzrokh P, Khosravi B, Faghani S, Moassefi M, Vera Garcia DV, Singh Y, Zhang K, Conte GM, Erickson BJ. Radiol Artif Intell. 2022 Aug 24;4(5):e210290. doi:10.1148/ryai.210290
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks
Zhang K, Hu H, Philbrick K, Conte GM, Sobek JD, Rouzrokh P, Erickson BJ. Tomography. 2022 Mar 24;8(2):905-919. doi:10.3390/tomography8020073
Magician's Corner: How to Start Learning about Deep Learning
Erickson BJ. Radiol Artif Intell. 2019 Jul 31;1(4):e190072. doi:10.1148/ryai.2019190072
Machine Learning: Discovering the Future of Medical Imaging
Erickson BJ. J Digit Imaging. 2017 Aug;30(4):391. doi:10.1007/s10278-017-9994-1
DEWEY: the DICOM-enabled workflow engine system
Erickson BJ, Langer SG, Blezek DJ, Ryan WJ, French TL. J Digit Imaging. 2014 Jun;27(3):309-13. doi:10.1007/s10278-013-9661-0

Mission & Vision

The objective of this work is to reduce the distance between AI research and clinical implementation. A substantial proportion of promising AI tools fail at deployment, and in most cases the limiting factor is not algorithm performance but the absence of adequate attention to clinical workflow, calibrated clinician trust, and patient safety.

MDSynapse.org is intended to support clinicians in evaluating these systems critically: identifying what a reported result does and does not establish, requiring transparency regarding training data and stratified performance, and distinguishing tools that improve patient outcomes from those that improve benchmark metrics.

At FlowSigma, the corresponding work is infrastructural: embedded workflows, automated quality control, and systems designed around observed clinical practice rather than an idealized process model. The objective is not to substitute for clinical judgment but to provide reliable tools that reduce the administrative and verification burden placed on it.

LLMs are used in creating content. My process is that I identify interesting topics or papers, I create an outline, I use 2 LLMs to draft content and a 3rd LLM to combine the drafts, and then I hand edit to produce the final product.

Connect & Collaborate

Inquiries regarding medical AI research, workflow automation, and collaboration are welcome.