Dr. Bradley J. Erickson

MD, PhD, FSIIM

Radiologist, AI Researcher, and Healthcare Innovator at the intersection of medical imaging, artificial intelligence, and clinical workflow automation.

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

Pioneering Medical AI & Imaging Informatics

Leading the transformation of radiology through artificial intelligence, workflow automation, and evidence-based innovation.

Dr. Bradley Erickson is a Professor of Radiology at Mayo Clinic, Director of the Mayo Clinic AI Lab, and CEO of FlowSigma. With dual MD and PhD degrees from Mayo Medical and Graduate School, he has spent over three decades pioneering medical imaging informatics and artificial intelligence research, mentoring 80+ trainees along the way.

His work bridges clinical practice, cutting-edge research, and real-world implementation—from leading Mayo Clinic's transition to filmless and paperless operations to developing deep learning algorithms that transform how physicians diagnose and treat disease.

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

Recognized globally 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 over 200 peer-reviewed publications on medical imaging AI, bias mitigation, and clinical informatics.

Recent Key Publications

United States Food and Drug Administration Review Process and Key Challenges for Radiologic Artificial Intelligence
Zhang Y, Saini N, Janus S, Swenson DW, Cheng T, Erickson BJ. J Am Coll Radiol. 2024;21(6):920-929. doi:10.1016/j.jacr.2024.02.018
Part 1: Mitigating Bias in Machine Learning—Data Handling
Rouzrokh P, Wyles CC, Philbrick KA, et al., Erickson BJ. J Arthroplasty. 2022;37(6S):S406-S413. doi:10.1016/j.arth.2022.02.092
Part 2: Mitigating Bias in Machine Learning—Model Development
Zhang Y, Wyles CC, Makhni MC, et al., Erickson BJ. J Arthroplasty. 2022;37(6S):S414-S420. doi:10.1016/j.arth.2022.02.085
Part 3: Mitigating Bias in Machine Learning—Performance Metrics, Healthcare Applications, and Fairness in Machine Learning
Faghani S, Khosravi B, Moassefi M, Rouzrokh P, Erickson BJ. J Arthroplasty. 2022;37(6S):S421-S428. doi:10.1016/j.arth.2022.02.087
Artificial Intelligence in Radiology: a Primer for Radiologists
Erickson BJ, Kitamura F. Radiol Clin North Am. 2021;59(6):991-1003. doi:10.1016/j.rcl.2021.07.004

Mission & Vision

My mission is to bridge the gap between cutting-edge AI research and practical clinical implementation. Too many promising AI tools fail at deployment—not because the algorithms are inadequate, but because they ignore the realities of clinical workflows, physician trust, and patient safety.

Through NeoSynapse.md, I aim to educate the next generation of clinicians on how to critically evaluate AI, demand transparency from vendors, and advocate for tools that genuinely improve patient care—not just boost metrics in research papers.

At FlowSigma, we're building the infrastructure to make medical AI work in practice: embedded workflows, automated quality checks, and systems that respect how physicians actually work. Because the future of medicine isn't about replacing clinicians—it's about empowering them with intelligent, reliable tools that let them focus on what matters most: healing.

Connect & Collaborate

Interested in medical AI research, workflow automation, or healthcare innovation?