About Dr. Bradley J. Erickson

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.

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

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 MDSynapse.org, 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.

Dr. Bradley J. Erickson