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  5. Public Workshop - Evolving Role of Artificial Intelligence in Radiological Imaging - 02/25/2020 - 02/26/2020
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Workshop

Event Title
Public Workshop - Evolving Role of Artificial Intelligence in Radiological Imaging
February 25 - 26, 2020


Date:
February 25 - 26, 2020
Time:
8:00 AM - 5:30 PM ET
Location:
Event Location

The Food and Drug Administration (FDA) is announcing the following public workshop entitled "Evolving Role of Artificial Intelligence in Radiological Imaging." The intent of this public workshop is to discuss emerging applications of Artificial Intelligence (AI) in radiological imaging including AI devices intended to automate the diagnostic radiology workflow as well as guided image acquisition. The purpose of the workshop is to work with interested stakeholders to identify the benefits and risks associated with use of AI in radiological imaging. We also plan to discuss best practices for the validation of AI-automated radiological imaging software and image acquisition devices. Validation of device performance with respect to the intended use is critical to assess safety and effectiveness.

INTRODUCTION:

Artificial intelligence (AI), including machine learning technologies, has the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Radiological applications of AI-based technologies are numerous and expanding. These applications aim to automate and streamline tasks to improve efficiency, accuracy, and consistency. Early applications of AI in radiological imaging include computer aided-detection and diagnosis software (CADe and CADx). CADe and CADx software analyze radiological images to suggest clinically relevant findings and aid diagnostic decisions. Similarly, computer aided triage (CADt) software analyzes images to prioritize the review of images for patients with potentially time sensitive findings.

Large sets of widely available imaging data across imaging modalities have supported the development of AI based algorithms for these devices. While historically the information provided by these algorithms has augmented the tasks performed by radiologists, software developments now can enable the devices to perform certain tasks autonomously. The potential for independent action by these devices to bypass human clinical review is an important factor in their benefit-risk profile, and it heightens expectations for the safety and effectiveness of these devices.

Another area of growth is the use of AI to provide prescriptive guidance for the operator to acquire optimal images. The image quality of ultrasound imaging can be greatly influenced by how the operator uses a handheld probe. Clinical AI applications may assist the acquisition of standardized images independent of the operator, guiding both sonographers and non-experts in sonography, potentially including lay users, to acquire images with equivalent diagnostic quality. The addition of such clinical AI applications and the potential for new users of these devices, similarly affect the benefit risk profiles for these devices and the expectations for the safety and effectiveness of these devices.

Through this workshop, FDA is seeking to engage with stakeholders to explore benefits and risks of these evolving applications of AI in radiology. As the benefit-risk profile changes, it is critical to adapt the methods used to evaluate and characterize their performance. In this workshop, FDA is also seeking innovative and consistent ways to leverage existing methods and to develop new methods for validation of these AI-based algorithms and explore opportunities for stakeholder collaboration in these efforts.  

During the workshop, we will be discussing specific topics outlined in the Agenda below.

DATE, TIME, and LOCATION

This public workshop will be held on February 25, 2020 from 8:00 a.m. to 5:30 p.m. (EST) and February 26, 2020 from 9:00 a.m. to 5:00 p.m. (EST) at the following location:

Natcher Conference Center, Building 45
Ruth Kirschstein Auditorium
NIH Main Campus
National Institutes of Health, 9000 Rockville Pike, Bethesda MD 20892

NIH Campus Information, (e.g., local airports, directions, local hotels, etc.)

Bethesda Hotels Local to NIH Campus

American Inn of Bethesda
8130 Wisconsin Avenue, Bethesda, MD 20814
T: (301) 656-9300, F: (301) 656-2907

Bethesda Marriott Hotel
5151 Pooks Hill Road, Bethesda, MD 20814
T: (301) 897-9400, F: (301) 897-0192

Bethesda North Marriott & Conference Center
5701 Marinelli Road, N. Bethesda, MD 20852
T: (301) 984-0004, F: (301) 984-1209

Embassy Suites Hotel at the Chevy Chase Pavilion
4300 Military Road, NW, Washington, DC 20015
T: (202) 362-9300, F: (202) 686-3405

Double Tree Bethesda
8120 Wisconsin Avenue, Bethesda, MD 20814
T: (301) 652-2000, F: (301) 652-3806

Hilton Garden Inn Washington DC/Bethesda
7301 Waverly St., Bethesda, MD 20814
T: 1-800-230-4134, Group Sales: 1-800-906-2871

Hyatt Regency Bethesda
One Bethesda Metro Center, Bethesda, MD 20814
T: (301) 657-1234, F: (301) 657-6453

Marriott Suites Bethesda
6711 Democracy Blvd., Bethesda, MD 20817
T: (301) 897-5600, F: (301) 530-1427

Residence Inn by Marriott
7335 Wisconsin Avenue, Bethesda, MD 20814
T: (301) 718-0200, F: (301) 718-0679

WEBCAST

AGENDA

The following public workshop Agenda is current as of 2/28/2020 and subject to change. More information will be made available as presenters are confirmed.

February 25, 2020, Day 1

Day 1 Organizing Committee: Alex Cadotte PhD, Yanna Kang PhD, Berkman Sahiner PhD, Subok Park PhD, and Jennifer Segui (FDA)

8:00 – 8:15 AM

Welcome Day 1
Moderator and Presenter: Jennifer Segui, Lead Reviewer, Division of Radiological Health (FDA)

Welcome: The Evolving Role of AI in Radiological Imaging

8:15 – 9:15 AM

Responsible Innovation and Regulation of AI/ML in Radiological Imaging Software
Moderator: Alex Cadotte PhD, Division Team Lead, Software and Digital Health, Division of Radiological Health (FDA)

This session will provide an overview of the evolving role of AI in radiology and the medical device regulatory framework.

8:15 – 8:35 AM

Bakul Patel, MSEE, MBA, Director, Division of Digital Health, Center for Devices and Radiological Health (FDA)

Tailoring FDA's Regulatory Framework to Encourage Responsible Innovation in AI/ML

8:35 – 8:55 AM

Ronald M. Summers, MD, PhD, FSAR, FAIMBE, Senior Investigator, Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, NIH Clinical Center (NIH)

Artificial Intelligence in Radiology: Where Does It Stand and Where Is It Going?

8:55 – 9:15 AM

Robert Ochs, PhD, Deputy Director for Radiological Health, Office of In Vitro Diagnostics and Radiological Health (FDA)

New Challenges in Regulating Artificial Intelligence in Radiological Imaging

9:15 – 10:15 AM

Emerging Trends in Radiological AI Software - Exploring Benefits and Risks
Moderator: Jessica Lamb, PhD, Assistant Director (Acting), Mammography, Ultrasound, and Imaging Software Team, Division of Radiological Health (FDA)

Advances in AI technology are leading to an expanding role for AI throughout the diagnostic clinical workflow. In this session, we aim to identify scientific, clinical, and regulatory challenges for radiological AI software that is intended for increased automation of triage, detection, or diagnosis of disease based on the review of medical images.

Presentations will discuss the impact of radiological AI software on standard of care, clinical benefit, and risk.

9:15 – 9:35 AM

Constance Lehman, MD, PhD, FACR, Professor of Radiology Harvard Medical School, Chief of Breast Imaging, Massachusetts General Hospital

Clinical Insights on Autonomous AI Implementation:  A Radiologist's Perspective

9:35 – 9:55 AM

Lisa M. Baumhardt, Sr. Manager of AI Regulatory Strategy, IBM Watson Health Imaging

Artificial Intelligence in Breast Cancer Screening: Regulatory and Clinical Considerations

9:55 – 10:15 AM

Joshua Schulman, PhD, VP Clinical, Regulatory, and Quality Affairs, MaxQ AI

Diagnostic rule-out: A path to confidence and efficacy for time-sensitive, life-threatening indications

10:15 – 10:30 AM

Morning break

10:30 AM – 12:00 PM

Emerging Trends in Radiological AI Software – Discussion Panel
Moderators: Jessica Lamb, PhD and Alex Cadotte PhD, Division of Radiological Health (FDA)

Participants in this moderated discussion panel include:

  1. Joshua Schulman, PhD, MaxQ AI
  2. Lisa M. Baumhardt, IBM Watson Health Imaging
  3. Nikos Gkanatsios, PhD. Senior Director, Systems Engineering and QA, Hologic
  4. Bibb Allen, MD, FACR, Chief Medical Officer, American College of Radiology Data Science Institute
  5. David Kent, MD, Professor of Medicine, Neurology and Clinical and Translational Science; Director, Predictive Analytics and Comparative Effectiveness (PACE) Center, ICRHPS; Attending Physician, Tufts Medical Center
  6. Constance Lehman, MD, PhD, FACR, Harvard Medical School, Massachusetts General Hospital
  7. Gary Levine MD, Medical Officer, Division of Radiological Health, FDA
  8. Ronald M. Summers, MD, PhD, FSAR, FAIMBE, Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, NIH Clinical Center (NIH)
  9. Bakul Patel, MSEE, MBA, Division of Digital Health, FDA
  10. Robert Ochs, PhD, Office of In Vitro Diagnostics and Radiological Health, FDA
  11. Richard Frank MD, PhD, Chief Medical Officer, Siemens Healthineers

12:00 – 1:00 PM

Lunch

1:00 – 2:00 PM

Public Presentations
Moderator: Robert Sauer, Deputy Director, Division of Program Operations and Management (FDA)

Scientific, Clinical & Regulatory Challenges for Radiological AI Software, R. Chad McClennan, MBA, President & CEO, koios

AI in Dental Radiography, E. Gültürk, Head of Quality & Regulatory, videahealth

AI-Assisted Autonomous Imaging: Brain MR, G. Srinivasan, MediYantri

AI in Nuclear-Medicine Imaging: Opportunities and Risks, A. K. Jha, Assistant Professor of Biomedical Engineering, Washington University

Will AI Make a Better Doctor? J. Weiner, Boca Raton Regional Hospital

The Role of Medical Physics in Artificial Intelligence in Medical Imaging, C. H. McCollough, Professor of Medical Physics and Biomedical Engineering, Mayo Clinic

NCI Image Repositories for Imaging AI, K. Farahani, National Cancer Institute

The Importance of Data Quality and True Noise, P. Kinahan, Vice Chair of Radiology, University of Washington, Chair AAPM Research Committee, AAPM Metrology and Standards Subcommittee

AI in Medical Imaging and Decision Support: The Perspective of the RSNA Quantitative Imaging Biomarkers Alliance (QIBA®), T. J. Hall, Vice-Chair RSNA-QIBA

Deployment of AI in Hospitals: Considerations Beyond AI Models, B. Hashemian, MGH & BWH Center for Clinical Data Science; Partners Healthcare

Medical Imaging AI - Data Standards, Kevin O’Donnell, Canon Medical Research USA

Autonomous AI and Ethics: lessons from real world implementation: Role of Physicians, M.D. Abramoff, Founder and Executive Chairman, IDx

2:00 – 3:25 PM

Evaluation of AI Software for Radiological Applications
Moderator: Subok Park, PhD, Lead Reviewer, Division of Radiological Health (FDA)

Stakeholders across the medical imaging community are working to develop effective methods for the validation of radiological AI software. This session will feature a discussion of ongoing efforts to develop methods and standards for the validation of these medical devices and different roles stakeholders can play.

Presentations will discuss individual and collaborative efforts to develop validation methods for imaging-related AI inside and outside the traditional medical imaging community.

2:00 – 2:15 PM

Nicholas Petrick, PhD, Deputy Director Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories (FDA)

Pre- and Post-Market Evaluation of Autonomous AI/ML: Lessons Learned from Prior CAD Devices

2:15 – 2:30 PM

Matthew Diamond, MD, PhD, Medical Officer, Digital Health, Center for Devices and Radiological Health (FDA)

Proposed Regulatory Framework for Modifications to Artificial Intelligence/ Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD)

2:30 – 2:50 PM

Peter Chang, MD, Assistant Professor-in-Residence, Department of Radiological Sciences, Co-Director, Center for Artificial Intelligence in Diagnostic Medicine (CAIDM), University of California, Irvine

Evolving Paradigms of Radiology AI Software - Current and Future Trends

2:50 – 3:10 PM

Laura P Coombs, PhD, VP, Data Science and Informatics, American College of Radiology and Keith Dreyer DO, PhD, FACR, FSIIM, Chief Data Science Officer, Partners Healthcare, Chief Science Officer, ACR Data Science Institute

The AI Lifecycle: Evaluating and Monitoring AI Algorithms

3:10 – 3:25 PM

Robert Lindsey, PhD, Co-Founder & Chief Science Officer, Imagen Technologies

Post-Market Surveillance Methods for AI in Radiology

3:25 – 3:40 PM

Afternoon break

3:40 – 5:20 PM

Evaluation of AI Software for Radiological Applications- Discussion Panel
Moderator: Berkman Sahiner, PhD, Physicist, Division of Imaging, Diagnostics, and Software Reliability (FDA)

Participants in this moderated discussion panel include:

  1. Maryellen L. Giger, PhD, Professor, Department of Radiology, University of Chicago and Advisor, Qlarity Imaging
  2. Arun Krishnan, PhD, Development Executive, AI Algorithms, IBM Watson Health Imaging
  3. David B. Larson, MD, MBA, Associate Professor of Pediatric Radiology Vice Chair of Education and Clinical Operations Department of Radiology, Stanford University School of Medicine
  4. Holger Roth, PhD, Sr. Applied Research Scientist, NVIDIA
  5. Marc Edgar, Sr. Staff Data Scientist, GE Healthcare – Digital
  6. Danica Marinac-Dabic, MD, PhD, MMSc, FISPE, Associate Director, Office of Clinical Evidence and Analysis, FDA
  7. Laura P. Coombs, PhD, American College of Radiology
  8. Keith Dreyer DO, PhD, FACR, FSIIM, Partners Healthcare, ACR Data Science Institute
  9. Matthew Diamond, MD, PhD, FDA
  10. Robert Lindsey, PhD, Imagen Technologies
  11. Nicholas Petrick, PhD, FDA
  12. Peter Chang, MD, University of California, Irvine

5:20 – 5:30 PM

Conclusion – Day 1 Summary
Moderator and Presenter: Jennifer Segui, Lead Reviewer, Division of Radiological Health (FDA)

Perspectives on the AI-Automated Radiology Workflow

February 26, 2020, Day 2

Day 2 Organizing Committee: Shahram Vaezy PhD, Marjan Nabili PhD, Tracy Gray, Jennifer Segui (FDA)

9:00 – 9:10 AM

Welcome Day 2
Moderator and Presenter: Marjan Nabili, PhD, Biomedical Engineer, Division of Radiological Health (FDA)

An Introduction to AI-Guided Image Acquisition

9:10 – 9:50 AM

Opportunities and Challenges in AI-Enabled Healthcare
Moderator: Marjan Nabili, PhD, Biomedical Engineer, Division of Radiological Health (FDA)

9:10 – 9:30 AM

Anthony Samir, MD, Director of CURT and Service Chief, Body Ultrasound
Department of Radiology/Massachusetts General Hospital

No-Human-in-the-Loop AI-enabled Healthcare: Risk, Rewards, and Regulation

9:30 – 9:50 AM

Randy King, Ph.D., Program Director, Ultrasound, Division of Applied Science and Technology and Behrouz Shabestari, Ph.D., Acting Director - Division of Health Informatics Technologies, Division of Applied Science & Technology (Bioimaging), National Institute of Biomedical Imaging and Bioengineering (NIBIB)

Implications and Opportunities for AI Implementation in Diagnostic Medical Imaging

9:50 – 10:50 AM

Innovation in AI-Guided Image Acquisition
Moderator: Shahram Vaezy, PhD, Biomedical Engineer, Division of Radiological Health (FDA)

Artificial intelligence may be applied in imaging to provide guidance for the operator to acquire optimal images and signals. The clinical applications include assisting sonography experts for standardization of image quality, helping non-experts in sonography for acquiring images with quality equivalent to those obtained by expert, and guiding novices, such as home users, for collecting images of diagnostic quality and value. In this session, we aim to identify scientific, clinical, and regulatory challenges for AI-Guided Image/Signal Acquisition systems.

Presentations will discuss the impact of AI-Guided Image/Signal Acquisition systems on the standard of care, clinical benefits, and risks.

9:50 – 10:05 AM

Ha Hong PhD, Investigator, Co-founder, Caption Health Inc.

Development and Validation of a Breakthrough AI-Guided Echocardiography System

10:05 – 10:20 AM

Benny Lam PhD, Principal Regulatory Affairs Specialist, AI/DH Regulatory Intelligence, Philips Ultrasound

Deep Learning-enabled Ultrasound Imaging – Opportunities, Risks, and Challenges

10:20 – 10:35 AM

John Martin, MD, Chief Medical Officer, Butterfly Network

Can machine learning tools bring diagnostic imaging to the home with safety and efficacy?

10:35: 10:50 AM

Mike Washburn, Chief Engineer, GE Healthcare - Ultrasound

AI-guided ultrasound image acquisition; GE perspective

10:50 – 11:00 AM

Morning Break

11:00 – 11:45 AM

AI-Guided Image Acquisition: Clinical and Patient Perspectives
Moderator: Brian Garra, MD, Physician, Division of Imaging, Diagnostics, and Software Reliability (FDA)

11:00 – 11:15 AM

Helen Feltovich, MD MS, Attending Physician, Intermountain Healthcare, and Associate Research Scientist, Medical Physics, University of Wisconsin, Madison

What's Real About Artificial Intelligence: A Clinician's Perspective

11:15 – 11:30 AM

Jean Lea Spitz, MPH, CAE, RDMS, Executive Director, Perinatal Quality Foundation

Artificial Intelligence (AI) and the Art of Sonography

11:30 – 11:45 AM

Joshua Basile, Founder, Determined2Heal & SPINALpedia;
Harsh Thakkar, Program coordinator at MedsStar NRH, President of United Spinal Metro DC Chapter

Patient Perspectives: Real-Life Excitement and Concerns of Community-Based AI Imaging

Isabella and Amy O'Brien, Youth Member and Parent, International Children's Advisory Network (iCAN)

At Home / Mobile Diagnostics

11:45 AM – 12:45 PM

AI-Guided Image Acquisition - Discussion Panel
Moderator: Shahram Vaezy, PhD, Biomedical Engineer, Division of Radiological Health (FDA)

Participants in this moderated discussion panel include:

  1. Ha Hong PhD, Caption Health Inc.
  2. Rob Trahms, Artificial Intelligence Leader, Philips Healthcare
  3. John Martin MD, Butterfly Network
  4. Mike Washburn, GE Healthcare - Ultrasound
  5. Helen Feltovich MD, Intermountain Health, Perinatal Quality Foundation
  6. Jean Lea Spitz MPH, CAE, RDMS, Perinatal Quality Foundation
  7. Anthony Samir MD, Massachusetts General Hospital
  8. Randy King PhD, NIBIB/NIH
  9. Behrouz Shabestari PhD, NIBIB, NIH
  10. Brian Garra, MD, FDA
  11. Richard Frank, Chief Medical Officer, Siemens Healthineers
  12. Joshua Basile, Determined2Heal & SPINALpedia
  13. Harsh Thakkar,  MedsStar NRH
  14. Isabella and Amy O'Brien, iCAN

12:45 – 1:45 PM

Lunch

1:45 – 2:45 PM

Public Presentations
Moderator: Jessica Lamb, PhD, Assistant Director (Acting), Mammography, Ultrasound, and Imaging Software Team, Division of Radiological Health (FDA)

Explainable AI and Regulation in Medical Devices, D. Ritscher, Senior Consult, Cambridge Consultants

AI in Medicine: Not Optional, A. A. Patel, Chair, Department of Radiology, Medical Director – Artificial Intelligence, Vice Chair, Imaging Informatics, Medical Director – 3D Laboratory, Geisinger

AI in Pediatric Radiology: Special Considerations, Ethics, and Explainability, A. Annapragada, Professor and Director of Basic Research in Radiology, The E.B.Singleton Department of Pediatric Radiology, Texas Children’s Hospital and Baylor College of Medicine

Applications of AI to Ultrasonography, H. Sagreiya, Assistant Professor of Radiology, University of Pennsylvania

AI software for image enhancement and better acquisition, E. Gong, Subtle Medical

AI-enabled imaging acquisition, image transformation, and dose reduction, G. Zaharchuk, Professor of Radiology, Stanford University

Build Trustworthy Medical Imaging AI. S. Wu, Associate Professor, Director, Intelligent Computing for Clinical Imaging (ICCI) Lab, Dept. Radiology, University of Pittsburgh

How Investors Can Encourage Startups to Work Constructively with the FDA, A. A. Vidian, Partner, DCVC

Connected Health Initiative, B. Scarpelli, Senior Global Policy Counsel

NCI Funding Opportunities for Early, Mid, Advanced and Translational Stage AI Imaging Developments, G. Redmond, H. Baker, Imaging Technology Development Branch, National Cancer Institute

2:45 – 3:45 PM

Regulation of Imaging Devices Containing AI Software
Moderator: Garrett Astary, PhD, Biomedical Engineer, Division of Radiological Health (FDA)

This session will feature a discussion of regulatory evaluation of AI-Guided Image Acquisition systems.  Ongoing efforts to develop methods and standards for the verification and validation of AI-Guided Image Acquisition systems will be discussed. Some emphasis will be placed on use of these devices by non-expert clinicians and patients as well as use of image acquisition systems in extreme environments.

Presentations will discuss individual and collaborative efforts to develop validation strategies for AI-Guided Image Acquisition inside and outside of the traditional medical imaging community.

.

2:45 – 3:00 PM

Shahram Vaezy, PhD, Biomedical Engineer, Division of Radiological Health (FDA)

Regulatory Considerations for AI-Guided Image Acquisition and Optimization

3:00 – 3:15 PM

Scott Paulson, Sr. Director of Regulatory Affairs and Quality Systems; Compliance Officer, FUJIFILM SonoSite

Achieving the Quadruple Aim with AI-Guided Ultrasound Systems in Clinical Settings Using the Current Ultrasound Premarket Guidance - Benefits and Challenges

3:15 – 3:30 PM

Berkman Sahiner, PhD, Physicist, Division of Imaging, Diagnostics, and Software Reliability (FDA)

Validation of AI Algorithms in Guided Imaging Applications  

3:30 – 3:45 PM

Xin Feng, PhD, Human Factors and Reliability Engineering Team (FDA)

Human Factors and Usability Engineering for AI-guided Acquisitions 

3:45 – 4:00 PM

Andy Milkowski, Senior Director Ultrasound R&D, Siemens Healthineers

Practical AI Experience from Imaging Industry

4:00 – 4:45 PM

Public Health and Regulation of Imaging Devices Containing AI Software - Discussion Panel
Moderator: Garrett Astary, PhD, Biomedical Engineer, Division of Radiological Health (FDA)

Participants in this moderated discussion panel include:

  1. Shahram Vaezy, PhD, FDA
  2. Berkman Sahiner, PhD, FDA
  3. Brian Garra, MD, FDA
  4. Xin Feng, PhD, FDA
  5. Sam Surette, Caption Health
  6. Benny Lam, PhD, Philips Ultrasound
  7. Scott Paulson, FUJIFILM SonoSite
  8. Tony Roder, Regulatory Affairs Executive, GE Healthcare
  9. Andy Milkowski, PhD, Siemens Healthineers

4:45 – 5:00 PM

Conclusion – Day 2 Summary
Moderator and Presenter: Jessica Lamb, PhD, Assistant Director (Acting), Mammography, Ultrasound, and Imaging Software Team, Division of Radiological Health (FDA)

Path Forward for AI in Radiological Imaging: Guidance & Autonomy

Event Managerial and Supervisory Committee:

  • Robert Ochs PhD, Deputy Director for Radiological Health, Office of In Vitro Diagnostics and Radiological Health (FDA)
  • Thalia Mills PhD, Director, Division Radiological Health (FDA)
  • Jessica Lamb PhD, Assistant Director (Acting), Mammography, Ultrasound, and Imaging Software Team, Division of Radiological Health (FDA)
  • Laurel Burk PhD, Assistant Director, Diagnostic X-ray Systems Team, Division of Radiological Health (FDA)
  • Robert Sauer, Deputy Director, Division of Program Operations and Management (FDA)
  • Nicholas Petrick PhD, Deputy Director Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories (FDA)
  • Matthew Diamond MD, PhD, Medical Officer, Digital Health, Center for Devices and Radiological Health (FDA)
  • Michelle Tarver MD, PhD, Director, Patient Science & Engagement, Center for Devices and Radiological Health (FDA)

Transcripts

Day 1

Day 2

REGISTRATION

Registration is now closed as of February 14, 2020.

There is no fee to register for the workshop and registration will be on a first-come, first-served basis.

If you need special accommodations due to a disability, please contact Susan Monahan at 301-796-5661, at susan.monahan@fda.hhs.gov at least 7 days in advance of the meeting.

Registrants will receive confirmation when requests for participation have been accepted. Onsite registration will not be available.

REQUESTS FOR ORAL PRESENTATIONS

This public workshop includes a public comment session and topic-focused sessions. During online registration, you may indicate if you wish to present during a public comment session, and which topics you wish to address. All requests to make oral presentations must be received by January 10, 2020, 4 p.m. If selected for presentation, FDA will notify presenters by January, 24, 2020 and presentation materials must be emailed to RadAIWorkshop2020@fda.hhs.gov by February 5, 2020 to present at the event. No commercial or promotional material will be permitted to be presented or distributed at the public workshop.

If you require special accommodations due to a disability, or need additional information regarding registration, please contact Susan Monahan, Office of Communication and Education, Center for Devices and Radiological Health, Food and Drug Administration, 10903 New Hampshire Avenue, Bldg. 32, Silver Spring, MD 20993, 301-796-5661, Susan.Monahan@fda.hhs.gov

SUBMIT COMMENTS

Please submit your comments regarding the workshop to https://www.regulations.gov/, Docket No. FDA-2019-N-5592 by June 30, 2020. 

Please refer to the instructions for submitting comments to the docket to ensure that your feedback is received. 

Please be advised that as soon as a transcript is available, it will be posted in the Dockets and accessible at http://www.regulations.gov

CONTACT

For questions regarding workshop content please contact:

Jennifer A. Segui
Center for Devices and Radiological Health
Food and Drug Administration
10903 New Hampshire Ave.
Bldg. 66, Rm 3545.
Silver Spring, MD 20993
301-796-6370
RadAIWorkshop2020@fda.hhs.gov

 


Event Materials

Title File Type/Size
Instructions for Submitting Comments - Evolving Role of Artificial Intelligence in Radiological Imaging pdf (25.70 KB)
Welcome: The Evolving Role of AI in Radiological Imaging pdf (255.80 KB)
Tailoring FDA's Regulatory Framework to Encourage Responsible Innovation in AI/ML pdf (1.18 MB)
Artificial Intelligence in Radiology: Where Does It Stand and Where Is It Going? pdf (43.87 MB)
New Challenges in Regulating Artificial Intelligence in Radiological Imaging pdf (323.88 KB)
Clinical Insights on Autonomous AI Implementation: A Radiologist's Perspective pdf (4.16 MB)
Artificial Intelligence in Breast Cancer Screening: Regulatory and Clinical Considerations pdf (3.13 MB)
Diagnostic rule-out: A path to confidence and efficacy for time-sensitive, life-threatening indications pdf (1.65 MB)
Pre- and Post-Market Evaluation of Autonomous AI/ML: Lessons Learned from Prior CAD Devices pdf (615.45 KB)
Proposed Regulatory Framework for Modifications to Artificial Intelligence/ Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) pdf (976.44 KB)
Evolving Paradigms of Radiology AI Software - Current and Future Trends pdf (1.76 MB)
The AI Lifecycle: Evaluating and Monitoring AI Algorithms pdf (3.10 MB)
Post-Market Surveillance Methods for AI in Radiology pdf (3.23 MB)
Perspectives on the AI-Automated Radiology Workflow pdf (1.37 MB)
Scientific, Clinical & Regulatory Challenges for Radiological AI Software pdf (304.79 KB)
AI in Dental Radiography pdf (405.19 KB)
AI-Assisted Autonomous Imaging pdf (11.69 MB)
AI in Nuclear-Medicine Imaging: Opportunities and Risks pdf (2.52 MB)
Will AI Make a Better Doctor? pdf (2.32 MB)
The Role of Medical Physics in Artificial Intelligence in Medical Imaging pdf (1.94 MB)
NCI Image Repositories for Imaging AI pdf (687.60 KB)
The Importance of Data Quality and True Noise pdf (620.53 KB)
AI in Medical Imaging and Decision Support: The Perspective of the RSNA Quantitative Imaging Biomarkers Alliance pdf (275.13 KB)
Deployment of AI in Hospitals: Considerations Beyond AI Models pdf (163.45 KB)
Medical Imaging AI - Data Standards pdf (314.98 KB)
Autonomous AI and Ethics: lessons from real world implementation: Role of Physicians pdf (2.27 MB)
An Introduction to AI-Guided Image Acquisition pdf (270.26 KB)
No-Human-in-the-Loop AI-enabled Healthcare: Risk, Rewards, and Regulation pdf (2.78 MB)
Implications and Opportunities for AI Implementation in Diagnostic Medical Imaging pdf (1.65 MB)
Development and Validation of a Breakthrough AI-Guided Echocardiography System pdf (1.39 MB)
Deep Learning-enabled Ultrasound Imaging – Opportunities, Risks, and Challenges pdf (259.89 KB)
Can machine learning tools bring diagnostic imaging to the home with safety and efficacy? pdf (1.80 MB)
AI-guided ultrasound image acquisition; GE perspective pdf (5.25 MB)
What's Real About Artificial Intelligence: A Clinician's Perspective pdf (16.91 MB)
Artificial Intelligence (AI) and the Art of Sonography pdf (949.70 KB)
Patient Perspectives: Real-Life Excitement and Concerns of Community-Based AI Imaging pdf (2.29 MB)
At Home / Mobile Diagnostics pdf (210.44 KB)
Regulatory Considerations for AI-Guided Image Acquisition and Optimization pdf (221.90 KB)
Achieving the Quadruple Aim with AI-Guided Ultrasound Systems in Clinical Settings Using the Current Ultrasound Premarket Guidance - Benefits and Challenges pdf (1.32 MB)
Validation of AI Algorithms in Guided Imaging Applications pdf (261.41 KB)
Human Factors and Usability Engineering for AI-guided Acquisitions pdf (862.96 KB)
Practical AI Experience from Imaging Industry pdf (1.55 MB)
Path Forward for AI in Radiological Imaging: Guidance & Autonomy pdf (404.34 KB)
Explainable AI and Regulation in Medical Devices pdf (397.39 KB)
AI in Medicine: Not Optional pdf (727.11 KB)
AI in Pediatric Radiology: Special Considerations, Ethics, and Explainability pdf (2.11 MB)
Applications of AI to Ultrasonography pdf (493.93 KB)
AI software for image enhancement and better acquisition pdf (1.31 MB)
AI-enabled imaging acquisition, image transformation, and dose reduction pdf (859.12 KB)
Build Trustworthy Medical Imaging pdf (2.69 MB)
How Investors Can Encourage Startups to Work Constructively with the FDA pdf (440.41 KB)
Connected Health Initiative pdf (1.47 MB)
NCI Funding Opportunities for Early, Mid, Advanced and Translational Stage AI Imaging Developments pdf (141.18 KB)