Implement artificial intelligence (AI) in your practice with new, innovative solutions

Noon: Welcome Address

Moderators: Tonuka Chatterjee, BSc and Ryan Morrison, BS

12:05–12:15PM: Introduction to ACR Medical Student Subcommittee

Speaker: Neil Jain, DO, Chair, ACR Medical Student Subcommittee, PGY-1 IR/DR Pathway MedStar Georgetown University Hospital

12:15–12:45PM: Keynote Session

Speaker: Geraldine B. McGinty, MD, MBA, FACR, ACR President

12:45–1:55PM: Subspecialties Session (10 minutes each)

Speakers:

• Neuroradiology - Dr. Ann K. Jay – Medstar Georgetown University Hospital.
• Pediatric Radiology - Dr. Summer L. Kaplan – Children's Hospital of Philadelphia.
• Musculoskeletal Radiology - Dr. William Morrison – Thomas Jefferson University Hospital.
• Body Magnetic Resonance Imaging (MRI) - Dr. Amita Kamath – Mount Sinai Hospital.
• Breast Radiology - Dr. Amy Patel – Liberty Hospital and University of Missouri - Kansas City School of Medicine.
• Interventional Radiology - Dr. Bill S. Majdalany – Emory University School of Medicine.
• Radiation Oncology - Dr. Iris Gibbs – Stanford University Medical Center

1:55–2:10PM: Women and Diversity

Learn about the ways in which radiology is tackling diversity in medicine.

Speaker: Dr. Johnson Lightfoote

2:10–2:25PM: Artificial Intelligence in Radiology

Learn about the role of artificial intelligence in the growing field of radiology.

Speaker: Dr. Tessa Cook

2:25–2:40PM: COVID-19 and Radiology  

Learn how the role of radiology has changed throughout this pandemic.

Speaker: Dr. Daniel Ocazionez-Trujillo

2:40–2:55PM: Tips for Successfully Matching into Radiology

Learn how to maximize your chances for matching into diagnostic radiology and interventional radiology residencies!

Speaker: Dr. Jocelyn Rapelyea – George Washington University

3–3:10PM: Break

3:10–3:55PM: Case Competition

All medical students are invited to submit case presentations that showcase unique diagnostic radiology cases!

The cases should follow the general outline of: Title slide (author names, institution), HPI, Diagnosis, Imaging Findings, Discussion/Novelty.

All cases must be original work and cannot have been previously presented or published elsewhere. Presentations should be between 5–10 minutes long and a maximum of eight PowerPoint slides. Presentations must be submitted as a PowerPoint file (.pptx, .ppt). All files should be submitted to [email protected] and [email protected] by January 10, 2021, at 11:59 PM ET.

A selection committee will select three cases which will be presented at the symposium. Each case will be judged based on educational quality, clarity, relevance and novelty. Cases will be blinded to both author and institution names upon review. Prizes will be awarded to the case competition winners.

3:55–4PM: Break

4–5PM: Escape the Room

Do you have what it takes to unlock the mystery of the missing radiologist?

Follow clues to solve the puzzle and build teamwork, spark friendly competition, and support well-being along the way!

KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams

Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.

Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)

SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion

Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.

• Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
• How to begin using AI
• Looking for increased efficiency in operations and incorporating AI into workflows

Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester

Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School

KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams

Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.

Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)

SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion

Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.

• Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
• How to begin using AI
• Looking for increased efficiency in operations and incorporating AI into workflows

Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester

Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School

KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams

Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.

Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)

SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion

Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.

• Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
• How to begin using AI
• Looking for increased efficiency in operations and incorporating AI into workflows

Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester

Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School

KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams

Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.

Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)

SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion

Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.

• Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
• How to begin using AI
• Looking for increased efficiency in operations and incorporating AI into workflows

Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester

Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School

KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams

Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.

Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)

SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion

Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.

• Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
• How to begin using AI
• Looking for increased efficiency in operations and incorporating AI into workflows

Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester

Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School

KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams

Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.

Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)

SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion

Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.

• Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
• How to begin using AI
• Looking for increased efficiency in operations and incorporating AI into workflows

Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester

Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School

KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams

Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.

Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)

SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion

Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.

• Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
• How to begin using AI
• Looking for increased efficiency in operations and incorporating AI into workflows

Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester

Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School