2023 ACR Medical Student Symposium
Explore the evolving field of radiology!
Jan. 28, 2023 | Virtual
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Saturday, Jan. 28
noon–12:20pm | Welcome Address
12:20–12:40pm | Keynote Address — Leadership
12:40–12:50pm | Musculoskeletal
12:50–1pm | Pediatric Radiology
1–1:10pm | Breast Imaging
1:10–1:20pm | Neuroradiology
1:20–1:30pm | Interventional Radiology
1:30–1:40pm | Break
1:40–1:50pm | Abdominal/Body
1:50–2pm | Being a Competitive Applicant
2:05–2:15pm | Nuclear Medicine
2:15–2:25pm | Radiation Oncology
2:25–2:35pm | The Future Of Radiology
2:35–2:45pm | Twitter and Social Media
2:50–3pm | Diversity and Inclusion in Radiology
3–3:10pm | Radiology Teaches
3:10–3:20pm | Health Equity
3:20–3:30pm | Chest CV
3:30–4:20pm | Q&A With Speakers
4:20–4:30pm | Break
4:30–4:50pm | Case Competition
4:50–5pm | Closing Statements
Case Competition Rules
- The goal of the case competition is to allow medical students interested in pursuing radiology the opportunity to showcase unique cases which align with a predetermined theme.
- From the pool of submissions, the top three selected cases will be chosen to be presented during the symposium and awarded prizes for their efforts.
- For the 2023 ACR Medical Student Symposium, the theme will be Complex Cases in Radiology — Cases where radiology was the determining factor in making a difficult diagnosis. Cases can range from common conditions with an atypical presentation, or unique and rare diagnoses. Special consideration will be given to cases in which radiology played a key role in both diagnosis and management of the patient.
- Each submitted case will be blinded to authors and institutions prior to review.
- The cases will be judged based on educational significance, novelty, relevance, imaging quality and overall display.
Submission Guidelines
- Title Slide (include author first and last names and institutional affiliation)
- Brief HPI/Background
- Unlabeled Imaging Findings
- Labeled Imaging Findings With Short Explanation
- Diagnosis/Intervention/Management
- Brief Discussion/Clinical Pearls
- References (around 2–5)
Day 3 | Monday, Septober 7
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 3: 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
Day 4 | Sunday, October 8
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 4: 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
Regina Barzilay, MD, PhD
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Case Competition Prizes
6.75 CME Credit
Accreditation Statement:
The American College of Radiology is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American College of Radiology designates this other (blend of live and enduring material) activity for a maximum of 6.75 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.