Attending the annual SIIM meeting for the first time was a privilege as an early career professional with an interest in imaging informatics. Although I first learned about SIIM through the National Imaging Informatics Course – Radiology (NIIC-RAD) in 2021, SIIM 2022 was my first in-person, immersive experience with the organization. Anecdotes relayed to me from SIIM members regarding the organization’s collegial culture and cutting-edge sessions cultivated anticipation for the meeting. Furthermore, I was fortunate to participate in initiatives led by the Members-in-Training committee, and I couldn’t wait to meet our team face-to-face for the first time.

My first annual SIIM meeting completely exceeded expectations. I was humbled by the warm welcome starting at the registration table from the legendary SIIM staff who tirelessly worked to ensure that all conference operations performed seamlessly. Even before the first session, I was thrust into a world with conversations where I wasn’t the only individual enthusiastic about imaging informatics! Excitement emanated throughout the hallways, arising from multidisciplinary conversations that often featured selfless acts of sharing advice to advance clinical and research endeavors. In fact, several of these conversations helped me reframe my approach to research questions that I struggled with prior to the meeting.

Aside from the “hallway track,” the program included formal sessions that built on the foundation provided by the NIIC-RAD. I left each session with a desire to learn more and inspiration to approach issues in imaging informatics with a multidisciplinary approach. For example, sessions on AI monitoring and the business case of AI introduced me to practical considerations for the future of imaging AI that I hope to explore in my practice. Other sessions featuring small-group discussions with data scientists and informaticists helped me approach AI workflow integration issues from alternative perspectives that I may not encounter on a daily basis.

I found that the organization is filled with members committed to helping early career professionals succeed in their career aspirations. Through the Members-in-Training sessions, I had the invaluable opportunity to learn from the foremost informatics educators, who generously set aside time from their busy schedules to provide advice on pursuing a career in imaging informatics. Additionally, the program committee created special events for first-time attendees to network with each other and meet leaders from SIIM committees, who provided guidance on making the most of the annual meeting.

As a newly-minted first-time attendee, I am motivated to continue working with the Members-in-Training committee to ensure that other early career professionals are aware of the plethora of resources offered by SIIM for career advancement. Additionally, as someone invested in informatics education, I cannot wait to share the knowledge and skills bestowed at the meeting with those who were unable to attend. Experiencing the altruistic culture first-hand left an everlasting impression, and I am proud to belong to the diverse SIIM community.

Ali Tejani, MD

Written by

Publish date

Jun 27, 2022

Topic

  • Annual Meeting

Media Type

  • News & Announcements

Audience Type

  • Clinician
  • Developer
  • Imaging IT
  • Researcher/Scientist

post

AAPM, ACR, RSNA, and SIIM Announce Joint Effort in Developing Innovative AI Educational Framework for Radiology

Oct 1, 2025

FOR IMMEDIATE RELEASE Leesburg, VA – October 1, 2025 In a groundbreaking collaboration led by the SIIM Machine Learning Education…

podcast

My Informatics Journey with Dr. Steve Horii – Part 1

Sep 24, 2025

Affectionately known as Dr. DICOM. The SIIMcast team got a rare chance to sit with Dr. Steve Horii during the…

post

Multi-Agent AI and Ethics in Radiology: Navigating the Trust Crisis in Advanced Medical Systems (Part 1)

Sep 9, 2025

Sara Salehi, Bradley J Erickson, Yashbir Singh 

Picture this: you’re a radiologist examining a chest X-ray, and an AI system flags a potential abnormality with 95% confidence.…