Radiology Residency Computing & Innovation Club
simple principle:
the only barrier for a resident to start, maintain, or improve a project that enhances the residency through computing should be their interest.
Faculty Leadership: Ali Dhanaliwala (clinical) + Kristen Martin (IS)
Resident Leadership: Ianto Xi, Alvaro Ordonez, Vineeth Gangaram
Current Members:
- Anoop Manjunath
- Peter Qiao
- Tuan Vu
- Shahriar Faghani
Current Projects:
- Index Study Finder
- AIRP Case Finder
- Case Logger
Why?
Use off-the-shelf image models to predict the "educational value" of chest x-rays and route high educational value studies to residents
Explore and document the capabilities of artificial intelligence in radiology
Why?
Use off-the-shelf image models to predict the "educational value" of chest x-rays and route high educational value studies to residents
Explore how humans interact with artificial intelligence in a deployed scenario
Stage 1: Retrospective analysis of PMBB data to determine the capabilities of open models
Stage 2: Depending on capabilities of open models, work with the chest department and AI Insights Infra to deploy the routing platform
Stage 3: Create HCI/HAII experiments making use of the platform
Stage 1: Model Retrospective Analysis
How to define "educational value"
- By list of important diagnoses
- By list of rare diagnoses
- By list of diagnoses individualized to that resident that the resident hasn't yet seen
- By diagnosis/finding individualized to the resident that they had missed in the past
Take advantage of our Macro-X system to filter for studies that have "Great Call" "Major Change" "Minor Change" "Notification"
| Class | Last Year | All Time | PMBB |
|---|---|---|---|
| All | 583,468 | 6,877,112 | ~2K |
| Notify | 6,044 | 86,901 | 1,693 |
| Major | 204 | 1,409 | 221 |
| Minor | 6,337 | 28,754 | 2,548 |
| Great | 228 | 1,918 | 347 |
| Model | Output | License |
|---|---|---|
| RAD-DINO | Embeddings | MIT |
| MAIRA-2 | VLM | MSRLA + disclaimer |
| MedSigLIP | Embeddings | HAI-DEF |
| MedGemma-27b | VLM | HAI-DEF |
| ARK+ | Embeddings | ASU academic |
Model
AI Insights Infra
Router
AI
Resident
Extender
$0.493 per hour for a NC4as T4 v3 (16gb VRAM)
- Embedding Models
- RAD-DINO
- MedSigLIP
- ARK+
- 4B VLMS
- MedGemma-4B
$3.505 per hour for a NV36ads A10 V5 (24gb VRAM)
- 7B VLMS
- MAIRA-2
*27B VLMS (Would require 80GB VRAM and are therefore too expensive for deployment -- MedGemma-27B)
deck
By Vineeth Gangaram
