Radiology Case-Based Question Developer
Budget: $290 – $340 USD
## Radiology Research Consultant (Remote)
I’m working on a non-clinical research project in collaboration with an AI research team, focused on understanding how experienced radiologists reason through complex imaging scenarios. The goal is to design and refine expert-level case material that can be used to evaluate and improve advanced AI systems.
This project is entirely research and educational in nature. All scenarios are hypothetical and de-identified, and no real patients, reports, or clinical services are involved.
### What you’ll do
- Review and refine existing draft imaging scenarios, then help develop additional ones to achieve a balanced mix of common and less common conditions
- Focus primarily on MRI- and CT-based reasoning, with occasional high-level references to other modalities for context when helpful
- Create diagnostic-reasoning questions (for example, multiple choice or short answer) and write clear explanations that show how imaging findings inform differential considerations
- Identify unclear wording, missing context, or inconsistencies so the scenarios remain realistic, coherent, and scientifically sound
- Provide feedback on where AI systems tend to struggle with radiologic reasoning
### Ideal background
- Fellowship-trained radiologist in any subspecialty, with particular relevance for musculoskeletal or neuroradiology experience
- Strong familiarity with complex MRI and CT interpretation workflows
- Experience explaining imaging reasoning clearly in written form, such as teaching, assessments, or case discussions
- High attention to detail and comfort working with structured research tasks
### Project details
- Fully remote, independent-contractor engagement
- Work is task-based and ongoing, with flexible scheduling
- Collaboration is primarily asynchronous, with occasional check-ins as needed
- All work is completed within the project platform using provided guidelines
This project is a good fit if you enjoy breaking down expert imaging reasoning in a research or educational context and contributing to the development of next-generation AI systems.
I’m working on a non-clinical research project in collaboration with an AI research team, focused on understanding how experienced radiologists reason through complex imaging scenarios. The goal is to design and refine expert-level case material that can be used to evaluate and improve advanced AI systems.
This project is entirely research and educational in nature. All scenarios are hypothetical and de-identified, and no real patients, reports, or clinical services are involved.
### What you’ll do
- Review and refine existing draft imaging scenarios, then help develop additional ones to achieve a balanced mix of common and less common conditions
- Focus primarily on MRI- and CT-based reasoning, with occasional high-level references to other modalities for context when helpful
- Create diagnostic-reasoning questions (for example, multiple choice or short answer) and write clear explanations that show how imaging findings inform differential considerations
- Identify unclear wording, missing context, or inconsistencies so the scenarios remain realistic, coherent, and scientifically sound
- Provide feedback on where AI systems tend to struggle with radiologic reasoning
### Ideal background
- Fellowship-trained radiologist in any subspecialty, with particular relevance for musculoskeletal or neuroradiology experience
- Strong familiarity with complex MRI and CT interpretation workflows
- Experience explaining imaging reasoning clearly in written form, such as teaching, assessments, or case discussions
- High attention to detail and comfort working with structured research tasks
### Project details
- Fully remote, independent-contractor engagement
- Work is task-based and ongoing, with flexible scheduling
- Collaboration is primarily asynchronous, with occasional check-ins as needed
- All work is completed within the project platform using provided guidelines
This project is a good fit if you enjoy breaking down expert imaging reasoning in a research or educational context and contributing to the development of next-generation AI systems.