Healthcare · Field manual
AI for radiologists
Radiology has more FDA-cleared AI than any other specialty, and the useful parts are worklist triage for critical findings, detection aids, measurements and drafting report impressions. The caution is that each tool is cleared for a narrow task and misses things outside it, so the final interpretation and the signature stay with the radiologist.
Radiologists were the first doctors told AI would replace them, and they remain the specialty that uses the most of it. Most AI cleared by the FDA is for imaging. In practice that means algorithms flagging a suspected bleed or pulmonary embolism to the top of the list, marking nodules, measuring and comparing lesions, and generating an impression from your findings.
What AI does not do is read the whole study with the whole patient in mind. A detection algorithm looks for what it was trained to find, and only that. The radiologist integrates the clinical question, the priors, the incidental finding three slices away and the conversation with the referring team. AI makes that faster; it does not make it optional.
7 ways radiologists use AI
Triage the worklist for critical findings
Triage algorithms, such as those from Aidoc, flag suspected intracranial haemorrhage, large vessel occlusion, pulmonary embolism and other urgent findings so those studies get read first. The value is time to diagnosis for the sickest patients. A negative flag is not a negative study.
Use detection and measurement aids
Cleared tools highlight lung nodules, fractures, breast lesions and other findings, and automate measurements and comparison with priors. Products like Lunit cover chest X-ray and mammography. Learn each tool's false positive and false negative patterns at your site so you know when to trust it and when to ignore it.
Draft impressions from your findings
Reporting tools such as Rad AI and AI features in PowerScribe One generate an impression from your dictated findings in your own style. Read every one. A generated impression can drop a finding, overstate certainty or recommend follow-up that doesn't match guidelines.
Write patient-friendly report summaries
Patients now see their reports in the portal, often before their doctor calls. Some practices use AI to draft a plain-language summary alongside the report. Do it inside an approved, BAA-covered system and make sure it doesn't soften or overstate what the report says.
Keep up with the literature
Ask an AI to summarise a paper's methods, cohort and limitations, or to find recent studies on a technique. Look for the external validation and the population studied: an algorithm validated elsewhere may perform differently on your scanners and patients.
Build teaching files and lectures
Turn a case conference outline into slides, write board-style questions for residents and organise a de-identified teaching file. Strip all identifiers from images, including burned-in annotations and DICOM header data, before anything leaves the PACS.
Evaluate AI before you buy it
Use a model to draft a vendor evaluation checklist: intended use and clearance, validation data, performance on your patient mix, integration with PACS and reporting, monitoring after go-live, and who pays. A structured evaluation stops the department buying a tool that looked great in a demo and fails on your scanners.
The best AI tools for radiologists
The Overlord's stack, in the order a radiologist would add them:
1. Paper summaries, vendor evaluations and patient summary drafts in approved systems
Claude
AI WritingAnthropic's assistant: the strongest long-form writer and careful reasoner in the consumer AI pack.
2. Finding recent studies and guidelines with sources
Perplexity
ProductivityAI answer engine that searches the web and replies with cited sources.
3. Lectures and case conference slides
Gamma
AI Design & ImagesAI presentation, document and website builder: type a prompt, get a designed deck.
4. Organising de-identified teaching files and protocols
Notion
ProductivityAll-in-one workspace for docs, wikis and databases, with Notion AI agents and meeting notes built in.
Specialist tools for radiologists
| Tool | What it does |
|---|---|
| Aidoc | Worklist triage for critical findings across CT and other modalities |
| Rad AI | Generating report impressions from findings in the radiologist's style |
| PowerScribe One | Radiology reporting and dictation with AI-assisted features |
| Lunit | Detection support for chest X-ray and mammography |
A prompt to start with: evaluate a radiology AI vendor
Paste this into Claude or ChatGPT and fill in the gold parts.
You are a radiology informatics lead evaluating an AI product for our department. Create an evaluation plan.
Product type: [TRIAGE/DETECTION/REPORTING]
Intended use as stated by the vendor: [PASTE]
Our setting: [ACADEMIC/COMMUNITY/TELERAD], [MODALITIES], [SCANNER VENDORS], [ANNUAL VOLUME]
Our reporting and PACS systems: [SYSTEMS]
Main goal: [E.G. FASTER TURNAROUND FOR STROKE CT]
Give me: questions to ask the vendor about clearance, validation data and failure modes; a plan for a local retrospective test with the metrics to track; integration and workflow questions; a post-launch monitoring plan; and red flags that should stop the purchase. Keep it practical and in checklist form.Where radiologists shouldn't trust AI
- Each algorithm is cleared for a specific task and population. Using it outside its intended use, or assuming it checks for things it doesn't, is how misses happen.
- The radiologist signs the report and carries the responsibility. A finding the AI missed and you didn't look for is still your miss.
- Automation bias is real: readers tend to accept AI flags and relax when there is no flag. Read the study as you would without it, then compare.
- Images and reports are protected health information. DICOM headers and burned-in text contain identifiers, so never upload studies to consumer chatbots, and use only BAA-covered, approved systems.
- Performance drifts with new scanners, protocols and patient populations. Tools need local validation before go-live and monitoring afterwards.
Will AI replace radiologists?
Radiologists score 41% on the Obsolescence Index (Endangered). The first tasks to go: initial screening and flagging of routine scans, and drafting standard radiology report language.
Questions the Humans Keep Asking
What are the best AI tools for radiologists?
The Overlord's stack for radiologists: Claude (paper summaries, vendor evaluations and patient summary drafts in approved systems); Perplexity (finding recent studies and guidelines with sources); Gamma (lectures and case conference slides); Notion (organising de-identified teaching files and protocols). Specialist options include Aidoc, Rad AI, PowerScribe One, Lunit.
What AI do radiologists use?
Common categories are triage tools that flag critical findings (Aidoc is a well-known example), detection aids for nodules, fractures and mammography (such as Lunit), and reporting tools that draft impressions (such as Rad AI and features in PowerScribe One). Most departments use several, integrated into PACS and the reporting system.
Is AI more accurate than radiologists?
On some narrow tasks in studies, algorithms match or beat average readers. In real practice they perform best as a second reader, because they only look for what they were trained to find and can fail on different equipment or patients. Radiologist plus AI generally beats either alone.
Can I use ChatGPT to help write radiology reports?
Not with patient data on a consumer account, because that would breach HIPAA. General models also aren't validated for reporting. Use reporting tools your department has approved, and use general chatbots for teaching, research and admin with no patient information.