Gains:
- Ability to distinguish where artificial intelligence saves real time in the imaging workflow (pre-acquisition, acquisition, reading, reporting) and where safety-critical decisions (diagnosis, release of the report, critical finding notification) are left to the radiologist, depending on the task risk level
- Ability to apply the discipline of connecting each AI output to the image and clinical context, independent review and final signature approval
- Anonymizing DICOM image and report data within the scope of KVKK/privacy and gaining the habit of choosing a safe vehicle
Hundreds of images flow through a radiology unit every day. Is the shadow on a chest radiograph a normal vessel or a small nodule; is there silent bleeding behind the patient's headache on a brain CT; whether a cluster of microcalcifications on a mammogram is benign or the first sign of an early cancer; In a control tomography, whether the nodule has really grown or is it just measured in a different plane. Some of these questions are repetitive and time-consuming; Some of them are decisions that will directly determine a person's life and whose margin of error should be close to zero. Artificial intelligence (AI for short – computer systems that can recognize patterns in images, classify them, measure them and generate text) fits right in the middle of this picture: when used correctly, it brings the urgent matter forward, speeds up the measurement, drafts the report and gives you time to think; When used incorrectly, it can transfer a seemingly safe but incorrect output to a patient report or lead you to false confidence.
The first unit of this module is not a software introduction. Its purpose is to clarify where to put AI in your profession and where not to put it at all. Because radiology is a "safety-critical" field: every report you produce affects a physician's diagnostic decision, a surgery plan, a chemotherapy dose, a patient's weeks of anxiety. Let's lay out the basic principle from the beginning: Artificial intelligence is an assistant, not a radiologist. The diagnosis, release (signing) of the report, and critical finding notification belong to the competent expert—the reporting radiologist and the attending clinician. An unverified AI output is an unsigned report.
Imaging workflow and the place of AI
To understand the business of radiology, it is useful to divide the process into four stages. Pre-extraction phase: examination request, indication (medical reason requiring examination) evaluation, protocol selection, patient preparation. Acquisition phase: application of the modality (imaging method — x-ray, CT/computed tomography, MRI/magnetic resonance, ultrasound, mammography), dose and quality adjustment. Reading phase: the radiologist evaluates the image, measurements, comparison with previous examinations. Reporting phase: structuring the findings, writing the conclusion, reporting the critical finding, and delivering the report to the clinician. AI can touch all four stages, but not each with the same authority.
By “modality” we mean the imaging modality. "Finding" is an abnormal or striking situation detected in the image. "Triage" means bringing urgent cases to the front of the worklist. A “false negative” is missing a finding that actually exists; It is the most feared type of error in radiology because it causes harm silently. AI can produce a priority signal, a flag, or a draft report for an audit; But only the radiologist evaluates whether these comply with the patient's clinical condition, history and previous examinations.
The following table summarizes the role and risk level of AI by mission:
Quest
Role of AI
Risk level
Who approves
Workload estimation, worklist planning
accelerator, planner
low
Unit manager
Protocol/dose recommendation
Suggestion, checked
Low-Medium
Technician + radiologist
Write a report draft
sketch generator
medium
radiologist
Automatic measurement (nodule, volume)
Calculator, checked
medium
radiologist
Triage/priority flag
Prioritizer, not the last word
Medium-High
Radiologist (still reads)
Lesion marking (CAD)
Pre-screener, second eye
high
Radiologist (gives the diagnosis)
Critical finding alert
reminder, draft
very high
Radiologist (confirms + reports)
Diagnosis / final report
not helpful
very high
Radiologist + clinician
Keep in mind the one line in this chart: as risk rises, AI's role shrinks, human approval grows. AI cannot "exempt" a study in any line.
Why verification is the heart of this business
Artificial intelligence seems confident in the output it gives, but it may not be sure. Image models may miss a finding (false negative) or flag a finding that does not exist (false positive). Text-generating language models, on the other hand, can add a finding that is not actually in the image to the report in a fluent sentence, just as if it were true; this is called hallucination. For example, a sentence "right adrenal adenoma" may be included in a thorax CT report draft that has never been looked at. Both traps come with equal fluidity; The only thing that separates right from wrong is your expertise and your habit of verifying.
The verification discipline consists of three steps:
- Link to image: Match every finding the AI flags and every sentence it writes into the report to the image itself. Any expression that does not have an equivalent in the image cannot be included in the report. Use AI to attract attention, not as evidence.
- Read independently / re-evaluate: Be sure to read the areas not marked by AI. A negative AI output is not a guarantee of “no findings”; Never skip your own systematic screening.
- Clinical filter: Test with the expert's eye whether the output contradicts the patient's history, indication, previous examinations and examination findings.
Caution: Signing a draft report produced by the AI without matching every sentence with the image carries the same liability as submitting an unsigned report. Smooth output is not correct output.
Privacy: image data is private data
Radiological images and reports are special personal data and are protected under KVKK (Personal Data Protection Law) in Türkiye and GDPR in Europe. Moreover, radiological data carries identity in two separate layers: DICOM header (fields such as patient name, identification number, date of birth, institution, examination date at the beginning of the image file) and sometimes burned-in text (patient information captured from the ultrasound or scanner screen and processed into pixels). Loading an image or report as is into a public AI tool exposes both layers. The rule is simple: anonymize first. Clear DICOM header, mask embedded text; When sharing the story, write "62-year-old woman, known history of breast cancer" instead of "Ayşe Yılmaz, protocol 2024-114523, breast cancer." If possible, choose corporate tools that have a data processing agreement and do not use your data in model training.
three mini cases
Case 1 — Safe use. A radiologist faces a worklist of 40 brain CT scans during a busy shift. Triage AI brings forward two tests as "possible acute bleeding". The radiologist reads these two first, finds a really large subdural bleed in one, and calls neurosurgery within 6 minutes; He sees that the other one is a movement artifact and eliminates it. He reads the remaining 38 studies in full, in order. AI changed the order, not eliminated reading; The critical event gained minutes.
Case 2 — Unverified draft trap. Another radiologist quickly signs the AI-generated draft report for a thorax CT. The draft contains the sentence "2 cm adenoma in the right adrenal gland"; whereas there was no such lesion in that section—the model produced the hallucination. In this form, the report goes to the clinician and the patient is put into an unnecessary chain of endocrine examinations. Validation was skipped, the sentence was not matched with the image.
Case 3 — Breach of confidentiality. A technician uploads an ultrasound image he finds interesting, with the patient's name embedded on it, to a public AI tool and asks "what could this be?" The image went to an external server along with the patient ID. The institution faces a KVKK review. The correct way was to mask the embedded text, clear the DICOM header, and share only the anonymous image.
Weak prompt / Strong prompt
Weak prompt:
Write this thorax CT report: Ayşe Yılmaz, TC 123..., protocol 2024-114523, I think there is something in the lung.
This request is wrong in three aspects: identity information is shared (KVKK violation), findings and modality details are not given, role and boundaries are not defined. AI fills in the gaps with guesswork and there is a risk of spurious findings.
Powerful prompt:
Your role: DRAFT preparing assistant to the reporting radiologist. Don't make a diagnosis, don't consider the report complete. Produce a structured DRAFT solely from the findings I give you; adding any findings that do not correspond to the image; Mark the area you are not sure of as "[let the radiologist confirm]". Patient: 62-year-old female, cough, history of smoking. Modality: non-contrast thorax CT. Findings given: 8 mm soft tissue density nodule in the right upper lobe; There are no lymph nodes of pathological size in the mediastinum; There is no pleural effusion. Headings: Clinical information, Technique, Findings, Conclusion, Recommendation.
The strong will is anonymous, defines the role and boundary, gives the modality and findings, prohibits fabrication, and requires a sign of verification.
Copiable prompt templates
ROLE AND BOUNDARY DESCRIPTION TEMPLATEYour role: DRAFT preparing assistant to the reporting radiologist. You are not a radiologist; making a diagnosis, releasing the report, reporting critical findings. Final approval and signature lies with the radiologist. Do not add any findings that do not correspond to the image; If you are not sure, mark it as "[let the radiologist confirm]", do not make it up. Task: [write task].
ANONYMIZATION CONTROL TEMPLATEExtract name, TR ID, protocol/examination number, date of birth, contact and institution information from the text below; Write "[removed]" instead. Leave only clinically relevant information (age group, gender, relevant history). Note: Remind me that if I am going to share an image, I need to clear the DICOM header and text embedded in the image as well. Text: [paste text]
CONFIRMATION CHECK TEMPLATEFor each finding statement in the draft you produce, indicate: (1) what input this finding is based on, (2) what is it in the image that needs to be verified, (3) where it depends on the clinical context. Use "possible/suspect" language when necessary, rather than definitive statement.
RISK LEVEL SORTING TEMPLATEPlace and justify the radiology assignment I will give you into the following categories: (A) low risk - AI outline/recommendation adequate, (B) medium risk -radiologist must confirm, (C) high/very high risk - diagnosis/decision/notification belongs to the radiologist, AI is only ancillary. Task: [write task].
Common mistakes
- Mistaking AI for a radiologist. AI scans for patterns but has no responsibility and no signature; The diagnosis is yours. The output is a draft, not a decision.
- Forgetting the identity embedded in the image. Clearing the DICOM header is not enough; The name engraved in pixels on ultrasound/scanner images should also be anonymized.
- Relying on negative AI output and relaxing the scan. "No sign" does not mean no finding; Never skip your own systematic reading.
- Signing the draft report without matching. You can add a finding that is not a model; Each sentence must be verified by image.
- Not giving clinical context. An interpretation made without indication, history, or previous examination is misleading.
Tip: For each exam, ask yourself one question: "What happens to the patient if this printout is incorrect?" If the answer is serious — and in radiology it often is — use AI only for prefetching/sketching/measurement and never skip validation.
In summary
Artificial intelligence is a powerful assistant in radiology: it brings forward the urgent case, makes measurements, produces sketches, becomes a second eye. But since you are working in a safety-critical area, diagnosis, release of the report, and notification of critical findings rest with the qualified radiologist. The role of AI in the four stages of the imaging process (pre-capture, capture, read, report) varies depending on the level of risk; As the risk increases, human approval grows. Three disciplines guard each step: image link, independent read, clinical filter. And underneath it all is confidentiality: the image and report do not enter any external device without being anonymized, including the DICOM header and embedded text.
Application task
Select three different tasks from your unit (or from an example scenario): one low risk (for example, a daily worklist summary), one for medium risk (for example, drafting a report), one for very high risk (for example, a critical findings notification). For each, (1) describe the role of the AI in one sentence, (2) write down what verification step you will take, (3) indicate how you will anonymize the data (both text and images). Then adapt the "Role and Boundaries Definition" template above to your medium risk task and write a prompt.
checklist
- [ ] I determined the risk level (low/medium/high/very high) of the task.
- [ ] I limited the AI's role to "assistant/foster/draft/measure"; diagnosis and signature by the radiologist.
- [ ] I anonymized the text; Name, TR ID, protocol and date of birth appeared.
- [ ] I noted that if I were to share images, I would clear the DICOM header and embedded text.
- [ ] I added the clinical context (age, gender, indication, history, previous examination) to the prompt.
- [ ] Despite the negative AI output, I committed to doing my own systematic reading.
- [ ] I will not sign the manuscript without matching every sentence with the image.