Gains:
- Ability to distinguish where artificial intelligence saves real time in the laboratory workflow (pre-analytical, analytical, post-analytical) and where safety-critical decisions (result validation, clinical interpretation, diagnosis) are left to the expert, according to the task risk level
- Ability to apply a discipline that validates each AI output through the steps of linking it to the source, recalculating it, and clinical/analytical filtering.
- Anonymizing patient data within the scope of KVKK/privacy and gaining the habit of choosing a safe vehicle
Thousands of decisions are made silently every day in a medical laboratory. Will a tube be accepted or rejected; whether a potassium result is real or due to hemolysis; whether the cell seen in a blood smear is a normal lymphocyte or a blast; This may be why a patient's creatinine has doubled compared to its previous value. Some of these decisions are repetitive and time-consuming; Some of them are decisions that can directly affect a person's life and whose margin of error should be close to zero. Artificial intelligence (AI, or AI for short—computer systems that can generate text, recognize patterns, classify images, and perform calculations like humans) fits right in the middle of this picture: when used correctly, it speeds up repetitive tasks and gives you time to think; When used incorrectly, it can carry a seemingly safe but incorrect output to a patient report.
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 medical laboratory sciences is a "safety-critical" field: every result you produce can affect a physician's diagnostic decision, a medication dose, a surgery delay. Let's reiterate the basic principle here: Artificial intelligence is an assistant, not a laboratory expert. The decision to release (validate) the result, clinical interpretation and diagnosis rests with the competent expert—the laboratory specialist physician and the attending clinician.
Total testing process and place of AI
To understand laboratory work, it is necessary to divide the process into three phases. The pre-analytical phase is the phase from ordering the sample until it enters the device: test order, patient preparation, blood collection, labeling, transport, centrifugation. The analytical phase is the phase where the measurement is made on the device. The post-analytical phase is the verification, interpretation, reporting and delivery of the result to the clinician. The interesting thing is that most laboratory errors occur in the pre-analytical phase, not in the analytical phase (instrument). AI can touch all three phases, but not each with the same authority.
By "analyte" we mean the substance or parameter being measured: glucose, creatinine, potassium, hemoglobin, etc. The "reference range" is the range of expected values of an analyte in a healthy population and varies by age, gender, method. "Validation" is used in the laboratory in two senses: confirming a result before releasing it, and proving the reliability of a method/method/model. AI can produce a clear preliminary interpretation of a result; But only the expert evaluates whether that interpretation is appropriate for the patient's clinical condition, medications and previous results.
The following table summarizes the role and risk level of AI by mission:
Quest
Role of AI
Risk level
Who approves
Workload estimation, sample sorting
accelerator, planner
low
Laboratory manager
Writing a conclusion comment draft
Sketch generator, controlled
medium
laboratory specialist
Delta check / QC alert generation
Statistical stimulus
Medium-High
Expert (finds root cause)
Image pre-classification (smear, urine)
Preliminary, never the final word
high
Specialist (confirms diagnosis)
Panic value notification
Browser, draft generator
very high
Expert (confirms, reports)
Clinical diagnosis/decision
not helpful
very high
Physician + laboratory specialist
Keep in mind the one line in this chart: as risk rises, AI's role shrinks, human approval grows.
Why "verification" is the heart of this business
Artificial intelligence language models seem confident in their answer, but they may not be sure. In technical language, this is called hallucination: it is the model's fabrication of non-existent information in a fluent sentence, as if it were true. For a laboratory professional, this is a fatal trap: the model may tell you “normal serum potassium in an adult is 3.5-5.1 mmol/L” (which is close to accurate), but it may also answer “normal ferritin range is 30-400 ng/mL for everyone,” ignoring the sex difference (this is incomplete and misleading). Since he says both with the same fluency, the only thing that separates right from wrong is your knowledge and habit of verifying.
The verification discipline consists of three steps:
- Link to source: For information such as reference range, unit, method, rely on your own laboratory's validated records and recognized sources (manufacturer's package insert, accredited method, national/international guidelines), not the memory of the AI. Use AI to interpret this data, not to remember it.
- Recalculate/compare: Independently check each numerical result, unit and delta change that the AI returns. In particular, test unit consistency (mg/dL ↔ mmol/L) and agreement with previous result.
- Clinical and analytical filter: Test with an expert eye to see if the output conflicts with the patient's medical condition, medications, sample quality and QC status.
Attention: Copying a result interpretation produced by the AI to the patient report without verifying it is like giving an unsigned result report in terms of your professional responsibility. The comment is not correct just because it came out fluent.
Privacy: patient data is private data
Patient laboratory data (test results, diagnosis, protocol number, identification information) are sensitive personal data and are protected under KVKK (Personal Data Protection Law) in Türkiye and GDPR in Europe. It is a serious violation to paste the patient's name, TR ID number, protocol number and diagnosis into a publicly available AI tool. The rule is simple: anonymize the data. Instead of "Ayşe Yılmaz, 68 years old, protocol 2024-114523, chronic kidney disease", write "68-year-old female patient, known renal failure"; Remove the name, contact information, institution and protocol number. 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 biochemistry specialist wants a clear outline of information to be given to the patient for 8 abnormal parameters in a patient report. The AI produces a plain sketch from anonymized values (no names). The specialist compares the reference ranges with the values of his own laboratory, removes sentences implying "definitive diagnosis" and adds the note "consult your physician for evaluation". Duration: 4 minutes instead of 15 minutes. AI gave the draft, the responsibility remained with the expert.
Case 2 — Unverified comment trap. Another specialist has YZ interpret a TSH result (thyroid stimulating hormone). The AI tells us that the result is “normal” relative to the adult reference range; but the sample is from a newborn and the newborn reference range is completely different. The comment is incorrect because it was made for the wrong age group. Validation was omitted, clinical context was missed.
Case 3 — Breach of confidentiality. A lab technician loads the patient's full name and report PDF into a public AI tool and says "comment." The data went to an external server. The institution faces a KVKK review. The right way was to anonymize the data and share only the necessary numerical values, anonymously.
Weak prompt / Strong prompt
Weak prompt:
Interpret the analysis results of this patient: Ayşe Yılmaz, TC 123..., protocol 2024-114523. Creatinine 2.1, urea 90, potassium 5.8.
This request is wrong in three aspects: identity information is shared (KVKK violation), units and reference range are not given, and there is no clinical context (age, gender, diagnosis). AI fills in the gaps with guesswork and there is a risk of misinterpretation.
Powerful prompt:
Your role: DRAFT preparation assistant to the laboratory specialist. Diagnosis, clinical decision making. DRAFT a simple explanation to the patient for the anonymous results below; implying a definitive diagnosis; Add note "consult your physician". Mark where you are unsure [for expert confirmation]. Patient: 68-year-old woman, known chronic kidney disease. Results (with our unit and laboratory reference): Creatinine 2.1 mg/dL (ref 0.5-0.9), Urea 90 mg/dL (ref 17-43), Potassium 5.8 mmol/L (ref 3.5-5.1).
The strong prompt is anonymous, defines the role and boundary, gives unit and reference, provides clinical context, and requests a sign of verification.
Copiable prompt templates
ROLE AND BOUNDARY DESCRIPTION TEMPLATEYour role: DRAFT preparing assistant to a laboratory specialist. You are not an expert; making a diagnosis, making a decision to release the result, giving clinical advice. Final approval belongs to the expert. If you are not sure, mark it as "[let the expert verify]", do not make it up. Task: [write task].
ANONYMIZATION CONTROL TEMPLATEExtract name, TR ID, protocol/file number, contact and institution information from the text below; replace with "[removed]". Leave only clinically/analytically necessary information. Leave no clues of identity in the output.Text: [paste text]
VALIDATION CHECK TEMPLATEFor each result interpretation you produce, indicate: (1) what reference range you assumed, (2) whether it is unit consistent, and (3) where the interpretation depends on the clinical context. Use "possible" language instead of absolutes.
RISK LEVEL SORTING TEMPLATE Place the laboratory task I will give you into one of three categories and write a rationale: (A) low risk - AI outline is sufficient, (B) medium risk - expert verification, (C) high/very high risk - diagnosis/decision belongs to the expert, AI is only auxiliary. Task: [write task].
Common mistakes
- Mistaking AI for an "expert". AI speaks fluently but has no responsibility; The signature is yours. The output is a draft, not a decision.
- Pasting the credential as is. Name, TR ID number and protocol number are special data; Sharing without anonymization is a violation of KVKK.
- Not questioning the reference range. AI gives a general range; The range of your device and age/gender group may be different.
- Not giving clinical context. Interpretation without age, gender, diagnosis, medication and sample quality is misleading.
- Letting go because “it looks right.” Fluency is not accuracy; Each output requires independent control.
Tip: Before you start working with AI, ask yourself one question: “What happens to the patient if this output is wrong?” If the answer is serious, use AI only for draft/qualification and never skip verification.
In summary
Artificial intelligence is a powerful assistant in the medical laboratory: it speeds up repetitive tasks, generates blueprints, scans patterns. But since you are working in a safety-critical area, the decision to release the result, clinical interpretation and diagnosis rests with the qualified specialist. The role of AI in each phase of the total testing process (pre-analytical, analytical, post-analytical) varies depending on the level of risk; As the risk increases, human approval grows. Three core disciplines guard each step: source, recalculate/compare, clinical-analytical filter. And confidentiality underlies everything: patient data does not enter any tool without being anonymized.
Application task
Select three different tasks from your own laboratory (or from an example scenario): one low risk (e.g. workload summary), one medium risk (e.g. result interpretation draft), one high risk (e.g. a critical value statement). 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. 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) of the task.
- [ ] I limited the AI's role to "assistant/draft/qualifier"; diagnosis/decision rests with the specialist.
- [ ] I anonymized the data; Name, TR ID, protocol number are output.
- [ ] I verified the reference range and unit with my own laboratory's values.
- [ ] I added the clinical context (age, gender, diagnosis, sample quality) to the prompt.
- [ ] I subjected the output to independent checks before releasing it.