Gains:
- Being able to distinguish where in the audiology workflow (scanning, diagnostic testing, device fitting, follow-up) artificial intelligence saves real time and where safety-critical decisions (diagnosis, device prescription, fitting approval) 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 connecting it to the source, comparing it with calibrated device data, and passing it through clinical filtering.
- Anonymizing patient and device data within the scope of KVKK/privacy and gaining the habit of choosing a safe vehicle
Audiology is both a technical and deeply human profession that deals with hearing and balance health. An audiologist (a healthcare professional who evaluates hearing and balance disorders and applies hearing aids) reads a newborn's screening result, interprets a retiree's audiogram (a graph showing hearing thresholds on a frequency-intensity plane), programs a teenager's hearing aid, calms an anxious relative, and writes a clinical report, all on the same day. Some of this work is related to organizing information, producing and remembering text; Some of them are clinical decisions that directly affect health and have a low margin of error. Artificial intelligence (AI for short; computer systems that have been trained with large amounts of text and can write and respond in human language) becomes a powerful aid in your audiology practice when you understand this distinction well: it speeds up the work of text and information, but never replaces clinical judgment.
In this first unit, we establish the foundation of where you can safely use artificial intelligence in the audiology workflow, where you should stop, and how to verify each output. The main principle we will repeat throughout the module is: AI is an assistant; It produces drafts, reminds, simplifies, and previews. Diagnosis, device prescription, fitting decision and clinical interpretation are the responsibility of the audiologist and, when necessary, the physician. An unverified AI output is like an unsigned clinical report: it has no binding force and cannot be used on its own.
What exactly can artificial intelligence do in audiology
Let's divide the practice of audiology into three layers and clarify the role of artificial intelligence in each layer. This distinction is the answer to the question "where should I use it, where should I stand?"
1. Information and text layer (AI is strong here). Hearing aid user guide to be given to the patient, plain language training brochure, appointment reminder message, guide summary, formatting of the report draft, list of frequently asked questions. Artificial intelligence saves you minutes in these tasks because editing, simplifying and formatting the text is its strongest area.
2. Thinking and organization layer (AI helps, decision is yours). Collecting a patient's history, listing anamnesis questions to be asked, reminding possible evaluation topics, structuring a case, prioritizing what needs to be followed up in screening lists. Here, the AI works like a checklist — it can remind you of a title you forgot — but you decide what information applies to this patient.
3. Clinical decision layer (AI cannot make decisions, it can only support). Diagnosis of type and degree of hearing loss, device prescription, fitting (programming) approval, emergency referral decision such as sudden hearing loss, referral to ENT, assessment of "is this measurement reliable?" These decisions belong to the competent specialist — the audiologist and the attending physician. AI can be at most a reminder; He can never make the decision himself.
Attention: Artificial intelligence creates fluent and confident sentences. This fluency is not a guarantee of accuracy. In a safety-critical field like healthcare, “sounds right” and “is right” are two completely separate things.
Mapping by mission risk level
The table below shows how much you'll free up the AI on which task. The golden rule in audiology is this: the higher the risk, the higher the verification threshold, the lower the autonomy of the AI.
Quest
Risk level
AI role
verification
Writing a brochure/message draft
low
Manufacturer
Language and fact check
List of anamnesis questions
low-medium
reminder
clinical strainer
Audiogram pre-interpretation draft
medium-high
sketch generator
Confirmation by calibrated measurement + expert
Scan list prioritization
medium-high
Pre-screener
False negative surveillance
Device prescription / fitting decision
high
Support only
REM + specialist + physician if necessary
Sudden hearing loss referral decision
critical
Just a reminder
Expert/physician decision is mandatory
Why verification is essential: hallucination and obsolescence
It is dangerous to use artificial intelligence in healthcare without knowing its two main weaknesses.
The first is hallucination: the model confidently fabricates information that does not actually exist (a threshold value, a test result, a guide substance, a device feature). Instead of saying "I don't know," the model may produce a plausible but incorrect answer. In audiology, this means that a statement "acoustic reflex found" that was not given is leaked into the report. The second is obsolescence: the model's knowledge is frozen at the time it was trained; Current device models, prescription formulas, screening protocols or legislation may have changed.
The verification discipline is three steps: (1) Link to the source — check what calibrated measurement or current guidance the information is based on. (2) Recalculate/check — verify if the given threshold, classification, gain value actually agrees with the device data. (3) Go through a clinical filter — evaluate with your professional knowledge whether this information is appropriate for the patient in front of you, his ear anatomy and history.
Privacy: the patient's data belongs to the patient
Audiology data are sensitive: degree of hearing loss, device use, developmental status of the child, occupational noise exposure, tinnitus complaint. Within the scope of KVKK (Personal Data Protection Law; the law regulating personal data processing in Türkiye), health data is considered special and requires the highest level of protection. The real name for a general-purpose artificial intelligence tool is T.C. ID number, address, phone number or identifying details are not entered. Instead, anonymize the data: An anonymous description such as “6-year-old boy, moderate loss of right ear” is clinically sufficient and does not reveal anyone.
Tip: When prompting a patient, ask yourself: “If this text were leaked as a screenshot, would this person be recognized?” If the answer is "yes", anonymization is missing.
Step by step: safe use of artificial intelligence in audiology
- Classify the task. Is this a text/information task (AI strong), or clinical judgment (AI support only)? If the risk is high, increase the verification threshold.
- Anonymize data. Establish a clinically adequate description without identifying information.
- Specify role and boundary. Tell the AI limits from the beginning, such as "don't make a diagnosis, use possible language, state that the decision is up to the audiologist."
- Validate the output in three steps. Connect to source/calibrated measurement, recheck, clinical filter.
- Get expert approval. Before the clinical content reaches the patient, it must be approved by the audiologist/physician.
- Save your trace. Note which tool you use for which purpose; It is important for accountability.
Weak prompt / Strong prompt
Weak prompt:
Ahmet Yıldız is 62 years old, he cannot hear well in his right ear. What type of hearing loss, which device should I prescribe?
This request involves a real name (breach of privacy), asks for a direct diagnosis and prescription decision (pushing the AI into the role of expert), and sets no boundaries.
Powerful prompt:
Your role: educational and reminder assistant to an audiologist. Diagnosing, prescribing devices; Use the language "possible" and state that the decision is up to the audiologist/physician. Situation (anonymous): 62-year-old patient, complaining of hearing in the right ear. Task: Remind me as a checklist of the test headings that the audiologist should evaluate in this patient and the anamnesis questions that he should not forget to ask. Emphasize that the decision will be made based on calibrated measurement and clinical evaluation.
The strong will is anonymous, determines the role and the boundary, asks for a reminder, not a decision, and requires verification.
three mini cases
Case 1 — Correct use. When an audiologist prepares a plain-language user brochure for the patient to whom he delivers the device, he asks the artificial intelligence for a draft and does not provide any identification information. He reviews the draft, corrects two exaggerated statements such as "the device completely normalizes hearing" and gives it to the patient. Preparation time reduces from 35 minutes to 10 minutes; The content has been verified.
Case 2 — Hallucination capture. An audiometrist asks the AI about types of tympanometry. AI gives a confident explanation by adding a non-existent "Type D" classification. The specialist compares this with his clinical knowledge and sees that there is no "Type D" outside the standard classification (A, B, C and their subtypes). Lesson: no technical knowledge should be conveyed without corroboration with professional knowledge.
Case 3 — Return from privacy violation. An audiologist is about to hastily type a child's full name and school information into the prompt; the team remembers its rule and anonymizes the text as "5 years old, suspected slight loss of both ears." The clinical outcome does not change, the person is protected.
Copiable prompt templates
TASK CLASSIFICATION TEMPLATEEvaluate the following task: [task].1) Is this a text/information task or a clinical decision?2) If it involves a clinical decision, which part is left to the audiologist/physician?3) What is the part that the artificial intelligence can do safely?Give the answer in a table.
ANONYMIZATION CHECK TEMPLATEMark every element that could identify you in the text below (name, ID, address, telephone, full date of birth, school/workplace name) and suggest an anonymous equivalent. Text: [text]
VERIFICATION TEMPLATEI will evaluate the following artificial intelligence output under three headings:Output: [output]1) On what caliber measurement/guideline should this information be based? (link to source)2) Which threshold/value/classification should be manually checked? (recalculate)3) Is this information appropriate for this patient? (clinical filter) [patient description]
ROLE AND BOUNDARY TEMPLATEYour role: assistant to an audiologist. Rules: making a diagnosis, prescribing a device, making a referral decision; Use the language “possible/must be confirmed by calibrated measurement”; add the note “the audiologist/physician's decision, verify by calibrating” at the end of each answer. Quest: [quest].
Common mistakes
- Delegating clinical decision making to artificial intelligence. "Should this patient use a device?" The answer to questions like these lies not in AI, but in measurement and expert evaluation.
- Using without verification. Thinking the fluent answer is correct; not confirming threshold, classification and device value.
- Entering real credentials. Name, phone number, T.R. Violating KVKK by typing.
- Forgetting about getting old. Substituting the model's information for the current device/protocol.
- Asking without setting limits. Releasing AI without instructing on role and boundary invites the language of diagnosis and precision.
In summary
Artificial intelligence is a powerful assistant that accelerates text and information work in audiology and previews screening lists; However, diagnosis, device prescription, fitting decision and clinical interpretation always belong to the audiologist and physician. Each output must be validated in three steps—link to source/measurement, recheck, clinical filter—patient data must be anonymized, and clinical content must undergo expert approval. Unverified output is an unsigned report.
Application task
Think about an actual week from your own practice and place your work in a three-column table: “text/information work,” “organization,” “clinical decision.” For each line, write the role of the AI (strong / helpful / support only). Then use the “Role and boundary” template from this unit to produce a draft patient education text, apply the three-step verification, and note what you corrected.
checklist
- [ ] I classified the task as text/organization/clinical decision.
- [ ] I anonymized the patient data so that the identity is not revealed.
- [ ] I gave role and boundary instructions to the artificial intelligence.
- [ ] I connected the output to the source/measurement, rechecked it and passed it through the clinical filter.
- [ ] I submitted the clinical content to the audiologist/physician for approval.
- [ ] I recorded which tool I used for which purpose.