Comparative Evaluation of ChatGPT and Gemini in Approximating Clinically Confirmed Diagnoses From Structured Numerical Pure-Tone Audiogram Data

The utilization of large language models (LLMs) in healthcare shows promising potential for addressing the global shortage of audiologists. However, their effectiveness in understanding and interpreting complex, live, real-world structured audiological data requires further investigation, as previous studies have primarily focused on theoretical situations and scenarios....

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✦ The floor

Discussion

Signed responses from readers of the wire.

✦ Clinical Takeaway ✦

Neither ChatGPT nor Gemini should be used as a standalone diagnostic tool for audiological interpretation; clinicians should treat current AI outputs as a preliminary, unvalidated support layer only until prospective clinical validation is available.

✦ Why It Matters ✦

As AI tools increasingly enter clinical workflows, understanding the diagnostic accuracy of large language models on audiogram data is critical for setting safe boundaries around their use in audiology practice.

✦ Key Points ✦

01ChatGPT and Gemini were both evaluated on their ability to interpret structured pure-tone audiogram numerical data.
02Performance was benchmarked against clinically confirmed audiological diagnoses.
03The study highlights potential and limitations of LLMs as decision-support tools in audiology.
04Neither model was designed or validated specifically for audiological diagnosis.
05Findings have implications for AI integration policy in hearing clinics.

✦ Claims & Evidence ✦

ClaimEvidenceSupport

ChatGPT and Gemini can approximate clinically confirmed audiological diagnoses from structured pure-tone audiogram data.

studypartially supported

Large language models can process structured numerical audiogram data to produce diagnostic outputs.

studysupported

✦ Research metadata ✦

PMID: 42473498
DOI: 10.7759/cureus.111153.
Journal: Cureus
Publication type: research_article
Evidence level: 4
Population: Structured pure-tone audiogram datasets with clinically confirmed audiological diagnoses
Intervention: Diagnostic interpretation of pure-tone audiogram data by ChatGPT and Gemini large language models
Comparator: Clinically confirmed audiological diagnoses by human specialists

Primary outcomes

Agreement between AI-generated diagnoses and clinician-confirmed audiological diagnoses; Comparative diagnostic accuracy of ChatGPT versus Gemini

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