Evidence map›Paper›PMID 41528618›Full record

ArticleEndocrine2026

Assessing the informatics utility of artificial intelligence chatbots for patient and clinician support: the case of adrenal incidentaloma.

Nergis Basmaci, Seren Aksun, Bulent Okan Yildiz

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Article in Endocrine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Nergis BasmaciDivision of Endocrinology and Metabolism, Department of Internal Medicine, Hacettepe University, Ankara, Turkey.ORCID 0009-0000-9730-1472
Seren AksunDivision of Endocrinology and Metabolism, Department of Internal Medicine, Hacettepe University, Ankara, Turkey.ORCID 0000-0001-5503-9381
Bulent Okan YildizDivision of Endocrinology and Metabolism, Department of Internal Medicine, Hacettepe University, Ankara, Turkey. byildiz@hacettepe.edu.tr.ORCID 0000-0003-1797-7662

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe use of artificial intelligence (AI) in clinical medicine is expanding rapidly. Over the past two decades, the incidence of adrenal tumors has increased tenfold, primarily due to the widespread use of imaging, leading to a surge in the detection of adrenal incidentalomas. This comparative study assessed the performance of leading AI chatbots in addressing patient and physician oriented questions on adrenal incidentalomas, focusing on accuracy, quality, hallucination tendency, and readability.

methodsIn this study, the performance of four AI chatbots, ChatGPT 4.0, Gemini 2.0 Flash, DeepSeek, and Perplexity, was evaluated. A total of 35 questions, categorized into “patient education” and “physician support,” were submitted to each chatbot. Responses were independently assessed for accuracy, quality, and hallucination tendency using a Likert scale. Readability was analyzed using standard metrics.

resultsDeepSeek demonstrated the highest performance in both accuracy and response quality, outperforming the other chatbots (p < 0.001). ChatGPT 4.0 performed better than both Gemini 2.0 Flash and Perplexity (p < 0.001), while no significant difference was observed between Gemini 2.0 Flash and Perplexity. Perplexity exhibited a higher tendency to hallucinate compared to the other models (p < 0.001). In terms of readability, Perplexity was the least readable model, whereas ChatGPT 4.0 achieved highest readability scores based on both FRE (Flesch Reading Ease) and FKGL (Flesch-Kincaid Grade Level) metrics.

conclusionAI chatbots can serve as valuable tools for disseminating information about adrenal incidentalomas, particularly in the context of patient education. Among the evaluated models, DeepSeek tended to provide comparatively higher accuracy and quality, with a lower tendency to hallucinate. In contrast, Perplexity demonstrated comparatively lower performance across multiple metrics.

Indexed as

Adrenal Gland NeoplasmsArtificial IntelligencePatient Education as TopicHumansAdrenal incidentalomaAI chatbotsArtificial intelligence (AI)Clinical decision supportDeep learning in medicineNatural language processing (NLP)

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.