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The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation

Marvin Kopka^1,2, Azeem Majeed³, Gabriella Spinelli⁴

¹Division of Ergonomics, Department of Psychology and Ergonomics (IPA), Technische Universität Berlin, Berlin, Germany.

PLOS Digital Health

|July 2, 2026

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This study introduces the Trust in AI-Generated Health Advice (TAIGHA) scale, a new tool to measure user trust in AI health advice. TAIGHA and its short form demonstrate strong validity and reliability for assessing trust and distrust in AI-driven health information.

Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Psychometrics

Background:

Artificial Intelligence (AI) tools, including large language models (LLMs), are increasingly used for health information seeking and decision-making.
Existing trust measures are generic and do not specifically assess user trust in AI-generated health advice, highlighting a critical research gap.
Accurate measurement of trust in AI health advice is crucial due to its clinical and safety implications.

Purpose of the Study:

To develop and validate the Trust in AI-Generated Health Advice (TAIGHA) scale and its short form (TAIGHA-S).
To create theory-based instruments for measuring state trust and distrust in AI-generated health advice.
To provide a validated alternative to generic trust scales and self-developed one-item measures.

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Main Methods:

A generative AI approach was used to create candidate items based on cognitive and affective components of trust theory.
Content, face, and psychometric validation were conducted with domain experts, lay participants, and 385 UK participants.
Confirmatory Factor Analysis (CFA) and reliability analyses (Cronbach's alpha, McDonald's omega) were performed on the developed scales.

Main Results:

The TAIGHA scale demonstrated excellent content (S-CVI/Ave=0.99) and face validity (S-FVI/Ave=0.99).
CFA confirmed a robust two-factor model with excellent fit indices (CFI=0.98, TLI=0.98, SRMR=0.03).
Both TAIGHA and TAIGHA-S exhibited high internal consistency and reliability, with strong correlations between the full and short forms.

Conclusions:

The TAIGHA and TAIGHA-S are validated instruments with excellent psychometric properties for assessing state trust and distrust in AI-generated health advice.
These scales offer a reliable and valid method for measuring user trust in AI health information, outperforming general trust scales in predicting reliance.
The developed instruments are essential for understanding user interactions with AI in healthcare and ensuring safe and effective implementation.