trust on Anne-Kathrin Kleine

Does it matter how your doctor uses AI? Patient trust in AI-supported medical decisions

Fri, 21 Aug 2026 00:00:00 +0000

More and more physicians make decisions together with AI-based clinical decision support. But how do patients feel about such hybrid decisions? In this project led by Insa Schaffernak, we examined whether the type of AI support and its timing in the decision process affect patients’ trust.

What we did

In two preregistered vignette experiments, 489 and 570 participants from Germany imagined four eye-care consultations, co-developed with ophthalmologists. The physician used no AI, descriptive AI (which highlights features in retinal images), or diagnostic AI (which additionally suggests a preliminary diagnosis). Study 2 also varied whether the physician looked at the AI output after an independent own assessment (sequential) or at the same time as the medical information (concurrent). We also analyzed more than 2,600 open-ended responses.

What we found

Diagnostic AI support led to lower trust in the medical decision and in the physician, and to a stronger feeling that the patient’s individual situation was neglected, compared with descriptive AI support. Participants’ willingness to follow the advice did not differ.
Study 2 replicated these results – and showed that timing matters: the disadvantage of diagnostic AI disappeared when the physician first assessed the case independently before consulting the AI.
In their open answers, participants worried that erroneous AI output could bias physicians and wished for AI as an independent second opinion.

Effects of the type (descriptive vs. diagnostic) and timing (concurrent vs. sequential) of AI support on trust in the decision, perceived trustworthiness, uniqueness neglect, and willingness to follow the advice. Figure 5 from Schaffernak et al. (2026), Journal of Medical Internet Research, licensed under CC BY 4.0.

What this means

Patients accept AI support more readily when it is used for analysis rather than for decisions – or when physicians visibly retain their independence. How AI is integrated into the consultation can therefore matter as much as whether it is used at all.

Read the paper

Schaffernak, I., Cecil, J., Kokje, E., Kleine, A.-K., Saad, A., Zemo, F., & Lermer, E. (2026). Effects of type and timing of clinician-facing AI support on patient trust in medical consultations: 2 vignette experiments. Journal of Medical Internet Research, 28, e93172. https://doi.org/10.2196/93172

When do researchers trust AI? Trust, accuracy, and experience in the use of generative AI

Mon, 20 Apr 2026 00:00:00 +0000

Generative AI (GenAI) is increasingly part of academic work – from literature searches to coding and writing. At the same time, its processes are opaque and its outputs can be misleading, so relying on it is inherently risky. How do researchers decide whether to use these tools?

What we did

Building on trust theory and a stimulus–organism–response logic, we tested whether perceived accuracy shapes researchers’ intention to use GenAI via trust, and whether this depends on their experience with GenAI. We conducted two online studies with a total of 402 researchers:

Study 1 (N = 232) used a three-wave longitudinal design with one-week lags, measuring perceived accuracy, trust, and usage intention at separate time points.
Study 2 (N = 170) was a vignette experiment in which we manipulated the accuracy of ChatGPT’s recommendations.

What we found

Researchers who perceived GenAI as more accurate trusted it more, and trust in turn predicted their intention to use it. In the experiment, more accurate recommendations increased trust.
Trust thus explained (mediated) the link between accuracy and usage intention.
Experience was a boundary condition: the more familiar researchers were with GenAI, the weaker the link between trust and usage intention.

What this means

For novices, trust works more like a heuristic – a shortcut for deciding whether to rely on a tool – and it becomes less relevant as experience grows. This suggests that, especially for less experienced users, it matters whether their trust is well-founded: helping researchers evaluate the accuracy of AI output supports calibrated rather than simply high trust.

Read the paper

Kleine, A.-K., Schaffernak, I., & Lermer, E. (2026). When do researchers trust AI? Trust as a mediator and experience as a boundary condition in researchers’ intentions to use generative AI. Computers in Human Behavior: Artificial Humans, 8, 100310. https://doi.org/10.1016/j.chbah.2026.100310

Trust in AI: Two talks and a symposium at the DGPs Congress 2024

Mon, 16 Sep 2024 09:00:00 +0200

Symposium (chair)

Navigating Trust in the Digital Realm: Trust in AI and Customer Relations.

Talk 1

Exploring the landscape of AI-based clinical decision support systems in mental healthcare: An analysis of patents and commercial products. Kleine, A.-K., Kokje, E., Cecil, J., Lermer, E., Hummelsberger, P., Heinrich, A., & Gaube, S.

Building on our patent review, we compared what is being patented with what is commercially available. The product analysis has since been published in Artificial Intelligence in Medicine ( summary).

Talk 2

The relationship between performance, trust, and the intention to use generative AI in academia.

How do researchers decide whether to rely on tools such as ChatGPT? This talk examined how the perceived performance of generative AI and trust in it shape researchers’ intentions to use it. Results from this line of research are now published in Computers in Human Behavior: Artificial Humans ( summary).

Contributions by the team

Cecil, J., Kleine, A.-K., Lermer, E., & Gaube, S. Exploring attitudes and adoption intentions of AI-enabled technologies among mental health practitioners.
Gaube, S., Biebl, I., Engelmann, M. K. M., Kleine, A.-K., & Lermer, E. Comparing preferences for skin cancer screening: A conjoint experiment.