Trust and accuracy in AI: Optometrists favor multimodal AI systems over unimodal for glaucoma diagnosis in collaborative environment

Abstract

Background:

User trust and decision accuracy are crucial for the successful collaboration of humans and Artificial Intelligence (AI) systems, especially in clinical settings such as glaucoma diagnosis. Both trust and accuracy are influenced by the data modality used by AI systems, which directly impacts the effectiveness of human-AI collaboration.

Objective:

The objective of this study is to discover the modality of an AI system that aligns best with an optometrist’s mental model. This was achieved by comparing trust levels between unimodal and multimodal AI systems used for glaucoma diagnosis. Additionally, we explore the impact of modality on various targets of user trust and user performance.

Methods:

We conducted a within-subject study with 20 optometrists, who were presented with both unimodal and multimodal AI mock-up systems specifically designed for glaucoma diagnosis. Trust was measured across five key targets using a 5 point Likert scale questionnaires. Statistical analysis was performed to assess trust differences between the unimodal and multimodal AI systems. Optometrist performance was evaluated based on the alignment of their decisions with those of the unimodal and multimodal AI systems.

Results:

The results showed that the multimodal system had a higher average trust rating of 3.98 on a Likert scale, indicating greater trust compared to the unimodal system, which had an average trust rating of 3.00. This difference was statistically significant (p$<$.001), with further analysis revealing significant variation across all trust targets (p$<$.001). Additionally, optometrists demonstrated higher F1 scores with the multimodal system (43.1%) compared to the unimodal system (37.3%), while accuracy remained comparable between the two systems (63.0% for multimodal and 63.3% for unimodal). However, major differences across these metrics were observed in relation to optometrist’s expertise.

Conclusions:

Multimodal AI systems, which use the same data modality as optometrists and align more closely with their mental models and decision-making processes, result in higher user trust and improved diagnostic performance. Therefore, for effective human-AI collaboration in healthcare, specifically for glaucoma diagnosis, AI systems should be designed to utilize the same data sources as the human counterparts, ensuring consistency and improving both trust and decision accuracy.