AI-Generated Images for Speech Pathology—An Exploratory Application to Aphasia Assessment and Intervention Materials

Abstract

Purpose: Images are a core component of aphasia assessment and intervention that require significant resources to produce or source. Text-to-image generation is an Artificial Intelligence (AI) technology that has recently made significant advances and could be a source of low-cost, highly customizable images. The aim of this study was to explore the potential of AI image generation for use in aphasia by examining its efficiency and cost during generation of typical images. Method: Two hundred targets (80 nouns, 80 verbs, and 40 sentences) were selected at random from existing aphasia assessments and treatment software. A widely known image generator, DALL-E 2, was given text prompts for each target. The success rate, number of prompts required, and costs were summarized across target categories (noun/verb/sentence) and compared to frequency and imageability. Results: Of 200 targets, 189 (94.5%) successfully conveyed the key concept. The process took a mean of 2.3 min per target at a cost of $0.31 in U.S. dollars each. However, there were aesthetic flaws in many successful images that could impact their utility. Noun images were generated with the highest efficiency and accuracy, followed by verbs, while sentences were more challenging, particularly those with unusual scenes. Patterns of flaws and errors in image generation are discussed. Conclusion: The ability to rapidly generate low-cost, high-quality images using AI is likely to be a major contribution to aphasia assessment and treatment going forward, particularly as advances in this technology continue.