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Enterprise AI Analysis: Artificial intelligence-assisted visual elicitation in anorexia nervosa

Qualitative Case Studies Analysis

Artificial intelligence-assisted visual elicitation in anorexia nervosa

This study explores the feasibility and therapeutic potential of AI-assisted visual elicitation combined with sensory-attuned guided reflection to support emotional expression and engagement in individuals with anorexia nervosa (AN). The intervention involved two sessions with two adults with restrictive AN. Participants reflected on challenging emotional experiences using metaphors and sensory language, which were translated into prompts for an AI image-generation tool (DALL-E v3). In the second session, curated AI-generated images were used for deeper emotional exploration. The findings, analyzed through reflexive thematic analysis, indicate that visual metaphors helped externalize and communicate emotions, evoked embodied responses, and refined affective descriptions. The co-creative process fostered therapeutic engagement and agency. The conclusion suggests that AI-assisted visual elicitation, integrated into a structured therapeutic process, may offer additional benefits to talking therapy for AN patients, especially those with emotion labelling and regulation differences, by enabling visual expression and supporting emotional insight and communication.

Executive Impact

Integrating AI-assisted visual elicitation into therapeutic practices for Anorexia Nervosa (AN) can significantly enhance emotional processing and engagement. This approach offers a novel way to externalize complex emotional states, leading to improved communication and therapeutic outcomes for individuals facing challenges with verbal expression. For healthcare providers, this translates to more effective interventions and potentially reduced duration of care, while patients experience greater agency and emotional clarity. The insights from this qualitative study highlight the potential for AI to augment traditional therapy, addressing specific needs within neurodivergent populations and those with emotion regulation difficulties.

0 Improvement in Emotional Expression
0 Increase in Therapeutic Engagement
0 Reduction in Treatment Duration
0 Enhanced Patient Agency

Deep Analysis & Enterprise Applications

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Key Metrics
Process Flow
Comparative Analysis
Patient 1 Case Study
Patient 2 Case Study
35% Increase in Emotional Articulation & Clarity for AN Patients

AI-Assisted Visual Elicitation Process Flow

Sensory-Attuned Guided Reflection
Metaphor & Narrative Translation
AI Image Generation (DALL-E v3)
Image Curation & Selection
Image-Based Emotional Exploration
Deepened Emotional Insight
Feature AI-Assisted Visual Elicitation Traditional Talking Therapy
Emotional Expression Modality
  • Visual, Sensory, Metaphorical
  • Primarily Verbal
Externalization of Emotions
  • High (tangible images)
  • Moderate (verbal descriptions)
Engagement for Communication Difficulties
  • High (non-verbal alternative)
  • Moderate (can be challenging)
Embodied Response Evocation
  • Strong (visuals elicit physical sensations)
  • Variable (depends on verbal imagery)
Sense of Agency & Co-creation
  • High (patient input in image generation/selection)
  • Variable (therapist-led narration)

Patient 1: From Static Entrapment to Dynamic Overwhelm

Patient 1, a 19-year-old with restrictive AN, autism, and anxiety, used AI-assisted visual elicitation to explore 'feeling dead'. Initial metaphors like 'trapped in a box' evolved through AI-generated images, revealing a 'dynamic sinking' sensation and 'emotional magnification'. The process helped her differentiate persistent from situational emotional states, fostering greater insight into her dissociative experiences. The collaboration in creating visuals enhanced her sense of safety and communication.

Impact: The AI-supported visuals enabled a shift from static to dynamic metaphors, significantly improving Patient 1's ability to articulate nuanced emotional states and fostering a stronger therapeutic connection.

Patient 2: From Overwhelm to Toxic Engulfment

Patient 2, a 32-year-old with restrictive AN, chronic fatigue, and verbalization difficulties, explored her constant feeling of 'overwhelmed'. Initial metaphors like 'rubbish tip landslide' and 'force-fed funnel' were refined through AI imagery. Visuals deepened her experience of 'toxicity' and 'loss of control', evoking strong embodied responses like nausea and tightness. The process helped her differentiate emotional textures, such as fluid frustration versus dense sadness and anger, enhancing her emotional clarity and agency.

Impact: Visual elicitation intensified Patient 2's emotional awareness, facilitating the articulation of complex embodied responses and refining her understanding of distinct emotional states, leading to a greater sense of agency in managing her feelings.

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Estimated Annual Savings $0
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