This chapter occupies a rare, interdisciplinary niche at the intersection of three rapidly evolving fields: oncology psychology, generative AI, and visual semiotics<\/strong>. Its uniqueness can be summarized in four key points:<\/p>\n Pioneering a New Subdomain<\/strong> Focus on the \u201cSelf-Psychology\u201d of Cancer Patients<\/strong> Chatbots as Image Generators, Not Just Conversational Agents<\/strong> Integration with Traditional Psychotherapy<\/strong> The benefits extend across research, clinical practice, and patient well\u2011being.<\/p>\n\n
While AI image generation (e.g., DALL\u00b7E, Midjourney, Bing Image Creator) has been explored in art therapy and general mental health, its specific application to cancer psychology<\/strong> is almost nonexistent in the literature. The chapter explicitly acknowledges that \u201cthere are few studies on this topic,\u201d which positions it as an early\u2014if not the first\u2014systematic attempt to bridge this gap. This timeliness is a major scholarly contribution.<\/p>\n<\/li>\n
Cancer patients frequently experience depression, loss of agency, and identity fragmentation. The chapter uniquely targets self-psychology<\/strong>\u2014how patients perceive themselves, their bodies, and their futures. By linking image generation to the reconstruction of the damaged self, it moves beyond generic \u201cart therapy\u201d into a theoretically grounded psychological intervention.<\/p>\n<\/li>\n
Most research on chatbots in oncology focuses on text\u2011based support (e.g., Woebot, Replika). This chapter reframes chatbots as visual co\u2011creators<\/strong>, where the patient\u2019s verbal prompts are transformed into externalized images. This is a fundamentally different mechanism: images can bypass verbal defense mechanisms and access pre\u2011symbolic or emotional content that talk therapy alone may miss.<\/p>\n<\/li>\n
The chapter does not propose AI as a replacement for psychotherapy but as a supplementary tool<\/strong>. This balanced, non\u2011techno\u2011solutionist stance is rare in early AI research, which often overpromises. The emphasis on \u201calongside traditional methods\u201d gives the work clinical credibility and practical applicability.<\/p>\n<\/li>\n<\/ul>\n2. Benefits of the Chapter<\/h3>\n