Sep 2026· Metaverse Science, Society and Law· 0 citations
TL;DR
This paper outlines a systematic framework designed to integrate generative AI modalities into the field of restorative tattoo art, specifically targeting psychological and somatic rehabilitation post-oncological disease.
Abstract
The rapid evolution of intelligent systems prompts a significant paradigm shift from purely computational optimization toward active mediation within highly subjective, delicate, and deeply personal human domains. Within the conceptual frameworks of Human-Centered Artificial Intelligence (HCAI) and Affective Computing, these intelligent architectures are increasingly recognized as dynamic digital intermediaries. This paper outlines a systematic framework designed to integrate generative AI modalities into the field of restorative tattoo art, specifically targeting psychological and somatic rehabilitation post-oncological disease. By combining multimodal large language models (LLMs) with advanced affective computing configurations, the proposed methodology resolves the "verbalization bottleneck" inherent in conventional trauma processing. The system maps uncodified emotional inputs into precise visual parameters, thereby facilitating post-traumatic growth, somatic reclamation, and clinical patient autonomy.
The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.
Clinical nurses' GenAI learning needs are currently oriented toward practical, application-focused skills, and curriculum development may benefit from a phased approach that prioritizes high-impact practical skills while progressively incorporating foundational, ethical, and advanced competencies.
Yeru Xia, Jingbang Liu, Kaili Wang et al.· Nurse Education Today· 1 citation· ⚡1
Nursing education must respond proactively by establishing AI literacy frameworks, revising academic integrity policies, and embedding source verification and citation skills into curricula, as generative AI threatens to erode the scholarly standards essential to both academic rigor and professional nursing practice.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.