Generative AI and Large Language Models for Collaborative Innovations Across Domains

Generative Artificial Intelligence (GAI) is profoundly transforming collaboration across diverse domains by enhancing creativity, decision-making, and human-AI co-creation. This review synthesizes current research on GAI’s integration within knowledge management, environmental monitoring, healthcare, education, and business sectors. It examines key ethical, technical, and psychological challenges, including transparency, alignment, bias, privacy, and workforce disruption. Large Language Models (LLMs), central to GAI advancements, have revolutionized natural language processing but raise concerns around interpretability, trust, and societal impact. This review explores explainability methods, human-aligned training, and fine-tuning strategies that improve reliability and user acceptance. The emergence of LLM-based autonomous agents and multi-agent collaborative frameworks signifies a critical leap toward adaptable, goal-driven AI systems approaching Artificial General Intelligence, highlighting scalability and ethical governance challenges. In healthcare, GAI applications such as ChatGPT and Med-PaLM enhance diagnostics, education, and clinical support, yet demand rigorous ethical oversight and regulatory frameworks to safeguard trust and patient safety. Explainable AI (XAI) further bridges the gap between model complexity and clinical transparency, promoting accountable AI adoption. This review advocates for human-centered, interdisciplinary approaches and scalable, responsible frameworks that balance innovation with ethical stewardship. Future research directions emphasize developing transparent, inclusive, and privacy-preserving GAI systems that maximize societal benefits while mitigating risks across sectors.