Large Language Models Are Reshaping Patient Data Management and Clinical Practice in Nuclear Medicine

LLMs represent a transformative evolution from traditional NLP approaches in nuclear medicine, offering unprecedented capabilities that extend far beyond the rule-based and statistical methods previously employed in the field. Current research demonstrates their superior utility in managing complex medical information, supporting clinical decisions, assisting with multimodal imaging tasks, and enhancing medical education. However, this technological advancement comes with critical challenges that were less prominent in earlier NLP systems, including reliability concerns (hallucinations), the need for specialized domain knowledge, multimodal data integration complexities, standardized evaluation frameworks, and heightened ethical and privacy considerations. The transition from traditional NLP to LLMs highlights the promise of more advanced language understanding and generation capabilities, offering opportunities to enhance personalized and efficient health care in nuclear medicine and theranostics. At the same time, these models demand rigorous validation, careful domain adaptation, and responsible implementation strategies to ensure patient safety and clinical reliability. By addressing these challenges through targeted research and clinical validation, LLMs can fulfill their potential as transformative tools in specialized medical practice.