The Artificial Intelligence in Clinical Nutrition: current applications and prospects

The integration of Artificial Intelligence (AI) into clinical nutrition represents a transformative shift in managing and assessing dietary practices. This research explores AI's multifaceted role in clinical nutrition, focusing on its potential to enhance dietary assessments, personalize nutritional recommendations, and refine clinical decision-making processes in the context of global noncommunicable diseases. We conducted a comprehensive review of literature spanning from 2019 to 2024, utilizing electronic bibliographic databases such as PubMed and Google Scholar. Keywords related to "artificial intelligence," "clinical nutrition," and "personalized medicine" were used to retrieve relevant studies. A total of 216 records were identified, and after meticulous screening based on relevance and quality, 80 papers were included for detailed analysis. AI technologies, notably machine learning and deep learning, are shown to facilitate the analysis of complex datasets, improving predictions of health outcomes and optimizing nutritional interventions. These technologies support a shift towards personalized medicine and enable healthcare providers to deliver more precise and effective patient care. AI in clinical nutrition offers promising enhancements to the accuracy of nutritional evaluations and the personalization of dietary recommendations. While the potential for AI to revolutionize clinical nutrition is significant, challenges such as data privacy, algorithmic bias, and the need for professional oversight must be carefully managed. Looking forward, the role of AI in clinical nutrition is set to expand into areas like real-time dietary monitoring and virtual health coaching, further integrating AI into preventive healthcare frameworks to improve patient outcomes and quality of life.