ROLE OF ARTIFICIAL INTELLIGENCE IN DYNAMIC POST-SURGICAL NUTRITIONAL ADJUSTMENTS FOR BARIATRIC SURGERY: A LITERATURE REVIEW sariyak2003@gmail.com

 

ROLE OF ARTIFICIAL INTELLIGENCE IN DYNAMIC POST-SURGICAL NUTRITIONAL ADJUSTMENTS FOR BARIATRIC SURGERY: A LITERATURE REVIEW

4

Saudi Arabia

Oral or Poster

Bariatric surgery (BS) is an extremely effective treatment for morbid obesity but tends to cause nutrient deficiencies and various post-operative results. Standard nutritional management seldom takes individual variations into account. The integration of Artificial Intelligence (AI) holds a possible solution for dynamic and customized nutritional adjustment following BS.

Narrative literature review was conducted on PubMed, ScienceDirect, Embase, and Google Scholar (2009–2025). Included were studies examining the use of AI in nutritional or postoperative BS management. There were sixty-seven eligible articles.

AI promised to predict post-BS outcomes such as weight loss trends, nutritional deficiency, and patient compliance. Machine learning (ML) models such as CNNs and ANNs exhibited predictive accuracies with AUCs often being above 0.80. Real-time monitoring from wearable device and AI-based personalized meal planning showed promising trends towards improvement. Ethical concerns, non-generalizability, and insufficient clinical integration remain important limitations.

AI has revolutionary potential in post-BS nutritional management with early intervention, customization of diet regimes, and improvement in long-term results. However, additional clinical validation, ethical standards, and patient-centered designs are needed before AI can be integrated completely into standard practice.