PP138
A COMPARATIVE EVALUATION OF AI-BASED RECOMMENDATIONS AND PERSONALIZED NUTRITION IN RENAL DISEASES
R. Mamellapalli1,*, D. N. Gajjala2
1DIETETICS, 2NEPHROLOGY, KRISHNA INSTITUTE OF MEDICAL SCIENCES (KIMS, SECUNDERABAD), HYDERABAD, India
Rationale: This study seeks to compare AI based(chat gpt) dietary recommendations with personalized nutrition supervised by a nutrition professional to assess their efficacy and investigate potential methods for integrating both approaches to improve renal patient care.
Methods: A comparative study was conducted among 200 patients diagnosed with renal disease (stage 3 to 5) for a period of 3 months divided into 2 groups each .Parameters such as age, gender, biochemical parameters including serum creatinine, urea, and electrolyte levels ,nutritive value of the diets were analyzed. Data w analysis was done using statistical software such as SPSS. An independent sample t-test was used to compare mean differences between the two approaches.
Results: Protein intake was adequate in personalized nutrition group 88%, whereas in AI based nutrition group it was 72%.Significant weight changes and muscle loss were observed in AI based diet group.Personalized group adherence to dietary guidelines and dietary compliance was 83% and 85% respectively and AI based dietary recommendations and dietary compliance was 67% and 62% respectively.In Personalized dietary approach group and AI group ,Creatinine 3.4 to 2.8 and 3.6 to 3.1 , Blood urea 71 to 57 and 66 to 61, Potassium 5.4 to 4.7 and 5.3 to 5.0 respectively were reduced .Improvement in albumin was seen in dietary approach group 3.4 to 3.9 compared to AI diet group 3.3 to 3.6.
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Conclusion: Personalized nutrition approach had a better diet adherence, improved various biochemical parameters meeting their nutritional goals.AI based dietary recommendations helped in screening,quantity restriction, nutrition education.But the challenges were due to the adherence.Integrating both personalised and AI as a supportive tool can help reach the patient expectations saving time and better decision making in a multidisciplinary team by reducing the workload.
Disclosure of Interest: None declared