Summary: This article reviews AI enabled nutritional risk screening with an academic and clinically oriented perspective. It synthesizes predictive models, dietary intake extraction from notes, and workflow integration for nursing assessment and intervention.

Malnutrition and risk of deterioration are common in hospitalized and long term care patients. Traditional screening tools are periodic and may miss dynamic declines. AI approaches combine EHR data, nursing intake notes, and bedside measurements to provide continuous risk estimates and to prioritize dietitian referrals.

Technically systems use NLP to extract dietary intake and swallowing concerns from free text, combine with weight trends laboratory markers and comorbidity indices, and apply gradient boosted trees or logistic models for risk prediction. Validation requires external cohorts, prospective pilots, and measurement of outcomes such as nutritional status, length of stay, and readmissions. Implementation challenges include documentation variability, integration with nutrition services, and patient acceptability.

Guidance: For nursing teams the guidance is: select validated screening instruments as anchors; augment with NLP extraction of intake and swallowing notes; pilot silent prospective validation; co design referral thresholds with dietitians; monitor process metrics and equity across age and language groups.

Conclusion: AI nutritional screening can enable earlier detection and targeted interventions when models are validated and integrated into nurse led workflows with dietitian collaboration.

Final Summary: EHR + NLP extraction; temporal weight trend modeling; dietitian referral thresholds; silent pilots; equity monitoring

Useful Facts: nutrition | screening | clinical informatics

Related Topics: nursing;nutrition NLP extracts intake details from notes; Weight trends predict malnutrition risk; External validation ensures generalizability; Co design with dietitians improves actionability; Equity audits prevent biased referrals