Background　&　aims:　Malnutrition　is　prevalent　among　hospitalised　patients,　and　increases　the　morbidity,　mortality,　and　medical　costs;　yet　nutritional　assessments　on　admission　are　not　routine.　This　study　assessed　the　clinical　and　economic　benefits　of　using　an　artificial　intelligence　(AI)-based　rapid　nutritional　diagnostic　system　for　routine　nutritional　screening　of　hospitalised　patients.　Methods:　A　nationwide　multicentre　randomised　controlled　trial　was　conducted　at　11　centres　in　10　provinces.　Hospitalised　patients　were　randomised　to　either　receive　an　assessment　using　an　AI-based　rapid　nutritional　diagnostic　system　as　part　of　routine　care　(experimental　group),　or　not　(control　group).　The　overall　medical　resource　costs　were　calculated　for　each　participant　and　a　decision-tree　was　generated　based　on　an　intention-to-treat　analysis　to　analyse　the　cost-effectiveness　of　various　treatment　modalities.　Subgroup　analyses　were　performed　according　to　clinical　characteristics　and　a　probabilistic　sensitivity　analysis　was　performed　to　evaluate　the　influence　of　parameter　variations　on　the　incremental　cost-effectiveness　ratio　(ICER).　Results:　In　total,　5763　patients　participated　in　the　study,　2830　in　the　experimental　arm　and　2933　in　the　control　arm.　The　experimental　arm　had　a　significantly　higher　cure　rate　than　the　control　arm　(23.24%　versus　20.18%;　p　1/4　0.005).　The　experimental　arm　incurred　an　incremental　cost　of　276.52　CNY,　leading　to　an　additional　3.06　cures,　yielding　an　ICER　of　90.37　CNY.　Sensitivity　analysis　revealed　that　the　decisiontree　model　was　relatively　stable.　Conclusion:　The　integration　of　the　AI-based　rapid　nutritional　diagnostic　system　into　routine　inpatient　care　substantially　enhanced　the　cure　rate　among　hospitalised　patients　and　was　cost-effective.　Registration:　NCT04776070　(https://clinicaltrials.gov/study/NCT04776070).　(c)　2024　Elsevier　Ltd　and　European　Society　for　Clinical　Nutrition　and　Metabolism.　All　rights　are　reserved,　including　those　for　text　and　data　mining,　AI　training,　and　similar　technologies.