On-Device ECG and Vital-Sign Prediction of 24-Hour Mortality and Unplanned ICU Admission in Emergency Department Patients: A Retrospective Evaluation of MEDI-AI

Background/Objectives: Smartphone-based analysis of electrocardiograms (ECGs) and vital signs may support risk assessment without an integrated electronic health record feed. We evaluated MEDI-AI for 24 h mortality and unplanned intensive care unit (ICU) admission, with secondary diagnostic and triage assessments. Methods: This single-center retrospective evaluation included 2000 encounters from 2025 after supplementary development on 3000 encounters from 2024, with patient-level separation. Stored probabilities were assessed for area under the receiver-operating-characteristic curve (AUROC), calibration, and threshold performance. Post hoc exploratory NEWS2 and paired comparisons were performed; accepted inputs were also analyzed separately. Results: There were 189 deaths and 378 ICU admissions. Fusion AUROCs were 0.9994 and 0.9982; NEWS2 AUROCs were 0.8801 and 0.8131. Fusion-minus-vital-only AUROC differences were −0.000039 (95% CI, −0.000450 to 0.000366) and 0.002114 (0.000632 to 0.003920). Fusion calibration slopes were 5.62 and 6.09. STEMI and ACS sensitivities were 90.9% (58.7–99.8%) and 92.3% (74.9–99.1%). Triage critical-class recall was 24.9%. Input rejection occurred in 48 encounters; median recorded latency was 1235 ms. These stored-score estimates do not measure end-to-end deployable performance. Conclusions: Near-ceiling stored-score discrimination did not establish reliable absolute risks or a clinically usable workflow. Incomplete predictor-timing and model-development records limit reproducibility and assessment of bias. The evaluated triage configuration is unsuitable for clinical deployment, and the diagnostic analyses do not validate rule-out use. Independent external and prospective validation are required.