Artificial Intelligence–Driven Accelerated Diagnostic Systems for Rapid X-Ray Interpretation, Automated Blood Analysis, and Ultra-Fast Laboratory Processing

This article proposed AI-ADS, an AI-Driven Accelerated Diagnostic System that integrates automated blood analysis, rapid X-ray interpretation, and rapid laboratory orchestration into a single latency-aware architecture. A physiology-structured blood analyzer, a reliability-gated fusion module with uncertainty estimation and budgeted early departures, and a dual-path radiograph encoder with semantic and frequency cues are all part of the proposed solution. In order to translate the speed of diagnostics into actual clinical throughput, AI-ADS employs a digital-twin controller to further model laboratory operations, and constraint-aware scheduling to reduce turnaround-time tails while prioritizing uncertain or critical cases. With an AUROC (macro) of 0.962, an AUPRC (macro) of 0.931, and F1 = 92.2% (Table 1; Fig. 1), the experimental results demonstrate that multimodal fusion improves diagnostic quality in comparison to single-stream baselines. Probability trustworthiness is enhanced by calibration and uncertainty gating, as shown in Table 2 and Figure 2, where ECE = 0.021, Brier = 0.074, and error@high-confidence = 1.9%. Table 3 and Figure 3 show that AI-ADS offers real-time inference with a throughput of 18.4 cases/sec, an early-exit rate of 71%, and a P50/P95 end-to-end latency of 50/120 ms. Table 4 and Figure 4 show that the lab's critical-test compliance increased from 81.2% to 93.5% after using the digital-twin CRL scheduler, and that the median TAT decreased from 49 to 34 minutes. The 90th percentile TAT also decreased from 92 to 62 minutes. Clinical timeliness, diagnostic reliability, and accuracy can all be improved with AI-ADS, as shown here.