Generative Simulation Benchmarking for precision oncology clinical workflows during mission-critical recovery windows

说明与用途

生成式仿真使得在精准肿瘤学中评估工作流恢复成为可能，关注的不仅是模型准确性，还包括恢复期间的吞吐量和公平性。

问题

Clinical decision‑support models for precision oncology can be unavailable for hours, but existing benchmarks focus only on model accuracy, ignoring how the surrounding workflow recovers during the critical catch‑up period.

结果

The simulation revealed that naive schedulers prioritize low‑urgency, low‑complexity cases, causing critical cases to backlog during recovery. The benchmark framework allows teams to evaluate and improve workflow resilience before real outages occur.

工作流

• Generate realistic oncology cases with structured variant priors and urgency levels
• Inject faults such as outages, latency spikes, partial data loss, and model rollbacks
• Run the full pipeline and measure recovery metrics: time to steady state and fairness across urgency tiers

证据

We were studying precision oncology pipelines — the kind that ingest genomic variants, match them to targeted therapies, and surface recommendations to tumor boards.

来源

Instead of waiting for real outages to test workflows, we can generate plausible clinical scenarios, inject controlled failures, and measure how the workflow recovers.

来源