Beyond hype: Structural bias, regulatory lag, and market power complicate benchmarking of clinical AI chatbots
Original framing: “STAT+: Why benchmarking clinical LLMs from OpenEvidence, Doximity is complicated” — STAT News
Scientific rigor demands transparent datasets, reproducible evaluation pipelines, and external validation across diverse patient cohorts. Current benchmarks often rely on proprietary data, limiting peer scrutiny and inflating performance claims. Robust statistical methods, such as stratified sampling and bias audits, are essential to produce trustworthy metrics.
Benchmarking clinical AI is not merely a technical hurdle but a nexus of data inequity, regulatory fragmentation, and corporate power that reproduces historic patterns of medical marginalisation.
By foregrounding Indigenous data sovereignty, historical lessons from past health technologies, and cross‑cultural validation, we can redesign benchmarks to serve public health rather than profit. Coordinated action—through a global consortium, community‑driven trials, algorithmic impact assessments, and sovereign data platforms—offers a pathway to trustworthy, equitable AI that respects diverse epistemologies and anticipates future challenges.