A new editorial argues that the majority of clinical machine-learning models never reach routine patient care, citing low external validation and limited real-world documentation. The piece frames adoption barriers as scientific and operational, not just technical. For developers, the implication is that performance on internal datasets and static benchmarks is insufficient; models must demonstrate reliability across sites, incorporate monitoring for drift and safety, and be supported by workflows that can be used by clinicians. The editorial’s emphasis on external validation aligns with broader calls for prospective evaluation, transparency in reporting, and clear governance before clinical deployment. For biotech and medtech stakeholders considering AI-enabled diagnostics or decision support, the message is direct: the translation gap remains substantial and requires evidence that goes beyond publication-level accuracy.